Select a part of the brain. Explain its functions and how it impacts learning!
The Brain-SELECT ONE PART AND EXPLAIN
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Hindbrain—primitive core, 1st to form, top of spinal cord, regulates basic somatic activities like breathing
- Brain stem-top of spinal cord-2 parts
i. Medulla oblongata-bump in spinal cord, controls breathing, heart rate, BP, digestion; damage is usually fatal
ii. Pons-connects the two halves of the cerebellum, regulates arousal
1. raphe nuclei—system of nerves through the pons, uses serotonin, believed to trigger and maintain slow wave sleep
- Cerebellum—maintains balance, coordinates movements, and controls posture. Damage can cause ataxia—slurred speech, tremors, and loss of balance.
Midbrain—old brain, next to form, involved with other aspects of movement and sleep
- Reticular formation—system of nerves; from spinal cord through hindbrain and into midbrain. Involved with sleep, maintaining a waking state, arousal and attention. Also plays a part in the sensation of touch.
- Substantia nigra—midbrain into forebrain—system of nerves; regulates many aspects of movement such as initiation, termination, smoothness, and directedness. Parkinson’s—reduced dopamine, destroys substantia nigra
Forebrain—newest brain, last to form, involved with higher order thinking
- Subcortical Structures
i. Thalamus—“the relay station”—relays information from incoming sensory systems (except for olfactory information, which goes directly to the limbic system) to the appropriate areas of the cortex. Also involved with motor activity, language, and memory. Korsakoff Syndrome involves damage to neurons in the thalamus and mammillary bodies of the hypothalamus.
ii. Hypothalamus—controls ANS and Endocrine system in conjunction with the pituitary gland. Maintains homeostasis of fluids, temperature, metabolism, and appetite. Involved with motivated behaviors such as eating, drinking, sex, and aggression.
1. Suprachiasmatic Nucleus (SCN)—system of nerves located in the hypothalamus; involved with regulating circadian rhythms. Takes information from the eyes (retina), interprets it, and passes it on to the pineal gland which then secretes the hormone melatonin.
—system of nerves; includes the caudate nucleus, putamen, globus pallidus, and substantia nigra. Involved with planning, organizing, and coordinating voluntary movement. Disorders associated with the basal ganglia are: Huntington’s Disease, Parkinson’s Disease, and Tourette’s Syndrome. Also implicated in mania, obsessive-compulsive symptoms, and psychosis.
iv. Limbic System—several brain structures that work together to mediate the emotional component of behavior. Also involved with memory.
1. Amygdala—integrates and directs emotional behavior, attaches emotional significance to sensory information and mediates defensive and aggressive behavior
2. Septum—inhibits emotionality
3. Hippocampus—involved more with memory, particularly the transfer of memory from short-term to long-term memory
- Cerebral Cortex—makes up more than 80% of brain’s total weight and is responsible for higher cognitive, emotional, and motor functions. This is the outer, gray “squiggly” area, and it is divided into 4 lobes.
i. Frontal lobe—includes motor, premotor, and prefrontal areas. Receives information from other areas of the brain and then sends out commands to muscles to make voluntary movements. Involved with expressive language. Higher order skills, such as planning, organizing, and reasoning. Also, some concentration, attention, and orientation.
ii. Parietal—contains somatosensory cortex; involved with interpreting and making sense of touch, pain, and temperature
iii. Temporal—sound and smell, receptive language, memory and emotion
-lateral fissure—separates the temporal lobe from the frontal and part of the parietal lobes
iv. Occipital—receives visual impulses, involved in understanding visual information
Transfer of learning is discussed in depth in Chapter 6, so make sure to review this!
What are the types of transfer that can occur? Describe the transfer process as it relates to learning in a specific workplace of your choosing.
this week’s course materials and learning activities, and
on your learning so far this week.
to one or more of the following prompts in one to two paragraphs:
- Describe what you found interesting regarding this topic, and why.
- Describe how you will apply that learning in your daily life, including your work life.
- Describe what may be unclear to you, and what you would like to learn.
**Provide citation and reference to the material(s) you discuss.**
University of Phoenix Material – YOU MUST WATCH THIS VIDEO
“The Learning Machine
” ONLINE IN ORDER TO COMPLETE THIS ASSIGNMENT
Analysis of Factors in the Transfer Process
the “The Learning Machine” video available on the student website.
Select and complete one of the following assignments:
Option 1: Transfer of Learning Presen
specific detailed examples of learning theories (behaviorism, social cognitive, information processing and constructivism) in the video that demonstrate ways to apply transfer of learning concepts in a specific workplace of your choosing.
a 10-12 slide Microsoft® PowerPoint® presentation with speaker notes for your classmates on your ideas.
the following in your presentation:
· Relate the example to one or more of the explanations of transfer of learning included in one of the learning theories.
· Provide a description of how this example can be generalized to the workplace.
Option 2: Transfer of Learning Paper
specific detailed examples of learning theories (behaviorism, social cognitive, information processing and constructivism) in the video that demonstrate methods to apply transfer of learning concepts in a specific workplace of your choosing.
a 3- to 5-page essay on your ideas. Share this essay with your classmates by posting on the student website or providing paper copies.
the following in your essay:
· Relate the example to one or more of the explanations of transfer of learning included in one of the learning theories.
· Provide a description of how this example can be generalized to the workplace.
your paper consistent with APA guidelines.
PAR-1 Select a part of the brain. Explain its functions and how it impacts learning!The Brain-SELECT ONE PART AND EXPLAIN Brain—
Analysis of Factors in the Transfer Process PSYCH/635 Version 2 67 Chapter 2 Neuroscience of Learning The Tarrytown Unified School District was holding an all-day workshop for teachers and administrators on the topic of “Using Brain Research to Design Effective Instruction.” During the afternoon break a group of four participants were discussing the day’s session: Joe Michela, assistant principal at North Tarrytown Middle School; Claudia Orondez, principal of Templeton Elementary School; Emma Thomas, teacher at Tarrytown Central High School; and Bryan Young, teacher at South Tarrytown Middle School. Joe: So, what do you think of this so far? Bryan: It’s really confusing. I followed pretty well this morning the part about the functions of different areas of the brain, but I’m having a hard time connecting that with what I do as a teacher. Emma: Me, too. The presenters are saying things that contradict what I thought. I had heard that each student has a dominant side of the brain so we should design instruction to match those preferences, but these presenters say that isn’t true. Joe: Well they’re not exactly saying it isn’t true. What I understood was that different parts of the brain have different primary functions but that there’s a lot of crossover and that many parts of the brain have to work at once for learning to occur. Claudia: That’s what I heard too. But I agree with Bryan—it’s confusing to know what a teacher is to do. If we’re supposed to appeal to all parts of the brain, then isn’t that what teachers try to do now? For years we’ve been telling teachers to design instruction to accommodate different student learning styles—seeing, hearing, touching. Seems like brain research says the same thing. Joe: Especially seeing they said how important the visual sense is. I tell teachers not to lecture so much since that’s not an effective way to learn. Bryan: True, Joe. Another thing they said that threw me was how much teens’ brains are developing. I thought their wacky behavior was all about hormones. I see now that I need to be helping them more to make good decisions. Emma: I think this really is fascinating. This session has made me aware of how the brain receives and uses information. But it’s so complex! For me, the challenge is to match brain functioning with how I organize and present information and the activities I design for students. Claudia: I’ve got lots of questions to ask after this break. I know there’s much that researchers don’t know, but I’m ready to start working with my elementary teachers to use brain research to benefit our children. Many different learning theories and processes are discussed in subsequent chapters in this text. Behavior theories (Chapter 3) focus on external behaviors and consequences, whereas cognitive theories—the focus of this text—posit that learning occurs internally. Cognitive processes include thoughts, beliefs, and emotions, all of which have neural representations. This chapter addresses the neuroscience of learning, or the science of the relation of the nervous system to learning and behavior. Although neuroscience is not a learning theory, being familiar with neuroscience will give you a better foundation to understand the learning chapters that follow. The focus of this chapter is on the central nervous system (CNS), which comprises the brain and spinal cord. Most of the chapter covers brain rather than spinal cord functions. The autonomic nervous system(ANS), which regulates involuntary actions (e.g., respiration, secretions), is mentioned where relevant. The role of the brain in learning and behavior is not a new topic, but its significance among educators has increased in recent years. Although educators always have been concerned about the brain because the business of educators is learning and the brain is where learning occurs, much brain research has investigated brain dysfunctions. To some extent, this research is relevant to education because educators have students in their classes with handicaps. But because most students do not have brain dysfunctions, findings from brain research have not been viewed as highly applicable to typical learners. The advances in technology have made possible new methods that can show how the brain functions while people perform mental operations involving learning and memory. The data yielded by these new methods are highly relevant to classroom teaching and learning and suggest implications for learning, motivation, and development. Educators are interested in findings from neuroscience research as they seek ways to improve teaching and learning for all students (Byrnes, 2012). This interest is evident in the opening vignette. This chapter begins by reviewing the brain’s neural organization and major structures involved in learning, motivation, and development. The topics of localization and interconnections of brain structures are discussed, along with methods used to conduct brain research. The neurophysiology of learning is covered, which includes the neural organization of information processing, memory networks, and language learning. The important topic of brain development is discussed to include the influential factors on development, phases of development, critical periods of development, language development, and the role of technology. How motivation and emotions are represented in the brain is explained, and the chapter concludes with a discussion of the major implications of brain research for teaching and learning. Discussions of the CNS are necessarily complex, as Emma notes in the opening scenario. Many structures are involved, there is much technical terminology, and CNS operation is complicated. The material in this chapter is presented as clearly as possible, but a certain degree of technicality is needed to preserve the accuracy of information. Readers who seek more technical descriptions of CNS structures and functions as they relate to learning, motivation, self-regulation, and development are referred to other sources (Byrnes, 2001, 2012; Centre for Educational Research and Innovation, 2007; Heatherton, 2011; Jensen, 2005; National Research Council, 2000; Wang & Morris, 2010; Wolfe, 2010). When you finish studying this chapter, you should be able to do the following: ■ Describe the neural organization and functions of axons, dendrites, and glial cells. ■ Discuss the primary functions of the major areas of the brain. ■ Identify some brain functions that are highly localized in the right and left hemispheres. ■ Discuss the uses of different brain research technologies. ■ Explain how learning occurs from a neuroscience perspective to include the operation of consolidation and memory networks. ■ Discuss how neural connections are formed and interact during language acquisition and use. ■ Discuss the key changes and critical periods in brain development as a function of maturation and experience. ■ Explain the role of the brain in the regulation of motivation and emotions. ■ Discuss some instructional implications of brain research for teaching and learning. ORGANIZATION AND STRUCTURES The central nervous system (CNS) is composed of the brain and spinal cord and is the body’s central mechanism for control of voluntary behavior (e.g., thinking, acting). The autonomic nervous system (ANS) regulates involuntary activities, such as those involved in digestion, respiration, and blood circulation. These systems are not entirely independent. People can, for example, exert some control over their heart rates, which means that they are voluntarily controlling an involuntary activity. The spinal cord is about 18 inches long and the width of an index finger. It runs from the base of the brain down the middle of the back. It is essentially an extension of the brain. Its primary function is to carry signals to and from the brain, making it the central messenger between the brain and the rest of the body. Its ascending pathway carries signals from body locations to the brain, and its descending pathway carries messages from the brain to the appropriate body structure (e.g., to cause movement). The spinal cord also is involved in some reactions independently of the brain (e.g., knee-jerk reflex). Damage to the spinal cord, such as from an accident, can result in symptoms ranging from numbness to total paralysis (Jensen, 2005; Wolfe, 2010). Figure 2.1 Structure of neurons. Neural Organization The CNS is composed of billions of cells in the brain and spinal cord. There are two major types of cells: neurons and glial cells. A depiction of neural organization is shown in Figure 2.1. Neurons. The brain and spinal cord contain about 100 billion neurons that send and receive information across muscles and organs (Wolfe, 2010). Most of the body’s neurons are found in the CNS. Neurons are different from other body cells (e.g., skin, blood) in two important ways. For one, most body cells regularly regenerate. This continual renewal is desirable; for example, when we cut ourselves, new cells regenerate to replace those that were damaged. But neurons do not regenerate in the same fashion. Brain and spinal cord cells destroyed by a stroke, disease, or accident may be permanently lost. On a positive note, however, there is evidence that neurons can show some regeneration (Kempermann & Gage, 1999), although the extent to which this occurs and the process by which it occurs are not well understood. Neurons are also different from other body cells because they communicate with one another through electrical signals and chemical reactions. They thus are organized differently than other body cells. This organization is discussed later in this section. Glial Cells. The second type of cell in the CNS is the glial cell. Glial cells are far more numerous than neurons. They may be thought of as supporting cells since they support the work of the neurons. They do not transmit signals like neurons, but they assist in the process. Glial cells perform many functions. A key one is to ensure that neurons operate in a good environment. Glial cells help to remove chemicals that may interfere with neuron operation. Glial cells also remove dead brain cells. Another important function is that glial cells put down myelin, a sheathlike wrapping around axons that helps transmit brain signals (discussed in the next section). Glial cells also appear to play key functions in the development of the fetal brain (Wolfe, 2010). In short, glial cells work in concert with neurons to ensure effective functioning of the CNS. Synapses. Figure 2.1 shows neural organization with cell bodies, axons, and dendrites. Each neuron is composed of a cell body, thousands of short dendrites, and one axon. A dendrite is an elongated tissue that receives information from other cells. An axon is a long thread of tissue that sends messages to other cells. Myelin sheath surrounds the axon and facilitates the travel of signals. Each axon ends in a branching structure. The ends of these branching structures connect with the ends of dendrites. This connection is known as a synapse. The interconnected structure is the key to how neurons communicate, because messages are passed among neurons at the synapses. The process by which neurons communicate is complex. At the end of each axon are chemical neurotransmitters. They do not quite touch dendrites of another cell. The gap is called the synaptic gap.When electrical and chemical signals reach a high enough level, neurotransmitters are released into the gap. The neurotransmitters either will activate or inhibit a reaction in the contacted dendrite. Thus, the process begins as an electrical reaction in the neuron and axon, changes to a chemical reaction in the gap, and then reconverts to an electrical response in the dendrite. This process continues from neuron to neuron in lightning speed. As discussed later in this chapter, the role of the neurotransmitters in the synaptic gap is critical for learning. From a neuroscience perspective, learning is a change in the receptivity of cells brought about by neural connections formed, strengthened, and connected with others through use (Jensen, 2005; Wolfe, 2010). Brain Structures The human adult brain (cerebrum) weighs approximately three pounds and is about the size of a cantaloupe or large grapefruit (Tolson, 2006; Wolfe, 2010). Its outward texture has a series of folds and is wrinkly in appearance, resembling a cauliflower. Its composition is mostly water (78%), with the rest fat and protein. Its texture is generally soft. The major brain structures involved in learning are shown in Figure 2.2 (Byrnes, 2001; Jensen, 2005; Wolfe, 2010) and described below. Cerebral Cortex. Covering the brain is the cerebral cortex, which is a thin layer about the thickness of an orange peel (less than ¼ of an inch). The cerebral cortex is the wrinkled “gray matter” of the brain. The wrinkles allow the cerebral cortex to have more surface area, which allows for more neurons and neural connections. The cerebral cortex has two hemispheres (right and left), each of which has four lobes (occipital, parietal, temporal, and frontal). The cortex is the central area involved in learning, memory, and processing of sensory information. Figure 2.2 Major brain structures. Brain Stem and Reticular Formation. At the base of the brain is the brain stem. The brain stem handles ANS (involuntary) functions through its reticular formation, which is a network of neurons and fibers that regulates control of such basic bodily functions as breathing, heart rate, blood pressure, eyeball movement, salivation, and taste. The reticular formation also is involved in awareness levels (e.g., sleep, wakefulness). For example, when you go into a quiet, dark room, the reticular formation decreases brain activation and allows you to sleep. The reticular formation also helps to control sensory inputs. Although we constantly are bombarded by multiple stimuli, the reticular formation allows us to focus on relevant stimuli. This is critical for attention and perception (Chapter 5), which are key components of the human information processing system. Finally, the reticular formation produces many of the chemical messengers for the brain. Cerebellum. The cerebellum at the back of the brain regulates body balance, muscular control, movement, and body posture. Although these activities are largely under conscious control (and therefore the domain of the cortex), the cortex does not have all the equipment it needs to regulate them. It works in concert with the cerebellum to coordinate movements. The cerebellum is the key to motor skill acquisition. With practice, many motor skills (e.g., playing the piano, driving a car) become largely automatic. This automaticityoccurs because the cerebellum takes over much of the control, which allows the cortex to focus on activities requiring consciousness (e.g., thinking, problem solving). Thalamus and Hypothalamus. Above the brain stem are two walnut-sized structures—the thalamus and hypothalamus. The thalamus acts as a bridge by sending inputs from the sense organs (except for smell) to the cortex. The hypothalamus is part of the ANS. It controls bodily functions needed to maintain homeostasis, such as body temperature, sleep, water, and food. The hypothalamus also is responsible for increased heart rate and breathing when we become frightened or stressed. Amygdala. The amygdala is involved in the control of emotion and aggression. Incoming sensory inputs (except for smell, which travel straight to the cortex) go to the thalamus, which in turn relays the information to the appropriate area of the cortex and to the amygdala. The amygdala’s function is to assess the harmfulness of sensory inputs. If it recognizes a potentially harmful stimulus, it signals the hypothalamus, which creates the emotional changes noted above (e.g., increased heart rate and blood pressure). Hippocampus. The hippocampus is the brain structure responsible for memory of the immediate past. How long is the immediate past? As we will see in Chapters 5 and 6, there is no objective criterion for what constitutes immediate and long-term (permanent) memory. Apparently the hippocampus helps establish information in long-term memory (which resides in the cortex), but maintains its role in activating that information as needed. Thus, the hippocampus may be involved in currently active (working) memory. Once information is fully encoded in long-term memory, the hippocampus may relinquish its role. Corpus Callosum. Running along the brain (cerebrum) from front to back is a band of fibers known as the corpus callosum.It divides the cerebrum into two halves, or hemispheres, and connects them for neural processing. This is critical, because much mental processing occurs in more than one location in the brain and often involves both hemispheres. Occipital Lobe. The occipital lobes of the cerebrum are primarily concerned with processing visual information. The occipital lobe also is known as the visual cortex. Visual stimuli are first received by the thalamus, which then sends these signals to the occipital lobes. Many functions occur here that involve determining motion, color, depth, distance, and other visual features. Once these determinations have occurred, the visual stimuli are compared to what is stored in memory to determine recognition (perception). An object that matches a stored pattern is recognized. When there is no match, then a new stimulus is encoded in memory. The visual cortex must communicate with other brain systems to determine whether a visual stimulus matches a stored pattern (Gazzaniga, Ivry, & Mangun, 1998). The importance of visual processing in learning is highlighted in the opening vignette by Joe. People can readily control their visual perception by forcing themselves to attend to certain features of the environment and to ignore others. If we are searching for a friend in a crowd we can ignore a multitude of visual stimuli and focus only on those (e.g., facial features) that will help us determine whether our friend is present. Teachers apply this idea when they ask students to pay attention to visual displays and inform them of learning objectives at the start of the class. Parietal Lobe. The parietal lobes at the top of the brain in the cerebrum are responsible for the sense of touch, and they help determine body position and integrate visual information. The parietal lobes have anterior (front) and posterior (rear) sections. The anterior part receives information from the body regarding touch, temperature, body position, and sensations of pain and pressure (Wolfe, 2010). Each part of the body has certain areas in the anterior part that receive its information and make identification accurate. The posterior portion integrates tactile information to provide spatial body awareness, or knowing where the parts of your body are at all times. The parietal lobes also can increase or decrease attention to various body parts. For example, a pain in your leg will be received and identified by the parietal lobe, but if you are watching an enjoyable movie and are attending closely to that, you may not experience the pain in your leg. Temporal Lobe. The temporal lobes, located on the side of the cerebrum, are responsible for processing auditory information. When an auditory input is received—such as a voice or other sound—that information is processed and transmitted to auditory memory to determine recognition. That recognition then can lead to action. When a teacher tells students to put away their books and line up at the door, that auditory information is processed and recognized, and then leads to the appropriate action. Located where the occipital, parietal, and temporal lobes intersect in the cortex’s left hemisphere is Wernicke’s area, which allows us to comprehend speech and to use proper syntax when speaking. This area works closely with another area in the frontal lobe of the left hemisphere known as Broca’s area,which is necessary for speaking. Although these key language processing areas are situated in the left hemisphere (but Broca’s area is in the right hemisphere for some people, as explained later), many parts of the brain work together to comprehend and produce language. Language is discussed in greater depth later in this chapter. Frontal Lobe. The frontal lobes, which lie at the front of the cerebrum, make up the largest part of the cortex. Their central functions are to process information relating to memory, planning, decision making, goal setting, and creativity. The frontal lobes also contain the primary motor cortex that regulates muscular movements. It might be argued that the frontal lobes in the brain most clearly distinguish us from lower animals and even from our ancestors of generations past. The frontal lobes have evolved to assume ever more complex functions. They allow us to plan and make conscious decisions, solve problems, and converse with others. Further, these lobes allow us to be aware of our thinking and other mental processes, a form of metacognition (Chapter 7). Running from the top of the brain down toward the ears is a strip of cells known as the primary motor cortex. This area is the area that controls the body’s movements. If while dancing the “Hokey Pokey” you think “put your right foot in,” it is the motor cortex that directs you to put your right foot in. Each part of the body is mapped to a particular location in the motor cortex, so a signal from a certain part of the cortex leads to the proper movement being made. In front of the motor cortex is Broca’s area, which is the location governing the production of speech. This area is located in the left hemisphere for about 95% of people; for the other 5% (30% of left-handers) this area is in the right hemisphere (Wolfe, 2010). Not surprisingly, this area is linked to Wernicke’s area in the left temporal lobe with nerve fibers. Speech is formed in Wernicke’s area and then transferred to Broca’s area to be produced (Wolfe, 2010). The front part of the frontal lobe, or prefrontal cortex, is proportionately larger in humans than in other animals. It is here that the highest forms of mental activity occur (Ackerman, 1992). Chapter 5 discusses how cognitive information processing associations are made in the brain. The prefrontal cortex is critical for these associations, because information received from the senses is related to knowledge stored in memory. In short, the seat of learning appears to be in the prefrontal cortex. It also is the regulator of consciousness, allowing us to be aware of what we are thinking, feeling, and doing. As explained later, the prefrontal cortex seems to be involved in the regulation of emotions. Table 2.1 summarizes the key functions of each of the major brain areas (Byrnes, 2001; Centre for Educational Research and Innovation, 2007; Jensen, 2005; Wolfe, 2010). When reviewing this table, keep in mind that no part of the brain works independently. Rather, information (in the form of neural impulses) is rapidly transferred among areas of the brain. Although many brain functions are localized, different parts of the brain are involved in even simple tasks. It therefore makes little sense to label any brain function as residing in only one area, as brought out in the opening vignette by Emma. Localization and Interconnections We know much more about the brain’s operation today than ever before, but the functions of the left and right hemispheres have been debated for a long time. Around 400 B.C. Hippocrates spoke of the duality of the brain (Wolfe, 2010). In 1870 researchers electrically stimulated different parts of the brains of animals and soldiers with head injuries (Cowey, 1998). They found that stimulation of certain parts of the brain caused movements in different parts of the body. The idea that the brain has a major hemisphere was proposed as early as 1874 (Binney & Janson, 1990). In general, the left hemisphere governs the right visual field and side of the body and the right hemisphere regulates the left visual field and side of the body. However, the two hemispheres are joined by bundles of fibers, the largest of which is the corpus callosum. Gazzaniga, Bogen, and Sperry (1962) demonstrated that language is controlled largely by the left hemisphere. These researchers found that when the corpus callosum was severed, patients who held an object out of sight in their left hands claimed they were holding nothing. Apparently, without the visual stimulus and because the left hand communicates with the right hemisphere, when this hemisphere received the input, it could not produce a name (because language is localized in the left hemisphere) and, with a severed corpus callosum, the information could not be transferred to the left hemisphere. Table 2.1 Key functions of areas of the brain. Area Key Functions Cerebral cortex Processes sensory information; regulates various learning and memory functions Reticular formation Controls bodily functions (e.g., breathing and blood pressure), arousal, sleep–wakefulness Cerebellum Regulates body balance, posture, muscular control, movement, motor skill acquisition Thalamus Sends inputs from senses (except for smell) to cortex Hypothalamus Controls homeostatic body functions (e.g., temperature, sleep, water, and food); increases heart rate and breathing during stress Amygdala Controls emotions and aggression; assesses harmfulness of sensory inputs Hippocampus Holds memory of immediate past and working memory; establishes information in long-term memory Corpus callosum Connects right and left hemispheres Occipital lobe Processes visual information Parietal lobe Processes tactile information; determines body position; integrates visual information Temporal lobe Processes auditory information Frontal lobe Processes information for memory, planning, decision making, goal setting, creativity; regulates muscular movements (primary motor cortex) Broca’s area Controls production of speech Wernicke’s area Comprehends speech; regulates use of proper syntax when speaking Brain research also has identified other localized functions. Analytical thinking seems to be centered in the left hemisphere, whereas spatial, auditory, emotional, and artistic processing occurs in the right hemisphere (but the right hemisphere apparently processes negative emotions and the left hemisphere processes positive emotions; Ornstein, 1997). Music is processed better in the right hemisphere; directionality, in the right hemisphere; and facial recognition, the left hemisphere. The right hemisphere also plays a critical role in interpreting contexts (Wolfe, 2010). For example, assume that someone hears a piece of news and says, “That’s great!” This could mean the person thinks the news is wonderful or horrible. The context determines the correct meaning (e.g., whether the speaker is being sincere or sarcastic). Context can be gained from intonation, people’s facial expressions and gestures, and knowledge of other elements in the situation. It appears that the right hemisphere is the primary location for assembling contextual information so that a proper interpretation can be made. Because functions are localized in brain sections, it has been tempting to postulate that people who are highly verbal are dominated by their left hemisphere (left brained), whereas those who are more artistic and emotional are controlled by their right hemisphere (right brained). But this is a simplistic and misleading conclusion, as the educators in the opening scenario realize. Although hemispheres have localized functions, they are connected, and there is much passing of information (neural impulses) between them. Very little mental processing likely occurs only in one hemisphere (Ornstein, 1997). Further, we might ask which hemisphere governs individuals who are both highly verbal and emotional (e.g., impassioned speakers). The hemispheres work in concert; information is available to both of them at all times. Speech offers a good example. If you are having a conversation with a friend, it is your left hemisphere that allows you to produce speech but your right hemisphere that provides the context and helps you comprehend meaning. Neuroscientists do not agree about the extent of lateralization. Some argue that specific cognitive functions are localized in specific regions of the brain, whereas others believe that different regions have the ability to perform various tasks (Byrnes & Fox, 1998). This debate mirrors that in cognitive psychology (Chapters 5 and 6) between the traditional view that knowledge is locally coded and the parallel distributed processing view that knowledge is coded not in one location but rather across many memory networks (Bowers, 2009). There is research evidence to support both positions. Different parts of the brain have different functions, but functions are rarely, if ever, completely localized in one section of the brain. This is especially true for complex mental operations, which depend on several basic mental operations whose functions may be spread out in several areas. Neuroscience researchers have shown, for example, that creativity does not depend on any single mental process and is not localized in any one brain region (Dietrich & Kanso, 2010). Studies employing fMRI have demonstrated that neural representations of stimuli in the cortex often are widely distributed (Rissman & Wagner, 2012), thus lending support to the idea that neural networks are highly connected. “Nearly any task requires the participation of both hemispheres, but the hemispheres seem to process certain types of information more efficiently than others” (Byrnes & Fox, 1998, p. 310). The practice of teaching to different sides of the brain (right brain, left brain) is not supported by empirical research. Some applications of these points on interconnectedness and lateralization are given in Application 2.1. Brain Research Methods We know so much more today about the operation of the CNS than ever before, in part because of a convergence of interest in brain research among people in different fields. Historically, investigations of the brain were conducted primarily by researchers in medicine, the biological sciences, and psychology. Over the years, people in other fields have taken greater interest in brain research, believing that research findings would have implications for developments in their fields. Today we find educators, sociologists, social workers, counselors, government workers (especially those in the judicial system), and others interested in brain research. Funding for brain research also has increased, including by agencies that primarily fund non-brain–related research (e.g., education). APPLICATION 2.1 Teaching to Both Brain Hemispheres Brain research shows that much academic content is processed primarily in the left hemisphere, but that the right hemisphere processes context. A common educational complaint is that teaching is too focused on content with little attention to context. Focusing primarily on content produces student learning that may be unconnected to life events and largely meaningless. These points suggest that to make learning meaningful—and thereby involve both brain hemispheres and build more extensive neural connections—teachers should integrate content and context as much as possible. Ms. Stone, a third-grade teacher, is doing a unit on butterflies. Children study material in books and on the Internet that shows pictures of different butterflies. To help connect this learning with context, she uses other activities. A local museum has a butterfly area, where butterflies live in a controlled environment. She takes her class to visit this so they can see the world of butterflies. A display is part of this exhibit, showing the different phases of a butterfly’s life. These activities help children connect characteristics of butterflies with contextual factors involving their development and environment. Mr. Marshall, a high school history teacher, knows that studying historical events in isolation is not meaningful and can be boring. Over the years, many world leaders have sought global peace. When covering President Wilson’s work to establish the League of Nations with his U.S. history class, Mr. Marshall draws parallels to the United Nations and contemporary ways that governments try to eliminate aggression (e.g., nuclear disarmament) to put the League of Nations into a context. Through class discussions, he has students relate the goals, structures, and problems of the League of Nations to current events and discuss how the League of Nations set the precedent for the United Nations and for worldwide vigilance of aggression. Learning about psychological processes in isolation from real situations often leaves students wondering how the processes apply to people. When Dr. Brown covers Piagetian processes (e.g., egocentrism) in her undergraduate educational psychology course, she has students in their internships document behaviors displayed by children that are indicative of those processes. She does the same thing with other units in the course to ensure that the content learning is linked with contexts (i.e., psychological processes have behavioral manifestations). Another reason for our increased knowledge is that there have been tremendous advances in technology for conducting brain research. Many years ago, the only way to perform brain research was to conduct an autopsy. Although examining brains of deceased persons has yielded useful information, this type of research cannot determine how the brain functions and constructs knowledge. Research investigating live brain functioning is needed to develop understanding about how the brain changes during learning and uses learned information to produce actions. Table 2.2 Methods used in brain research. Method Description X-ray High-frequency electromagnetic waves used to determine abnormalities in solid structures (e.g., bones) Computerized Axial Tomography (CAT) Scan Enhanced images (three dimensions) used to detect body abnormalities (e.g., tumors) Electroencephalograph (EEG) Measures electrical patterns caused by movement of neurons; used to investigate various brain disorders (e.g., language and sleep) Positron Emission Tomography (PET) Scan Assesses gamma rays produced by mental activity; provides overall picture of brain activity but limited by slow speed and participants’ ingestion of radioactive material Magnetic Resonance Imaging (MRI) Radio waves cause brain to produce signals that are mapped; used to detect tumors, lesions, and other abnormalities Functional Magnetic Resonance Imaging (fMRI) Performance of mental tasks fires neurons, causes blood flow, and changes magnetic flow; comparison with image of brain at rest shows responsible regions Near-Infrared Optical Topography (NIR-OT) Noninvasive technique for investigating higher-order brain functions in which near-infrared light is radiated on and penetrates the scalp, then is reflected by the cortex and passed back through the scalp Techniques that have yielded useful information are discussed below and summarized in Table 2.2. These are ordered roughly from least to most sophisticated. X-Ray. An X-ray consists of high-frequency electromagnetic waves that can pass through nonmetallic objects where they are absorbed by body structures (Wolfe, 2010). The unabsorbed rays strike a photographic plate. Interpretation is based on light and dark areas (shades of gray). X-rays are two dimensional and are most useful for solid structures, such as determining whether you have broken a bone. They do not work particularly well in the brain because it is composed of soft tissue, although X-rays can determine damage to the skull (a bone structure). CAT Scan. The CAT (computerized or computed axial tomography) scan was developed in the early 1970s to increase the gradations in shades of gray produced by X-rays. CAT scans use X-ray technology but enhance the images from two to three dimensions. CAT scans are used to investigate tumors and other abnormalities, but, like X-rays, they do not provide detailed information about brain functioning. EEG. The EEG (electroencephalograph) is an imaging method that measures electrical patterns created by the movements of neurons (Wolfe, 2010). Electrodes placed on the scalp detect neural impulses passing through the skull. The EEG technology magnifies the signals and records them on a monitor or paper chart (brain waves). Frequency of brain waves (oscillations) increases during mental activity and decreases during sleep. EEGs have proven useful to image certain types of brain disorders (e.g., epilepsy, language), as well as to monitor sleep disorders (Wolfe, 2010). EEGs provide valuable temporal information through event-related potentials (see the section, Language Development), but they cannot detect the type of spatial information (i.e., where the activity occurs) that is needed to investigate learning in depth. EEGs have been used to assess cognitive load (Chapter 5), or the demands placed on students’ working memories while learning. Cognitive load is important; the goal is to reduce extraneous load not directly connected with learning so that learners can use their cognitive resources for learning. Newer wireless EEG technologies allow greater learner movements, reduce the size of the equipment, and can be applied to several learners at once (Antonenko, Paas, Grabner, & van Gog, 2010), thereby producing results more reflective of learners’ actual cognitive processes while learning. PET Scan. The PET (positron emission tomography) scan allows one to investigate brain activity while an individual performs tasks. The person is injected with a small dose of radioactive glucose, which the blood carries to the brain. While in the PET scanner the individual performs mental tasks. Those areas of the brain that become involved use more of the glucose and produce gamma rays, which are detected by the equipment. This leads to computerized color images (maps) being produced that show areas of activity. Although PET scans represent an advance in brain imaging technology, there is a limit to how many sessions one can do and how many images can be produced at one time because the procedure requires ingesting radioactive material. Also, producing the images is a relatively slow process, so the speed with which neural activity occurs cannot be fully captured. Although the PET scan gives a good idea of overall brain activity, it does not show the specific areas of activity in sufficient detail (Wolfe, 2010). MRI and fMRI. Magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) are brain imaging techniques that address problems with PET scans. In an MRI, a beam of radio waves is fired at the brain. The brain is mostly water, which contains hydrogen atoms. The radio waves make the hydrogen atoms produce radio signals, which are detected by sensors and mapped onto a computerized image. The level of detail is superior to that of a CAT scan, and MRIs are commonly used to detect tumors, lesions and other abnormalities (Wolfe, 2010). The fMRI works much like the MRI, except that as persons perform mental or behavioral tasks the parts of the brain responsible fire neurons, which cause more blood to flow to these regions. The blood flow changes the magnetic field so the signals become more intense. The fMRI scanner senses these changes and maps them onto a computerized image. This image can be compared to an image of the brain at rest to detect changes. The fMRI can capture brain activity as it occurs and where it occurs with second-to-second changes in blood flow (Pine, 2006); the fMRI can record four images per second (Wolfe, 2010). There is, however, some temporal disparity because blood flow changes can take several seconds to occur (Varma, McCandliss, & Schwartz, 2008). Compared with other methods, the fMRI has many advantages. It does not require ingesting a radioactive substance. It works quickly and can measure activity precisely. It can record an image of a brain in a few seconds, which is much faster than other methods. And the fMRI can be repeated without problems. Issues with brain technologies are that they must be used in artificial contexts (e.g., laboratories) with specialized equipment (e.g., CAT scan machines), which preclude their capturing learning in classrooms or other learning environments. These issues can be partially addressed by giving participants learning tasks during brain experiments or by subjecting them to the technology immediately after they have experienced different classroom contexts (Varma et al., 2008). NIR-OT. NIR-OT (near-infrared optical topography) is a newer noninvasive technique that has been used in brain research to investigate higher-level cognitive processing and learning. An optical fiber transmits a near-infrared light, which is radiated on the scalp. Some of that light penetrates to a depth of 30 mm. The cerebral cortex reflects the light and passes it back through the scalp, where it is detected by another optical fiber located near the point of penetration. NIR-OT measures concentrations of deoxygenated hemoglobin in the brain, which reflect brain activity (Centre for Educational Research and Innovation, 2007). NIR-OT has many advantages over other methods. It can be employed in natural learning settings such as classrooms, homes, and workplaces. Its use has no mobility restrictions; participants move around freely. The NIR-OT analytical device is a mobile semiconductor. It can be used over longer periods of time with no serious side effects. And because the technology can be employed with multiple learners simultaneously, it can record brain changes as a consequence of social interactions. The field of brain research is rapidly changing, and technologies are being developed and refined (e.g., wireless EEG, handheld NIR-OT integrated circuit). In the future, we can expect to see techniques of greater sophistication that will allow learners greater mobility in natural learning environments, which will further pinpoint brain processes while learning occurs. We now turn to the neurophysiology of learning, which addresses how the brain processes, integrates, and uses knowledge. NEUROPHYSIOLOGY OF LEARNING The section covering brain processing during learning uses as a frame of reference the information processing models discussed in Chapter 5. Brain processing during learning is complex (as the opening scenario shows), and what follows covers only the central elements. Readers who want detailed information about learning and memory from a neurophysiological perspective should consult other sources (Byrnes, 2001, 2012; Centre for Educational Research and Innovation, 2007; Jensen, 2005; Rose, 1998; Wolfe, 2010). Information Processing System As explained in Chapter 5, key elements of the information processing system are sensory registers, working memory (WM), and long-term memory (LTM). The sensory registers receive inputs and hold them for a fraction of a second, after which they are discarded or channeled to WM. Most sensory inputs are discarded, since at any given time we are bombarded with multiple sensory inputs. Earlier in this chapter we saw that all sensory inputs (except for smells) go directly to the thalamus, where at least some of them then are sent to the appropriate part of the cerebral cortex for processing (e.g., brain lobes that process the appropriate sensory information). But the inputs are not sent in the same form in which they were received; rather, they are sent as neural “perceptions” of those inputs. For example, an auditory stimulus received by the thalamus will be transformed into the neural equivalent of the perception of that stimulus. This perception also is responsible for matching information to what already is stored in memory, a process known as pattern recognition (see Chapter 5). Thus, if the visual stimulus is the classroom teacher, the perception sent to the cortex will match the stored representation of the teacher, and the stimulus will be recognized. Part of what makes perception meaningful is that the brain’s reticular activating system filters information to exclude trivial information and focus on important material (Wolfe, 2010). This process is adaptive because if we tried to attend to every input, we would never be able to focus on anything. There are several factors that influence this filtering. Perceived importance, such as teachers announcing that material is important (e.g., will be tested), is apt to command students’ attention. Novelty attracts attention; the brain tends to focus on inputs that are novel or different from what might be expected. Another factor is intensity; stimuli that are louder, brighter, or more pronounced get more attention. Movement also helps to focus attention. Although these attentional systems largely operate unconsciously, it is possible to use these ideas for helping to focus students’ attention in the classroom, such as by using bright and novel visual displays. Applications of these ideas to learning settings are given in Application 2.2. APPLICATION 2.2 Arousing and Maintaining Students’ Attention Cognitive neuroscience research shows that various environmental factors can arouse and maintain people’s attention. These factors include importance, novelty, intensity, and movement. As teachers plan instruction, they can determine ways to build these factors into their lessons and student activities. Importance Mrs. Peoples is teaching children to find main ideas in paragraphs. She wants children to focus on main ideas and not be distracted by interesting details. Children ask the question, “What is this story mostly about?” read the story, and ask the question again. They then pick out the sentence that best answers the question. Mrs. Peoples reviews the other sentences to show how they discuss details that may support the main idea but do not state it. A middle school teacher is covering a unit on the state’s history. There are many details in the text, and the teacher wants students to focus on key events and persons who helped create the history. Before covering each section, the teacher gives students a list of key terms that includes events and persons. Students have to write a short explanatory sentence for each term. Novelty A fifth-grade teacher contacted an entomology professor at the local university who is an expert on cockroaches. The teacher took her class to his laboratory. There the students saw all types of cockroaches. The professor had various pieces of equipment that allowed students to see the activities of cockroaches firsthand, for example, how fast they can run and what types of things they eat. A high school tennis coach obtained a ball machine that sends tennis balls out at various speeds and arcs, which players then attempt to return. Rather than have players practice repetitively returning the balls, the coach sets up each session as a match (player versus machine) without the serves. If a player can successfully return the ball sent out from the ball machine, then the player gets the point; if not, the machine earns the point. Scoring follows the standard format (love-15-30-40-game). Intensity Many elementary children have difficulty with regrouping in subtraction and incorrectly subtract the smaller from the larger number in each column. To help correct this error, Mr. Kincaid has students draw an arrow from the top number to the bottom number in each column before they subtract. If the number on top is smaller, students first draw an arrow from the top number in the adjacent column to the top number in the column being subtracted and then perform the appropriate regrouping. The use of arrows makes the order of operations more pronounced. Ms. Lammaker wants her students to memorize the Gettysburg Address and be able to recite it with emphasis in key places. She demonstrates the reading while being accompanied at a low volume by an instrumental version of “The Battle Hymn of the Republic.” When she comes to a key part (e.g., “of the people, by the people, for the people”), she uses body and hand language and raises her inflection to emphasize certain words. Movement Studying birds and animals in books can be boring and does not capture their typical activities. An elementary teacher uses Internet sources and interactive videos to show birds and animals in their natural habitats. Students can see what their typical activities are as they hunt for food and prey, take care of their young, and move from place to place. Dr. Tsauro, an elementary methods instructor, works with her interns on their movements while they are teaching and working with children. Dr. Tsauro has each of her students practice a lesson with other students. As they teach they are to move around and not simply stand or sit in one place at the front of the class. If they are using projected images, they are to move away from the screen. Then she teaches the students seat work monitoring, or how to move around the room effectively and check on students’ progress as they are engaged in tasks individually or in small groups. Brain research has helped to clarify attentional processes and differences seen in students with attention-deficit/hyperactivity disorder (ADHD). Attentional problems seen in these children include not paying close attention to details, difficulty in sustaining attention, and being easily distracted (Byrnes, 2012). MRI and fMRI studies have implicated certain brain areas including the prefrontal cortex, thalamus, and the area where the temporal, occipital, and parietal lobes join. Many of these same areas also have been implicated in WM deficits, which, not surprisingly, many children with ADHD have. ADHD children also often show problems with planning, strategic behavior, and self-regulation, which are affected by prefrontal cortex activity (Byrnes, 2012). In summary, sensory inputs are processed in the sensory memories portions of the brain, and those that are retained long enough are transferred to WM. WM seems to reside in multiple parts of the brain but primarily in the prefrontal cortex of the frontal lobe (Wolfe, 2010). As we will see in Chapter 5, information is lost from WM in a few seconds unless it is rehearsed or transferred to LTM. For information to be retained there must be a neural signal to do so; that is, the information is deemed important and needs to be used. The parts of the brain primarily involved in memory and information processing are the cortex and the medial temporal lobe (Wolfe, 2010). It appears that the brain processes and stores memories in the same structures that initially perceive and process information. At the same time, the particular parts of the brain involved in LTM vary depending on the type of information. In information processing theory, a distinction is made between declarative memory (facts, definitions, events) and procedural memory (procedures, strategies). Different parts of the brain are involved in using declarative and procedural information. With declarative information, the sensory registers in the cerebral cortex (e.g., visual, auditory) receive the input and transfer it to the hippocampus and the nearby medial temporal lobe. Inputs are registered in much the same format as they appear (e.g., as a visual or auditory stimulus). The hippocampus is not the ultimate storage site; it acts as a processor and conveyor of inputs. As we will see in the next section, inputs that occur more often make stronger neural connections. With multiple activations, the memories form neural networks that become strongly embedded in the frontal and temporal cortexes. LTM for declarative information, therefore, appears to reside in the frontal and temporal cortex. Much procedural information becomes automatized such that procedures can be accomplished with little or no conscious awareness (e.g., typing, riding a bicycle). Initial procedural learning involves the prefrontal cortex, the parietal lobe, and the cerebellum, which ensure that we consciously attend to the movements or steps and that these movements or steps are assembled correctly. With practice, these areas show less activity and other brain structures, such as the motor cortex, become more involved (Wolfe, 2010). Observational learning is covered in Chapter 4. Cognitive neuroscience supports the idea that much can be learned through observation (Bandura, 1986). Research shows that the cortical circuits involved in performing an action also respond when we observe someone else perform that action (van Gog, Paas, Marcus, Ayres, & Sweller, 2009). With nonmotor procedures (e.g., decoding words, simple addition), the visual cortex is heavily involved. Repetition actually can change the neural structure of the visual cortex. These changes allow us to recognize visual stimuli (e.g., words, numbers) quickly without consciously having to process their meanings. As a consequence, many of these cognitive tasks become routinized. Conscious processing of information (e.g., stopping to think about what the reading passage means) requires extended activity in other parts of the brain. But what if no meaning can be attached to an input? What if incoming information, although deemed important (such as by a teacher saying, “Pay attention”), cannot be linked with anything in memory? This situation necessitates creation of a new memory network, as discussed next. Memory Networks With repeated presentations of stimuli or information, neural networks can become strengthened such that the neural responses occur quickly. From a cognitive neuroscience perspective, learning involves forming and strengthening neural connections and networks (synaptic connections). This definition is quite similar to the definition of learning in current information processing theories (Chapter 5). Hebb’s Theory. The process by which these synaptic connections and networks are formed has been the study of scientific investigations for many years. Hebb (1949) formulated a neurophysiological theory of learning that highlights the role of two cortical structures: cell assemblies and phase sequences. A cell assembly is a structure that includes cells in the cortex and subcortical centers (Hilgard, 1956). Basically a cell assembly is a neural counterpart of a simple association and is formed through frequently repeated stimulations. When the particular stimulation occurs again, the cell assembly is aroused. Hebb believed that when the cell assembly was aroused, it would facilitate neural responses in other systems, as well as motor responses. Hebb only could speculate on how cell assemblies formed, because in his time the technology for examining brain processes was limited. Hebb felt that repeated stimulations led to the growth of synaptic knobs that increased the contact between axons and dendrites (Hilgard, 1956). With repeated stimulations, the cell assembly would be activated automatically, which facilitates neural processing. A phase sequence is a series of cell assemblies. Cell assemblies that are stimulated repeatedly form a pattern or sequence that imposes some organization on the process. For example, we are exposed to multiple visual stimuli when we look at the face of a friend. One can imagine multiple cell assemblies, each of which covers a particular aspect of the face (e.g., left corner of the left eye, bottom of the right ear). By repeatedly looking at the friend’s face, these multiple cell assemblies are simultaneously activated and become connected to form a coordinated phase sequence that orders the parts (e.g., so we do not transpose the bottom of the right ear onto the left corner of the left eye). The phase sequence allows the coordinated whole to be meaningfully and consciously perceived. Neural Connections. Hebb’s ideas, despite being formulated over 65 years ago, are remarkably consistent with contemporary views on how learning occurs and memories are formed. As we will see in the next section on development, we are born with a large number of neural (synaptic) connections. Our experiences then work on this system. Connections are selected or ignored, strengthened or lost, and can be added and altered through new experiences (National Research Council, 2000). It is noteworthy that the process of forming and strengthening synaptic connections (learning) changes the physical structure of the brain and alters its functional organization (National Research Council, 2000). Learning specific tasks produces localized changes in brain areas appropriate for the task, and these changes impose new organization on the brain. We tend to think that the brain determines learning, but in fact there is a reciprocal relationship because of the plasticity of the brain, or its capacity to change its structure and function as a result of experience (Begley, 2007; Centre for Educational Research and Innovation, 2007). Although brain research continues on this topic, available information indicates that memory is not formed completely at the time initial learning occurs. Rather, memory formation is a continuous process in which neural connections are stabilized over time (Wolfe, 2010). The process of stabilizing and strengthening neural (synaptic) connections is known as consolidation (Wang & Morris, 2010). The hippocampus appears to play a key role in consolidation, despite the fact that the hippocampus is not where memories are stored. What factors improve consolidation? As discussed in Chapter 5, organization, rehearsal, and elaboration serve to impose structure. Research shows that the brain, far from being a passive receiver and recorder of information, plays an active role in storing and retrieving information (National Research Council, 2000). In summary, it appears that stimuli or incoming information activates the appropriate brain portion and becomes encoded as synaptic connections. With repetition, these connections increase in number and become strengthened, which means they occur more automatically and communicate better with one another. Learning alters the specific regions of the brain involved in the tasks (National Research Council, 2000). Experiences are critical for learning, both with the environment (e.g., visual and auditory stimuli) and from one’s mental activities (e.g., thoughts). Given that the brain imposes some structure on incoming information, it is important that this structure help facilitate memory. We might say, then, that simple consolidation and memory are not sufficient to guarantee long-term learning. Rather, instruction should play a key role by helping to impose a desirable structure on the learning, a point noted by Emma and Claudia in the opening scenario. Some applications of these ideas and suggestions for assisting learners to consolidate memories are given in Application 2.3. Language Learning The interaction of multiple brain structures and synaptic connections is seen clearly in language learning and especially in reading. Although contemporary technologies allow researchers to investigate real-time brain functioning as individuals acquire and use language skills, much brain research on language acquisition and use has been conducted on persons who have suffered brain injury and experienced some degree of language loss. Such research is informative of what functions are affected by injury to particular brain areas, but this research does not address language acquisition and use in children’s developing brains. APPLICATION 2.3 Teaching for Consolidation Factors such as organization, rehearsal, and elaboration help the brain impose structure on learning and assist in the consolidation of neural connections in memory. Teachers can incorporate these ideas in various ways. Organization Ms. Standar’s students are studying the American Revolution. Rather than ask them to learn many dates, she creates a time line of key events and explains how each event led to subsequent events. Thus, she helps students chronologically organize the key events by relating them to events that they helped cause. In her high school statistics course, Ms. Conwell organizes information about normally distributed data using the normal curve. On the curve she labels the mean and the standard deviations above and below the mean. She also labels the percentages of the area under portions of the curve so students can relate the mean and standard deviations to the percentages of the distribution. Using this visual organizer is more meaningful to students than is written information explaining these points. Rehearsal Mr. Luongo’s elementary students will perform a Thanksgiving skit for parents. Students must learn their lines and their movements. He breaks the skit into subparts and works on one part each day, then gradually merges the parts into a longer sequence. Students thus get plenty of rehearsal, including several rehearsals of the entire skit. Mr. Gomez has his ninth-grade English students rehearse with their vocabulary words. For each word list, students write the word and the definition and then write a sentence using the word. Students also write short essays every week, in which they try to incorporate at least five vocabulary words they have studied this year. This rehearsal helps to build memory networks with word spellings, meanings, and usage. Elaboration Elaboration is the process of expanding information to make it meaningful. Elaboration can help to build memory networks and link them with other relevant ones. Mr. Jackson knows that students find precalculus difficult to link with other knowledge. Mr. Jackson surveys his students to determine their interests and what other courses they are taking. Then he relates precalculus concepts to these interests and courses. For example, for students taking physics he links principles of motion and gravity to conic sections (e.g., parabolas) and quadratic equations. Ms. Kay’s middle school students are engaged in applying critical thinking to issues of personal responsibility. Students read vignettes and then discuss them. Rather than letting them simply agree or disagree with the story character’s choices, she forces them to elaborate by addressing questions such as: How did this choice affect other people? What might have been the consequences if the character had made a different choice? What would you have done and why? Brain trauma studies have shown that the left side of the brain’s cerebral cortex is central to reading and that the posterior (back) cortical association areas of the left hemisphere are critical for understanding and using language and for normal reading (Vellutino & Denckla, 1996). Reading dysfunctions often are symptoms of left posterior cortical lesions. Autopsies of brains of adolescents and young adults with a history of reading difficulties have shown structural abnormalities in the left hemispheres. Reading dysfunctions also are sometimes associated with brain lesions in the anterior (front) lobes—the area that controls speech—although the evidence much more strongly associates it with posterior lobe abnormalities. Since these results come from studies of persons who knew how to read (to varying degrees) and then lost some or all of the ability, we can conclude that the primarily left-sided areas of the brain associated with language and speech are critical for the maintenance of reading. Keep in mind, however, that there is no one central area of the brain involved in reading. Rather, the various aspects of reading (e.g., letter and word identification, syntax, semantics) involve many localized and specialized brain structures and synaptic connections that must be coordinated to successfully read (Vellutino & Denckla, 1996). The section that follows examines how these interconnections seem to develop in normal readers and in those with reading problems. The idea is that coordinated reading requires the formation of neural assemblies, or collections of neural groups that have formed synaptic connections with one another (Byrnes, 2001). Neural assemblies seem conceptually akin to Hebb’s cell assemblies and phase sequences. Results from neuroscience research show that specific brain regions are associated with orthographic, phonological, semantic, and syntactic processing required for reading (Byrnes, 2001). Orthographic (e.g., letters, characters) processing depends heavily on the primary visual area. Phonological processing (e.g., phonemes, syllables) is associated with the superior (upper) temporal lobes. Semantic processing (e.g., meanings) is associated with Broca’s area in the frontal lobe and areas in the medial (middle) temporal lobe in the left hemisphere. Syntactic processing (e.g., sentence structure) also seems to occur in Broca’s area. Noted earlier were two key areas in the brain involved in language. Broca’s area plays a major role in the production of grammatically correct speech. Wernicke’s area (located in the left temporal lobe below the lateral fissure) is critical for proper word choice and elocution. Persons with deficiencies in Wernicke’s area may use an incorrect word but one close in meaning (e.g., say “knife” when “fork” was intended). Language and reading require the coordination of the various brain areas. Such coordination occurs through bundles of nerve fibers that connect the language areas to each other and to other parts of the cerebral cortex on both sides of the brain (Geschwind, 1998). The corpus callosum is the largest collection of such fibers, but there are others. Damage to or destruction of these fibers prevents the communication in the brain needed for proper language functioning, which can result in a language disorder. Brain researchers explore how dysfunctions operate and which brain functions continue in the presence of damage. This topic is considered further in the following section, because it is intimately linked with brain development. For educators, knowing how the brain develops is important because developmental changes must be considered in planning instruction to ensure student learning. BRAIN DEVELOPMENT To this point we have focused on mature CNS functioning. Many educators, however, work with preschoolers, children, and adolescents. The topic of brain development is of interest not only in its own right but also because the educational implications for teaching and learning vary depending on the level of brain development. In the opening scenario, Bryan notes the importance of educators understanding brain development. This section discusses influential factors on development, the course of development, sensitive periods in development, the role of development in language acquisition and use, and the influence of technology. Influential Factors Although human brains are structurally similar, there are differences among individuals. Five influences on brain development are genetics, environmental stimulation, nutrition, steroids, and teratogens (Byrnes, 2001; Table 2.3). These influences begin during fetal development (Paul, 2010). Genetics. The human brain differs in size and composition from those of other animals. Although the difference between the human genome and that of our closest animal relative (the chimpanzee) is only 1.23% (Lemonick & Dorfman, 2006), that difference and other genetic variations produce a species that can design and build bridges, compose music, write novels, solve complex equations, and so forth. Human brains have a similar genetic structure, but they nonetheless differ in size and structure. Studies of monozygotic (one-egg) twins show that they sometimes develop brains that are structurally different (Byrnes, 2001). Genetic instructions determine the size, structure, and neural connectivity of the brain. Most of the time these differences yield normally functioning brains, but brain research continues to identify how certain genetic differences produce abnormalities. Environmental Stimulation. Table 2.3 Factors affecting brain development. ■ Genetics ■ Environmental stimulation ■ Nutrition ■ Steroids ■ Teratogens Brain development requires stimulation from the environment. Prenatal development sets the stage for learning by developing a neural circuitry that can receive and process stimuli and experiences. Those experiences further shape the circuitry by adding and reorganizing synapses. For example, pregnant women who talk and sing to their babies may, through their speech and singing, help to establish neural connections in the babies (Wolfe, 2010). Brain development lags when experiences are missing or minimal. Although there are certain critical periods when stimulation can have profound effects (Jensen, 2005), research suggests that stimulation is important during the entire life span to ensure continued brain development. Nutrition. Lack of good nutrition can have major effects on brain development, and the particular effects depend on when the poor nutrition occurs (Byrnes, 2001). Prenatal malnutrition, for example, slows the production and growth of neurons and glial cells. A critical period is between the 4th and 7th months of gestation when most brain cells are produced (Jensen, 2005). Later malnutrition slows how quickly cells grow in size and acquire a myelin sheath. Although the latter problem can be corrected with proper diet, the former cannot because too few cells have developed. This is why pregnant women are advised to avoid drugs, alcohol, and tobacco; maintain a good diet; and avoid stress (stress also causes problems for a developing fetus). Steroids. Steroids refer to a class of hormones that affect several functions, including sexual development and stress reactions (Byrnes, 2001). Steroids can affect brain development in various ways. The brain has receptors for hormones. Such hormones as estrogen and cortisol will be absorbed and will potentially change brain structure during prenatal development. Excessive stress hormones can cause neurons to die. Researchers also have explored whether gender and sexual orientation differences arise in part due to differences in steroids. Although the evidence on the role of steroids in brain development is less conclusive than that for nutrition, steroids have the potential to affect the brain. Teratogens. Teratogens are foreign substances (e.g., alcohol, viruses) that can cause abnormalities in a developing embryo or fetus (Byrnes, 2001). A substance is considered to be a teratogen only if research shows that a not unrealistically high level can affect brain development. For example, caffeine in small amounts may not be a teratogen, but it may become one when intake is higher. Teratogens can have effects on the development and interconnections of neurons and glial cells. In extreme cases (e.g., the rubella virus), they can cause birth defects. Phases of Development During prenatal development, the brain grows in size and structure, as well as in number of neurons, glial cells, and neural connections (synapses). Prenatal brain development is rapid, because it occurs in 9 months and most cells are produced between months 4 and 7 (Jensen, 2005). Cells travel up the neural tube, migrate to various parts of the brain, and form connections. It is estimated that at its peak, the embryo generates a quarter of a million brain cells a minute. At birth the brain has over a million connections, which represent about 60% of the peak number of synapses that will develop over the lifetime (Jensen, 2005). Given these numbers, it is little wonder that prenatal development is so important. Changes that occur then can have far-reaching and permanent effects. Brain development also occurs rapidly in infants. By the age of 2 years, a child will have as many synapses as an adult, and by the age of 3 years the child will have billions more than an adult. Young children’s brains are dense and have many complex neural connections, more than at any other time in life (Trawick-Smith, 2003). In fact, young children have too many synapses. About 60% of babies’ energy is used by their brains; in comparison, adult brains require only 20–25% (Brunton, 2007). With development, children and adolescents lose far more brain synapses than they gain. By the time adolescents turn 18, they have lost about half of their infant synapses. Brain connections that are not used or needed simply disappear. This “use it or lose it” strategy is desirable because connections that are used will be reinforced and consolidated, whereas those not used will be permanently lost. By the age of 5 years, the child’s brain has acquired a language and developed sensory motor skills and other competencies. The rapid changes of the first years have slowed, but the brain continues to add synapses. Neural networks are becoming more complex in their linkages. This process continues throughout development. As noted by Bryan in the opening vignette, major changes occur during the teenage years when the brain undergoes structural alterations (Jensen, 2005). The frontal lobes, which handle abstract reasoning and problem solving, are maturing, and the parietal lobes increase in size. The prefrontal cortex, which controls judgments and impulses, matures slowly (Shute, 2009). There also are changes in neurotransmitters—especially dopamine—that can leave the brain more sensitive to the pleasurable effects of drugs and alcohol. There is a thickening of brain cells and massive reorganizations of synapses, which makes this a key time for learning. The “use it or lose it” strategy results in brain regions becoming strengthened through practice (e.g., practicing the piano thickens neurons in the brain region controlling the fingers; Wallis, 2004). Given these widespread changes in their brains, it is not surprising that teenagers often make poor decisions and engage in high-risk behaviors involving drugs, alcohol, and sex. Instructional strategies need to take these changes into account. Applications of these ideas to instruction are given in Application 2.4. Sensitive Periods Some books on child rearing stress critical periods (e.g., the first 2–3 years of life), such that if certain experiences do not occur then, the child’s development will suffer permanently. There is some truth to this statement, although the claim is overstated. It is more accurate to label them sensitive periods, which means that development proceeds well then but that further development can occur later. Five aspects of brain development for which there seem to be sensitive periods are language, emotions, sensory motor development, auditory development, and vision (Jensen, 2005; Table 2.4). Language and emotions are discussed elsewhere in this chapter; the remaining three are covered next. Sensory Motor Development. The systems associated with vision, hearing, and motor movements develop extensively through experiences during the first two years of life. The vestibular system in the inner ear influences the senses of movement and balance and affects other sensory systems. There is evidence that inadequate vestibular stimulation among infants and toddlers can lead to learning problems later (Jensen, 2005). APPLICATION 2.4 Teaching and Learning with Teenagers The rapid and extensive changes that occur in teenagers’ brains suggest that we not view teens as smaller versions of adults (or as young children either). Some suggestions for instruction with teens based on brain research follow. Give Simple and Straightforward Directions Mr. Glenn, who teaches 10th-grade English, knows that his students’ memories may not accommodate many ideas at once. For each novel students read, they must do a literary analysis that comprises several sections (e.g., plot summary, literary devices, analysis of a major character). Mr. Glenn reviews these sections carefully. For each, he explains what it should include and shows a sample or two. Use Models Students process information well when it is presented in multiple modes—visual, auditory, tactile. In her chemistry class, Ms. Carchina wants to ensure that students understand laboratory procedures. She explains and demonstrates each procedure she wants students to learn, then has students work in pairs to perform the procedure. As students work, she circulates among them and offers corrective feedback as needed. Ensure That Students Develop Competence Motivation theory and research show that students want to avoid appearing incompetent (Chapter 9). This is especially true during the teenage years when their senses of self are developing. Ms. Patterson teaches calculus, which is difficult for some students. Through quizzes, homework, and class work she knows which students are having difficulty. Ms. Patterson holds review sessions before school every day for her students, and she makes a point to advise students having difficulty to attend those sessions. Incorporate Decision Making The rapid development occurring in teens’ brains means that their decision making often is flawed. They may base decisions on incomplete information or what they think will please their friends and fail to think through potential consequences. Mr. Manley incorporates much decision making and discussions of consequences into his marine science classes. Students study topics such as global warming and water pollution, and he presents them with case studies that they discuss (e.g., a ship’s captain who wants to dump garbage at sea). He asks students questions that address topics such as the potential consequences of possible actions and other ways that the problem could be addressed. Often, however, infants and toddlers are not in stimulating environments, especially those who spend much time in day care centers that provide mostly caregiving. Many children also do not receive sufficient stimulation outside of those settings, because they spend too much time in car seats, walkers, or in front of televisions. Allowing youngsters movement and even rocking them provides stimulation. About 60% of infants and toddlers spend an average of one to two hours per day watching television or videos (Courage & Setliff, 2009). Although young children can learn from these media, they do not do so easily. Children’s comprehension and learning are enhanced when parents watch with them and provide descriptions and explanations (Courage & Setliff, 2009). Table 2.4 Aspects of brain development having sensitive periods. ■ Sensory motor ■ Auditory ■ Visual ■ Emotional ■ Language Auditory Development. The child’s first 2 years are ideal for auditory development. By the age of 6 months, infants can discriminate most sounds in their environments (Jensen, 2005). In the first 2 years, children’s auditory systems mature in terms of range of sounds heard and ability to discriminate among sounds. Problems in auditory development can lead to problems in learning language, because much language acquisition depends on children hearing the speech of others in their environments. Vision. Vision develops largely during the first year of life and especially after the fourth month. Synaptic density in the visual system increases dramatically, including the neural connections regulating the perception of color, depth, movement, and hue. Proper visual development requires a visually rich environment where infants can explore objects and movements. Television and movies are poor substitutes. Although they provide color and movement, they are two dimensional, and the developing brain needs depth. The action shown on television and in the movies often occurs too rapidly for infants to focus on properly (Jensen, 2005). In short, the first 2 years of life are important for proper development of the sensory motor, visual, and auditory systems, and development of these systems is aided when infants are in a rich environment that allows them to experience movements, sights, and sounds. At the same time, brain development is a lifelong process; brains need stimulation after the age of 2 years. The brain continually is adding, deleting, and reorganizing synaptic connections and changing structurally. Although researchers have shown that certain aspects of brain development occur more rapidly at certain times, individuals of all ages benefit from stimulating environments. Language Development Previously we saw how certain functions associated with language operate in the brain. Although researchers have explored brain processes with different types of content involving various mental abilities, a wealth of research has been conducted on language acquisition and use. This is a key aspect of cognitive development and one that has profound implications for learning. As noted earlier, much brain research on language has been conducted on persons who have suffered brain injury and experienced some degree of language loss. Such research is informative about what functions are affected by injury to particular brain areas, but these research investigations do not address language acquisition and use in children’s developing brains. Brain studies of developing children, while less common, have offered important insights into the development of language functions. Studies often have compared normally developing children with those who have difficulties learning in school. In place of the surgical techniques often used on brain-injured or deceased patients, these studies employ less-invasive techniques such as those described earlier in this chapter. Researchers often measure event-related potentials (or evoked potentials), which are changes in brain waves that occur when individuals anticipate or engage in various tasks (Halliday, 1998). Differences in event-related potentials reliably differentiate among below-average, average, and above-average children (Molfese et al., 2006). Children who are normally developing show extensive bilateral and anterior (front) cortical activation and accentuated left-sided activations in language and speech areas. In contrast to reading maintenance, it appears that reading development also depends on anterior activation, perhaps on both sides of the brain (Vellutino & Denckla, 1996). Other research shows that developing children who experience left-sided dysfunction apparently compensate to some extent by learning to read using the right hemisphere. The right hemisphere may be able to support and sustain an adequate level of reading, but it seems critical for this transition to occur prior to the development of language competence. Such assumption of language functions by the right hemisphere may not occur among individuals who have sustained left-hemisphere damage as adults. A sensitive period in language development seems to be between birth and age 5. During this time, children’s brains develop most of their language capabilities. There is a rapid increase in vocabulary between the ages of 19 and 31 months (Jensen, 2005). The development of these language capabilities is enhanced when children are in language-rich environments where parents and others talk with children. This sensitive period for language development overlaps the one for auditory development between birth and age 2. In addition to this period, language development also seems to be part of a natural process with a timetable. We have seen how the auditory and visual systems develop capacities to supply the input for the development of language. A parallel process may occur in language development for the capacity to perceive phonemes, which are the smallest units of speech sounds (e.g., the “b” and “p” sounds in “bet” and “pet”). Children learn or acquire phonemes when they are exposed to them in their environments; if phonemes are absent in their environments, then children do not acquire them. Thus, there may be a sensitive period in which synaptic connections are properly formed, but only if the environment provides the inputs. In short, children’s brains may be “ready” (“prewired”) to learn various aspects of language at different times in line with their levels of brain development (National Research Council, 2000). Importantly for education, instruction can help to facilitate language development. Different areas of the brain must work together to learn language, such as the areas involved in seeing, hearing, speaking, and thinking (Byrnes, 2001; National Research Council, 2000). Acquiring and using language is a coordinated activity. People listen to speech and read text, think about what was said or what they read, and compose sentences to write or speak. This coordinated activity implies that language development should benefit from instruction that coordinates these functions, that is, experiences that require vision, hearing, speech, and thinking (see Application 2.5). APPLICATION 2.5 Facilitating Language Development Although birth to age 5 represents a sensitive period for the development of language, its acquisition and use are lifelong activities. Teachers can help develop the language skills of students of all ages. Instruction ideally should coordinate the component functions of seeing, hearing, thinking, and speaking. A kindergarten teacher works with her students on learning phonemes. To help develop recognition of phonemes in “__at” words (e.g., mat, hat, pat, cat, sat), she has each of these words printed on a slide. The phoneme is printed in red and the “at” appears in black. She gives students practice by showing a slide, asking them to say the word, and then asking individual students to use the word in a sentence. Mrs. O’Neal teaches her third graders animal names and spellings. She has a picture of each animal and its printed name on a slide, along with two to three interesting facts about the animal (e.g., where it lives, what it eats). She has children pronounce the animal’s name several times and spell it aloud, then write a short sentence using the word. This is especially helpful for animal names that are difficult to pronounce or spell (e.g., giraffe, hippopotamus). Ms. Kaiton, a middle school mathematics teacher, is working with her students on place value. Some students are having a lot of difficulty and cannot correctly order numbers from smallest to largest (e.g., .007, 7/100, seven-tenths, 7). Ms. Kaiton has three large magnetic number lines, each ranging from 0 to 1 and broken into units of tenths, hundredths, and thousandths. She asked students to put a magnetic bar on the appropriate number line (e.g., put the bar on the 7 of the hundredths line for 7/100). Then she broke students into small groups and gave them problems, and asked them to use number lines or pie charts to show where numbers fell so they could properly order them. Next she worked with them to convert all numbers to a common denominator (e.g., 7/10 = 70/100) and to place the markers on the same board (e.g., thousandths) so they could see the correct order. Students in Mr. Bushnell’s tenth-grade class learn about key documents in U.S. history (e.g., Declaration of Independence, Constitution, Bill of Rights). To appeal to multiple senses, he brought facsimile copies of these documents to class. Then he had students engage in role-playing where they read selections from the documents. Students were taught how to put emphasis at appropriate places while reading to make these passages especially distinctive. Many students in Dr. Hua’s child development course have difficulty comprehending and correctly using psychological terms (e.g., assimilation, satiation, zone of proximal development). He obtains videos that demonstrate these concepts (e.g., child being administered Piagetian tasks) and gives students case studies that exemplify concepts, which students discuss in class. For example, in a case study illustrating satiation a student is repeatedly praised by a teacher. Finally the student becomes satiated with praise and tells the teacher that she does not always have to tell him that he did so well. In summary, different areas of the brain participate in language development in normally developing children, although left-hemisphere contributions typically are more prominent than right-hemisphere ones. Over time, language functions are heavily subsumed by the left hemisphere. In particular, reading skill seems to require left-hemisphere control. But more research is needed before we fully understand the relationships between brain functions and developing language and reading competencies. Like other aspects of brain development, language acquisition reflects the interaction between heredity and environment discussed in Chapter 1. The cultural experiences of infants and children will determine to a large extent which brain synapses they retain. If the culture stresses motor functions, then these should be strengthened; whereas if the culture stresses cognitive processes, then these will ascend. If young children are exposed to a rich linguistic environment stressing oral and written language, then their language acquisition will develop more rapidly than will the language capabilities of children in impoverished environments. The implication for facilitating early brain development is to provide rich experiences for infants and young children, stressing perceptual, motor, and language functions. This is especially critical in the first years of life. These experiences should enhance the formation of synaptic connections and networks. There also is evidence that babies who have suffered in utero (e.g., from mothers’ drug or alcohol abuse), as well as those with developmental disabilities (e.g., retardation, autism), benefit from intervention in the first three years (Shore, 1997). Influence of Technology We have seen that the brain exhibits neuroplasticity, which means its neural connections are formed, strengthened, and weakened, based on experiences. In recent years the rapid growth of technology and its influx into everyday lives have created a new set of experiences that heretofore were not present. We might ask how technology affects brain development. Before addressing this question, we should consider how technology is used, especially by students. We live in an age of technological multitasking! There are desktop and laptop computers, phones, tablets, and other personal devices. It is not uncommon to use multiple devices simultaneously. A student may be using the Internet on a computer while e-mailing on a personal device and texting on a phone. The student likely rapidly shifts back and forth between these applications. For any single application, technology may present us with much information rapidly. Internet use, for example, is based on quick and often superficial reading and rapid following of links. Texting is limited to short messages such that one can send and receive several in a few minutes. Living in an online environment can promote cursory reading, hurried and distracted thinking, and superficial learning (Carr, 2011). It is possible to think deeply and take one’s time on the Internet, but its structure does not encourage it. The Internet delivers sensory and cognitive stimuli that tend to be repetitive, interactive, and intensive. Users repeat the same or similar actions (e.g., following links) at high speed and often in response to cues. Some cues require physical responses (e.g., type, rotate screen), but others provide a lot of visual and auditory input. These activities tend to be rewarded; clicking links or answering messages gives quick responses and new inputs. The rapid feedback that often brings rewards encourages continued use. As we will see in Chapter 5, our attention to stimuli is a limited resource. Heavy use of technology can bombard our capacity to attend and overload it. Stimuli attended to are transferred to working memory for processing. When multiple stimuli impinge, working memory can become overloaded due to the high cognitive load (Chapter 5). This situation means that most information is lost since it is not adequately processed or connected with information in long-term memory. As Carr (2011) notes, the Internet seizes our attention only to scatter it. The resulting learning can be minimal. Information not rehearsed is lost, and it is easy not to rehearse in an online environment. Further, the knowledge that is retained may not be well connected with itself or knowledge in long-term memory. From a neuroscience perspective, different cognitive activities show different patterns of brain activity. Small, Moody, Siddarth, and Bookheimer (2009) found differences in brain activity between book reading (which requires sustained attention and deep thought) and Internet use. Book reading led to activity in brain areas associated with language, memory, and visual processes. Web surfing, conversely, resulted in more brain activity in prefrontal areas associated with decision making and problem solving. Further, such brain “rewiring” can occur with only a few hours of online use (Small & Vorgan, 2008). These tasks work at cross purposes. Evaluating links and making navigational choices requires mental coordination and decision making, which distract the brain from interpreting text or other information and thereby impede comprehension and retention. Although one can read deeply online, it is not easily compatible with doing so without distractions. Deep reading requires deep thinking during which we eliminate distractions and quiet the problem-solving functions of the frontal lobes. When multiple devices are used at once, the distractions increase, and the learning that occurs is apt to be fragmented. There is, of course, nothing wrong with browsing and scanning. These are useful skills in many endeavors, including those outside of online environments. We often do not need to read or think deeply; rather, we are interested in getting the gist of information or browsing quickly to find the resources we desire. Neuroscience evidence shows benefits of Web browsing on the development of visual-spatial skills (Carr, 2011). As we work in a busy online environment, our neural circuits devoted to scanning, skimming, and multitasking are expanding and strengthening. But the downside is that if browsing and scanning become dominant modes—as opposed to operations we use less often—synapses devoted to thinking deeply and sustaining concentration may be weakening. From an evolutionary perspective, we might say that success in online environments promotes a survival of the busiest! Another point to keep in mind is that long-term memories require consolidation of events that have been attended to and processed in working memory. Consolidation takes time to form strong memories. When too much information impinges rapidly, it is not properly consolidated and linked with existing knowledge in long-term memory. To grow, strengthen, and maintain synapses requires that students devote some time away from the rapid pace of online environments and think about what they have been reading. Consolidation continues to occur after exposure to information stops. The use of technology is neither inherently good nor bad (Wolfe, 2010). An educational implication of neuroscience research is that to develop different cognitive brain functions requires students to engage in different activities. Scanning, problem solving, and decision making are useful skills, but so are reflective and meditative thinking and evaluating and interpreting information. Teachers can develop instructional activities that require different skills and ensure that students do not spend too much time engaged in Web surfing and not enough on assembling knowledge into a coherent whole. MOTIVATION AND EMOTIONS Researchers have investigated how brain processes link with many different cognitive functions. But they also have been concerned with the brain processes involved with noncognitive functions, such as motivation and emotions. These functions are discussed in turn. Motivation In Chapter 9, motivation is defined as the process whereby goal-directed activities are instigated and sustained. Motivated actions include choosing to engage in tasks, expending physical and mental effort, persisting in the face of difficulties, and achieving well. Chapter 9 also discusses various processes that have been hypothesized to affect motivation, such as goals, self-efficacy, needs, values, and perceptions of control. Contemporary theories posit that motivational processes have cognitive components. Self-efficacy, for example, refers to perceived capabilities to learn or perform behaviors at designated levels. Self-efficacy is a cognitive belief. As such, it likely has a neural representation of the kind discussed in this chapter. Although research is lacking in this area, we might expect that self-efficacy beliefs are represented in the brain as a neural network that links the domain being studied (e.g., fractions, reading novels) with current sensory input. Other motivational processes also may be represented in synaptic networks, as might processes involved in self-regulated learning (Chapter 10). More neurophysiological research on motivation and self-regulation variables would help to bridge the gap between education and neuroscience. From a cognitive neuroscience perspective, there are at least two kinds of neural counterparts of motivation. These involve rewards and motivational states. Rewards. Rewards have a long history in motivation research. They are key components of behavior theories (Chapter 3), which contend that behaviors that are reinforced (rewarded) tend to be repeated in the future. Motivation represents an increase in the rate, intensity, or duration of behavior. Cognitive and constructivist theories of motivation postulate that it is the expectation of reward, rather than the reward itself, that motivates behavior. Rewards can sustain motivation when they are given contingent on competent performance or progress in learning. Motivation may decline over time when people view the rewards as controlling their behavior (i.e., they are performing a task so that they can earn a reward). Further, new learning can occur rapidly when events run contrary to expectancies. Previous neural connections become disrupted and new ones form to reflect the new contingencies between responses and outcomes (Tucker & Luu, 2007). The brain seems to have a system for processing rewards (Jensen, 2005), but, like other brain functions, this one is complex. Many brain structures are involved, including the hypothalamus, prefrontal cortex, and amygdala. The brain produces its own rewards in the form of opiates that result in a natural high. This effect suggests that the brain may be predisposed toward experiencing and sustaining pleasurable outcomes. The expectation that one may receive a reward for competent or improved performance can activate this pleasure network, which produces the neurotransmitter dopamine. It may be that the brain stores, as part of a neural network, the expectation of reward for performing the action. In fact, dopamine can be produced by the expectation of pleasure (anticipation of reward), as well as by the pleasure itself. Dopamine increases when there is a discrepancy between expected and realized rewards (e.g., one expects a large reward but receives a small one). The dopamine system can help people adjust their expectations, which is a type of learning (Varma et al., 2008). It should be noted that addictive substances (e.g., drugs, alcohol) also increase the amount of dopamine (Lemonick, 2007b), which raises feelings of pleasure. Addiction may occur when repetitive use of addictive substances disrupts the normal balance of synaptic connections that control rewards, cognition, and memory. The brain also can become satiated with rewards such that the expectation of a reward or the receipt of a reward does not produce as much pleasure as previously. It is possible that the expectation of a larger reward is needed to produce dopamine, and if that is not forthcoming, then the effect may extinguish. This point may help explain why a particular reward can lose its power to motivate over time. Research is needed on whether other cognitive motivators—such as goals and the perception of learning progress—also trigger dopamine responses and thus have neuro-physiological referents. Since dopamine production is idiosyncratic, the same level of reward or expectation of reward will not motivate all students uniformly. This point suggests that additional brain processes are involved in motivation, which has practical implications for teaching. Teachers who plan to use rewards must learn what motivates each student and establish a reward system that can accommodate changes in students’ preferences. Motivational States. Motivational states are complex neural connections that include emotions, cognitions, and behaviors (Jensen, 2005). States change with conditions. If it has been several hours since we have eaten, then we likely are in a hunger state. We may be in a worried state if problems are pressing on us. If things are going well, we may be in a happy state. Similarly, a motivational state may include emotions, cognitions, and behaviors geared toward learning. Like other states, a motivational state is an integrated combination of mind, body, and behavior that ultimately links with a web-like network of synaptic connections. States are fluid; they are ever changing based on internal (e.g., thoughts) and external (e.g., environmental) events. Any given motivational state can strengthen, weaken, or change to another type of state. This changing nature of synaptic connections matches the nature of motivation (discussed in Chapter 9); that is, motivation is a process rather than an outcome. As a process, it may not be steady but rather wax and wane. The key to learning is to maintain motivation within an optimal range. Teachers intuitively understand the idea of motivational states. Their goal is to have students in a motivational state for learning. At any given moment, some students will be in that state, but others will be experiencing different states, including apathy, sadness, hyperactivity, and distraction. To change these states, teachers may have to first address the present states (e.g., attend to why Kira is sad) and then attempt to focus students’ attention on the task at hand. The integration of cognition, emotion, and behavior posited by neuroscience is important. The individual components will not lead to desirable learning. For example, students who believe they want to learn and are emotionally ready to do so nonetheless will learn little if they engage in no behavior. Likewise, motivated behavior without a clear cognitive focus on learning will be wasted activity. Students who are experiencing emotional stress yet want to learn and engage in learning actions are apt to find their learning less than maximal because emotions are thwarting synaptic connections from being formed and consolidated. Emotions Similar to the neurophysiological evidence for motivation, the operation of emotions in the CNS is not fully understood. There are various theories to account for human emotions (Byrnes, 2001). One theory that is consistent with the preceding view of motivation is a network theory (Halgren & Marinkovic, 1995). In this view, emotional reactions consist of four overlapping stages: orienting complex, emotional event integration, response selection, and sustained emotional context. The orienting complex is an automatic response in which individuals direct their attention toward a stimulus or event and mobilize resources to deal with it. The orienting complex produces a neural response that is sent to other stages. In the emotional event integration stage, this stimulus or event is integrated with information in WM and LTM, such as information about the definition or meaning of the stimulus or event and the context. In the third (response selection) stage, the individual ascribes cognitive meaning to the stimulus or event, integrates this meaning with an affective component, identifies possible actions, and selects one. Finally, during the sustained emotional context stage, the individual’s mood is linked with outputs of prior stages. Each stage is linked with specific neural areas. For example, sustained emotional context seems to be associated with neural firings in areas of the frontal lobe (Halgren & Marinkovic, 1995). But emotions are more complex than this analysis, because the same event has the potential to arouse different emotions. The English language reflects this potential multiple triggering, as when one says after hearing a piece of news, “I didn’t know whether to laugh or cry.” Neuroscience research studies of emotion regulation show that the prefrontal cortex can regulate the amygdala (Heatherton, 2011). When the prefrontal cortex is regulating emotions, the amygdala shows decreased activity. It also is possible that emotional activity in the brain is different for primary and culturally based emotions (Byrnes, 2001). Primary emotions (e.g., fear, anger, surprise) may have an innate neural basis centered in the right hemisphere (which regulates much ANS functioning), whereas emotions that involve cultural meanings (e.g., statements made by people that can be interpreted in different ways) may be governed more by the left hemisphere with its language functions. Emotions can help to direct attention, which is necessary for learning (Phelps, 2006). Information from the environment goes to the thalamus, where it is relayed to the amygdala and to the frontal cortex. The amgydala determines the emotional significance of the stimulus (Wolfe, 2010). This determination is facilitative, because it tells us whether to run, seek shelter, attack, or stay calm. The frontal cortex provides the cognitive interpretation of the stimulus, but this takes additional time. Part of what is meant by “emotional control” is not to simply react to the emotional significance (although when safety is an issue, that is desirable), but rather to delay action until the proper cognitive interpretation can be made. In addition to their role in attention, emotions also influence learning and memory (Phelps, 2006). It appears that the hormones epinephrine and norepinephrine, which are secreted by the adrenal cortex to produce the autonomic responses involved in emotions, also enhance memory for the triggering stimulus or event in the temporal lobe of the brain (Wolfe, 2010). Conscious memory of emotional situations is consolidated better due to the actions of these hormones. The point that emotions can enhance learning should not be interpreted as a recommendation that educators should make learning as stressful as possible. As we saw earlier, too much stress interferes with the formation and consolidation of neural networks. But a certain amount of stress can facilitate memory and learning. In developing skills, learners may engage in blocked practice where they perform the same skills repeatedly (e.g., work several problems of the same type) or in interleaving where they perform different skills from task to task (e.g., work several problems where problem types keep changing). Because learners do not become bored, interleaving produces more stress, which can result in hormonal changes that strengthen synapses (Gregory, 2013). Motivation and emotions can be used constructively to produce better learning. Teachers who lecture a lot engender little emotional involvement by students. But emotional interest should rise when teachers get students involved in the learning. Activities such as role-playing, searching the Internet, discussions, and demonstrations are likely to instigate greater motivation and emotions and lead to better learning than will teacher lecturing (Application 2.6). Increasing emotion during learning is effective only up to a point. Too much emotion (e.g., high stress) for lengthy periods is not desirable because of all the negative side effects (e.g., increased blood pressure, compromised immune system). Students in prolonged stressful situations also worry excessively, and the thoughts associated with worry thwart learning. These negative effects brought on by stress or threats arise partly because of the hormone cortisol, which like epinephrine and norepinephrine is secreted by the adrenal glands (Lemonick, 2007a). Epinephrine and norepinephrine act quickly, and cortisol is a type of long-lasting backup. High amounts of cortisol in the body over long time periods can lead to deterioration of the hippocampus and a decline in cognitive functioning (Wolfe, 2010). Cortisol also is critical during brain development. Infants bond emotionally with parents or caregivers. When babies experience stress, their levels of cortisol become elevated in their bodies. Cortisol retards brain development because it reduces the number of synapses and leaves neurons vulnerable to damage (Trawick-Smith, 2003). In contrast, when babies form attachments and maintain them over time, cortisol levels do not become elevated (Gunnar, 1996). When attachments are secure, cortisol levels do not rise to dangerous levels even under stressful conditions. Thus, it is critical that young children believe that their parents or caregivers love them and are reliable caregivers. APPLICATION 2.6 Involving Emotions in Learning Mrs. Ortiz wants her elementary students to enjoy school, and she knows how important it is to arouse children’s emotions for learning. She tries to link academic content to students’ experiences so that their positive emotions associated with these experiences become associated with the learning. When her children read a story about a child who took a trip, she asks them to tell about when they took a trip to visit a relative, go on vacation, or so forth. When working on mathematical division, she asks children to think about something that was divided into parts (e.g., pie, cake) so that several people could enjoy it. Mr. LeTourneau wants his students to not only learn history but also experience the emotions involved in key events. Reading about events such as World War I and the Great Depression can devoid them of emotions, yet these and other events stirred strong emotions among those who lived then. He helps students express emotions they likely would have felt. For a role-playing on the Great Depression, one student was a person looking for work and others played the roles of employers he visited asking for work. As each employer turned him down, the job seeker became more frustrated and finally began sobbing and saying, “All I want is a job so I can provide for my family. I hope my children never see this again in their lives!” Dr. Smith-Burton understands that some students might view her elementary social studies methods course as dry and boring. To invoke her students’ emotions, each week she has her students focus on one or two concepts to address in their school internships. For example, reading about learning can be dull, but seeing a child learn is exciting. As her students work with schoolchildren, they keep a log on the children’s behaviors and reactions as they are learning. Her students report how excited they become when they are tutoring children and the children begin to show that they are learning. As one of her students reported, “I became so happy while working with Keenan when he said, ‘Oh, I get it,’ and sure enough he did!” In summary, we can see that motivation and emotions are integrally linked with cognitive processing and neural activities. Further, the evidence summarized in this section makes it clear that when motivation and emotions are properly regulated, they can positively affect attention, learning, and memory. We now turn to the instructional applications of neuroscience for teaching and learning. INSTRUCTIONAL APPLICATIONS Relevance of Brain Research There has been a surge of interest in the last several years in neurophysiological research exploring brain development and functioning. Many educators view brain research with interest, because they believe that it might suggest ways to make educational materials and instruction compatible with how children process information and learn. Unfortunately, the history of behavioral science reflects a disconnect between brain research and learning theories. Learning theorists in various traditions, while acknowledging the importance of brain research, have tended to formulate and test theories independent of brain research findings. This situation clearly is changing. Educational researchers increasingly believe that understanding brain processes provides additional insights into the nature of learning and development (Byrnes & Fox, 1998). Indeed, some cognitive explanations for learning (e.g., activation of knowledge in memory, transfer of information from WM to LTM; Chapter 5) involve CNS processes, and brain psychology has begun to explain operations involved in learning and memory. Findings from brain research actually support many results obtained in research studies on learning and memory (Byrnes, 2012; Centre for Educational Research and Innovation, 2007). It is unfortunate that some educators have overgeneralized results of brain research to make unwarranted instructional recommendations. Although brain functions are to some extent localized, there is much evidence that tasks require activity of both hemispheres and that their differences are more relative than absolute (Byrnes & Fox, 1998). The identification of “right-brained” and “left-brained” students usually is based on informal observations rather than on scientifically valid and reliable measures and instruments. The result is that some educational methods are being used with students not because of proven effects on learning, but rather because they presumably use students’ assumed brain preferences. Brain Myths The complexity of brain research means that most people have difficulty understanding it. That, coupled with a general fascination with the brain, has yielded myths about the brain. Some myths that have relevance to instruction and learning are summarized in this section (Centre for Educational Research and Innovation, 2007; Table 2.5). Table 2.5 Brain myths. ■ The most important learning occurs before the age of 3 years. ■ There are critical periods for learning. ■ We use only 10% of our brains. ■ Men and women have different brains. ■ You can learn while you sleep. ■ People are right brained or left brained. The Most Important Learning Occurs Before the Age of 3 Years. It is true that in the early years children’s brains are undergoing rapid increases (synaptogenesis) and consolidation (pruning) of synapses and that early stimulation can aid brain development and especially of language. But brain development never ceases. If it did, there would be no point in having our present educational system because it does not formally begin until age 5. What happens from birth to age 3 influences later development but does not completely determine it. There are Critical Periods for Learning. There are times when learning is easier, but it is not critical that it occurs then. For example, the capability to reproduce sounds of a language (phonology, accent) and integrate them with grammar is optimal during childhood (Centre for Educational Research and Innovation, 2007). After that, people still can learn a language and can learn vocabulary equally well as children. It is more accurate to say that there are sensitive periods for learning. Learning of different skills occurs across the life span. We Use Only 10% of Our Brains. In one sense that is true. Of our billions of brain cells, 10% are neurons; the remaining 90% are glial cells. Since neurons are involved in learning, we only use 10% of our brain cells for learning. But neuroscience research shows that 100% of the brain is always active and especially so compared with the rest of the body. Although the brain represents 2% of body weight, it consumes 20% of available energy (Centre for Educational Research and Innovation, 2007). Men and Women have Different Brains. There are some differences. Men’s brains are larger, and the language areas of the brain are more strongly activated among women (Centre for Educational Research and Innovation, 2007). At times cognitive terms are used that have no biological reality (e.g., a “male” brain better understands mechanics; a “female” brain communicates better). Neuroscience research does not show gender differences in developing neural networks during learning. Teachers are wise to treat all students as capable of learning. You Can Learn While you Sleep. This is every student’s dream! But there is no neuroscience evidence to support it. Some research has shown that sleep may help memory of things learned just prior to going to sleep (Gais & Born, 2004). The learning occurs before sleep; perhaps sleep helps consolidate the memory. People are Right Brained or Left Brained. This issue is discussed in this chapter. Although there is localization of functions to some degree, crossover is the rule rather than the exception. In short, we use all of our brains for learning. Educational Issues Brain research, and CNS research in general, raises many issues relevant to education (Table 2.6). With respect to developmental changes, one issue involves the critical role of early education. The fact that children’s brains are super-dense implies that more neurons are not necessarily better. There likely is an optimal state of functioning in which brains have the “right” number of neurons and synapses—neither too many nor too few. Physical, emotional, and cognitive development involves the brain approaching its optimal state. Atypical development—resulting in developmental disabilities—may occur because this paring-down process does not proceed normally. Table 2.6 Educational issues relevant to brain research. ■ Role of early education ■ Complexity of cognitive processes ■ Diagnosis of specific difficulties ■ Multifaceted nature of learning This molding and shaping process in the brain suggests that early childhood education is important. The developmental periods of infancy and preschool can set the stage for the acquisition of competencies needed to be successful in school (Byrnes & Fox, 1998). Early intervention programs (e.g., Head Start) have been shown to improve children’s school readiness and learning, and many states have implemented preschool education programs. Brain research justifies this emphasis on early education. A second issue is that instruction and learning experiences must be planned to take into account the complexities of cognitive processes such as attention and memory (Chapter 5). Neuroscience research has shown that attention is not a unitary process, but rather includes many components (e.g., alerting to a change in the current state, localizing the source of the change). Memory is similarly differentiated into types, such as declarative and procedural. The implication is that educators cannot assume that a particular instructional technique “gains students’ attention” or “helps them remember.” Rather, we must be more specific about what aspects of attention that instruction will appeal to and what specific type of memory is being addressed. A third issue involves remedying students’ learning difficulties. Brain research suggests that the key to correcting deficiencies in a specific subject is to determine with which aspects of the subject the learner is having difficulty and then specifically address those. Mathematics, for example, includes many subcomponents, such as comprehension of written numbers and symbols, retrieval of facts, and the ability to write numbers. Reading comprises orthographic, phonological, semantic, and syntactic processes. To say that one is a poor reader does not diagnose where the difficulty lies. Only fine-tuned assessments can make that identification, and then a corrective procedure can be implemented that will address the specific deficiency. A general reading program that addresses all aspects of reading (e.g., word identification, word meanings) is analogous to a general antibiotic given to one who is sick; it may not be the best therapy. It seems educationally advantageous to offer corrective instruction in those areas that require correction most. For example, cognitive strategy instruction in children’s weaknesses can be combined with traditional reading instruction (Katzir & Paré-Blagoev, 2006). The final issue concerns the complexity of learning theories. Brain researchers have shown that multifaceted theories of learning seem to capture the actual state of affairs better than do parsimonious models. There is much redundancy in brain functions, which accounts for the common finding that when an area of the brain known to be associated with a given function is traumatized, the function may not completely disappear (another reason why the “right-brain” and “left-brain” distinctions do not hold much credibility). Over time, theories of learning have become more complex. Classical and operant conditioning theories (Chapter 3) are much simpler than social cognitive theory, information processing theory, and constructivist theory (Chapters 4–8). These latter theories better reflect brain reality. This suggests that educators should accept the complexity of school learning environments and investigate ways that the many aspects of environments can be coordinated to improve student learning. Table 2.7 Educational practices substantiated by brain research. ■ Problem-based learning ■ Simulations and role-playing ■ Active discussions ■ Graphics ■ Positive climate Brain-Based Educational Practices This chapter suggests some specific educational practices that facilitate learning and that are substantiated by brain research. Byrnes (2001) contended that brain research is relevant to psychology and education to the extent that it helps psychologists and educators develop a clearer understanding of learning, development, and motivation; that is, it is relevant when it helps to substantiate existing predictions of learning theories. In other chapters of this text, theories and research findings are reviewed that suggest effective teaching and learning practices. Table 2.7 lists some educational practices that are derived from learning theories and supported by both learning research and brain research. In the opening vignette, we suspect that Emma and Claudia will be using these practices (among others). Application 2.7 gives examples of these applied in learning settings. APPLICATION 2.7 Effective Educational Practices There are many educational practices whose positive effects on learning are supported by both learning and brain research. Some important practices are problem-based learning, simulations and role-playing, active discussions, graphics, and positive climate. Problem-Based Learning Mr. Abernathy’s eighth graders have studied their state’s geography to include characteristics of the main regions and cities of the state. He divided the class into small groups to work on the following problem. A large computer company wants to open a manufacturing facility in the state. Each small student group is assigned a specific region in the state. The task for each group is to make a convincing argument for why the facility should be located in that region. Factors to be addressed include costs associated with locating in that area, accessibility to major highways and airports, availability of a labor force, quality of schools, nearness of higher education facilities, and support from the community. Students gather information from various sources (e.g., media center, Internet), prepare a poster with pictures and descriptions, and give a 10-minute presentation with slides supporting their position. Each member of a group has responsibility for one or more aspects of the project. Simulations and Role-Playing Mr. Barth’s fifth-grade students have read Freedom on the Menu by Carole Boston Weatherford. This book tells the story of the Greensboro, North Carolina, lunch counter sitins in the 1960s as seen through the eyes of a young African American girl. Mr. Barth discusses this book with the students and probes them for how they thought it felt to these individuals to be discriminated against. He then organizes class simulations and role-plays so that students can see how discrimination can operate. For one activity, he selected the girls to be the leaders and the boys to follow their directions. For another activity, he only called on boys with blue eyes, and for a third activity he moved all students with dark hair to the front of the room. Using these activities, he hoped that students would see and feel the unfairness of treating people differently based on characteristics that they cannot change. Active Discussions Ms. Carring’s civics class has been studying U.S. presidential elections. U.S. presidents are elected by electoral votes. There have been occasions where presidents elected by gaining the necessary electoral votes have not had a majority (50%) of the popular vote or have actually had a lower popular vote total than the losing candidate. Ms. Carring holds a class discussion on the topic, “Should U.S. presidents be elected by popular vote?” She facilitates the discussion by raising questions in response to points raised by students. For example, Candace argued that a popular vote better reflects the will of the people. Ms. Carring then asked whether, if we used only a popular vote, candidates would tend to focus on voters in large cities (e.g., New York, Chicago) and neglect voters in states with small populations (e.g., Montana, Vermont). Graphics Mr. Antonelli, a high school vocational instructor, has his students design a house, which they then will help to build with help from community members. The school system owns the land, a local contractor will pour the foundation, and a builder’s supply company will donate the lumber and electrical and plumbing supplies. The students use computer graphics to design different house styles and interior layouts. The class considers these and decides on an exterior and interior design plan. They then work with Mr. Antonelli and the builder’s supply company to determine what supplies and equipment they will need. Several community members volunteer to help students build the house, and after they finish it the house is given to a local family selected by a community organization. Positive Climate Ms. Taylor teaches second grade in a school serving a high-poverty neighborhood. Many of her students live in single-parent homes, and over 80% of the students receive lunch for free or at a reduced cost. Ms. Taylor does many things to create a positive climate. Her classroom (“Taylor’s Nest”) is warm and inviting and has cozy corners where students can go to read. Each day she talks with every student individually to learn what is happening in their lives. Ms. Taylor has a teacher’s aide and an intern from a local university in her class, so students get much individual attention. She has a private space (“Taylor’s Corner”) where she goes to talk privately with a student about any problems or stresses the student may be experiencing. She contacts the parents or guardians of her students to invite them to come to class and assist in any way that they can. Problem-Based Learning. Problem-based learning is an effective learning method (Chapter 8). Problem-based learning engages students in learning and helps to motivate them. When students work in groups, they also can improve their cooperative learning skills. Problem-based learning requires students to think creatively and bring their knowledge to bear in unique ways. It is especially useful for projects that have no one correct solution. The effectiveness of problem-based learning is substantiated by brain research. With its multiple connections, the human brain is wired to solve problems (Jensen, 2005). Students who collaborate to solve problems become aware of new ways that knowledge can be used and combined, which forms new synaptic connections. Further, problem-based learning is apt to appeal to students’ motivation and engender emotional involvement, which also can create more extensive neural networks. Simulations and Role-Playing. Simulations and role-playing have many of the same benefits as does problem-based learning. Simulations might occur via technology applications, in the regular class, or in special settings (e.g., museums). Role-playing is a form of modeling (Chapter 4) where students observe others. Both simulations and role-playing provide students with learning opportunities that are not ordinarily available. These methods have motivational benefits and command student attention. They allow students to engage with the material actively and invest themselves emotionally. Collectively, these benefits help foster learning. Active Discussions. Many topics lend themselves well to student discussions. Students who are part of a discussion are forced to participate; they cannot be passive observers. This increased level of cognitive and emotional engagement leads to better learning. Further, by participating in discussions, students are exposed to new ideas and integrate these with their current conceptions. This cognitive activity helps build synaptic connections and new ways of using information. Graphics. The human body is structured such that we take in more information visually than through all other senses (Wolfe, 2010). Visual displays help to foster attention, learning, and retention. The collective findings from learning and brain research support the benefits of graphics. Teachers who use graphics in their teaching and have students employ graphics (e.g., PowerPoint© presentations, demonstrations, drawings, concept maps, graphic organizers) capitalize on visual information processing and are apt to improve learning. Positive Climate. We saw in the section on emotions that learning proceeds better when students have a positive attitude and feel emotionally secure. Conversely, learning is not facilitated when students are stressful or anxious, such as when they fear volunteering answers because the teacher becomes angry if their answers are incorrect. In Chapter 9 and elsewhere in this text we discuss how students’ positive beliefs about themselves and their environments are critical for effective learning. Brain research substantiates the positive effect that emotional involvement can have on learning and the building of synaptic connections. Teachers who create a positive classroom climate will find that behavior problems are minimized and that students become more invested in learning. SUMMARY The neuroscience of learning is the science of the relation of the nervous system to learning and behavior. Although neuroscience research has been conducted for several years in medicine and the sciences, it recently has become of interest to educators because of the instructional implications of research findings. Neuroscience research addresses the central nervous system (CNS), which comprises the brain and spinal cord and regulates voluntary behavior, and the autonomic nervous system (ANS), which regulates involuntary actions. The CNS is composed of billions of cells in the brain and spinal cord. There are two major types of cells: neurons and glial cells. Neurons send and receive information across muscles and organs. Each neuron is composed of a cell body, thousands of short dendrites, and one axon. Dendrites receive information from other cells; axons send messages to cells. Myelin sheath surrounds axons and facilitates the travel of signals. Axons end in branching structures (synapses) that connect with the ends of dendrites. Chemical neurotransmitters at the ends of axons activate or inhibit reactions in the contracted dendrites. This process allows signals to be sent rapidly across neural and bodily structures. Glial cells support the work of neurons by removing unneeded chemicals and dead brain cells. Glial cells also establish the myelin sheath. The human adult brain (cerebrum) weighs about three pounds and is approximately the size of a cantaloupe. Its outer texture is wrinkled. Covering the brain is the cerebral cortex, a thin layer that is the wrinkled gray matter of the brain. The wrinkles allow the cortex to have more neurons and neural connections. The cortex has two hemispheres (left and right), each of which has four lobes (occipital, parietal, temporal, frontal). With some exceptions, the structure of the brain is roughly symmetrical. The cortex is the primary area involved in learning, memory, and processing of sensory information. Some other key areas of the brain are the brain stem, reticular formation, cerebellum, thalamus, hypothalamus, amygdala, hippocampus, corpus callosum, Broca’s area, and Wernicke’s area. The brain’s left hemisphere generally governs the right visual field, and vice versa. Many brain functions are localized to some extent. Analytical thinking seems to be centered in the left hemisphere, whereas spatial, auditory, emotional, and artistic processing occurs primarily in the right hemisphere. At the same time, many brain areas work together to process information and regulate actions. There is much crossover between the two hemispheres as they are joined by bundles of fibers, the largest of which is the corpus callosum. The working together of multiple brain areas is seen clearly in language acquisition and use. The left side of the brain’s cerebral cortex is central to reading. Specific brain regions are associated with orthographic, phonological, semantic, and syntactic processing required in reading. Wernicke’s area in the left hemisphere controls speech comprehension and use of proper syntax when speaking. Wernicke’s area works closely with Broca’s area in the left frontal lobe, which is necessary for speaking. However, the right hemisphere is critical for interpreting context and thus the meaning of much speech. Various technologies are used to conduct brain research. These include the X-ray, CAT scan, EEG, PET scan, MRI, fMRI, and NIR-OT. The field of brain research is changing rapidly, and new technologies of greater sophistication will continue to be developed. From a neuroscientific perspective, learning is the process of building and modifying neural (synaptic) connections and networks. Sensory inputs are processed in the sensory memories portions of the brain; those that are retained are transferred to WM, which seems to reside in multiple parts of the brain but primarily in the prefrontal cortex of the frontal lobe. Information then may be transferred to LTM. Different parts of the brain are involved in LTM depending on the type of information (e.g., declarative, procedural). With repeated presentations of stimuli or information, neural networks become strengthened such that the neural responses occur quickly. Because of its plasticity, the brain changes as a result of learning. The process of stabilizing and strengthening synaptic connections is known as consolidation, and through consolidation the physical structure and functional organization of the brain is changed. Table 2.8 Summary of learning issues. How Does Learning Occur? Learning involves the forming and strengthening of neural connections (synapses), a process known as consolidation. Repeated experiences help to strengthen connections and make neural firings and transmissions of information more rapid. Other factors that improve consolidation are organization, rehearsal, elaboration, and emotional involvement in learning. How Does Memory Function? Memory is not a unitary phenomenon. Instead, different areas of the brain are involved in working memory (WM) and long-term memory (LTM). Memory involves information being established so that neural connections are made and neural transmissions become automatic. What Is the Role of Motivation? The brain has a natural predisposition toward pleasurable outcomes and produces opiates to produce a natural high. This predisposition also seems to be triggered by the expectation of rewards. Motivational states are complex neural connections that include emotions, cognitions, and behaviors. How Does Transfer Occur? Transfer involves using information in new ways or in new situations. Transfer occurs when neural connections are formed between the learning and the new uses and situations. These connections are not made automatically. Students must learn them through experiences (e.g., teaching) or determine them on their own (e.g., through problem solving). How Does Self-Regulated Learning Operate? The processes involved in self-regulated learning (e.g., goals, assessment of goal progress, self-efficacy; Chapter 10) are cognitions that are represented in the same way that knowledge is represented; namely, by synaptic connections in the brain. Most of these self-regulatory activities likely reside in the brain’s frontal lobe. Neural connections formed between self-regulatory activities and the task students are engaged in allow learners to self-regulate their learning. What Are the Implications for Instruction? Brain research suggests that early childhood education is important and that instruction and remediation must be specified clearly so that interventions can be tailored to specific needs. Activities that engage learners (e.g., discussions, role playing) and command and hold their attention (e.g., graphical displays) are apt to produce better learning. Influential factors on brain development are genetics, environmental stimulation, nutrition, steroids, and teratogens. During prenatal development, the brain grows in size, structure, and number of neurons, glial cells, and synapses. The brain develops rapidly in infants; young children have complex neural connections. As children lose brain synapses, those they retain depend partly on the activities they engage in. There seem to be sensitive periods during the first few years of life for the development of language, emotions, sensory motor functions, auditory capabilities, and vision. Early brain development benefits from rich environmental experiences and emotional bonding with parents and caregivers. Major changes also occur in teenagers’ brains in size, structure, and number and organization of neurons. Two neural counterparts of motivation involve rewards and motivational states. The brain seems to have a system for processing rewards and produces its own rewards in the form of opiates that result in a natural high. The brain may be predisposed toward experiencing and sustaining pleasurable outcomes, and the pleasure network can be activated by the expectation of reward. Motivational states are complex neural connections that include emotions, cognitions, and behaviors. It is important to maintain motivation for learning within an optimal range. The operation of emotions in the CNS is complex. Emotional reactions consist of stages, such as orienting to the event, integrating the event, selecting a response, and sustaining the emotional context. Brain-linked emotional activity may differ for primary and culturally based emotions. Emotions can facilitate learning because they direct attention and influence learning and memory. Emotional involvement is desirable for learning; but when emotions become too strong, cognitive learning is impeded. Findings from brain research support many results obtained in cognitive research studies on learning and memory. But it is important not to overgeneralize brain research findings through such labeling of students as right or left brained. Most learning tasks require activity of both hemispheres, and the differences between brain functions are more relative than absolute. Brain research suggests that early education is important, instruction should take children’s cognitive complexities into account, assessment of specific problems is necessary to plan proper interventions, and complex theories of learning capture the brain’s operation better than do simpler theories. Some educational practices supported by brain research are problem-based learning, simulations and role-playing, active discussions, graphics, and a positive climate. A summary of learning issues appears in Table 2.8. ASCD. Chapter 11 Contextual Influences In an undergraduate teacher education course, Dr. Richards is having a discussion with her students on student boredom. Her students are serving internships in middle and high schools. Dr. Richards asks her students why they think so many middle and high school students seem bored in school. Tanya: I think they’ve just got their minds on other things. They’re interested in hanging out with friends, guys and girls. They’re not thinking about the learning. Rick: The classes are boring. So many teachers just stand up in front and lecture. Students hardly ever get to talk or move around. I don’t like classes like that. Jenna: Maybe some of the problems are from home. A lot of parents don’t stress education enough. How their kids do in school just isn’t very important to them. Alec: It also could be because of the friends they hang out with. If you hang out with a good crowd you’ll do better in school. But if no one in your crowd values school, then you won’t either. Isn’t that modeling? Stefano: And it’s not just the peers. It’s in the communities where the kids live. The school where I’m doing my internship is in a bad neighborhood. Most of the people there aren’t educated. So the kids don’t have good role models. Renee: And kids have different backgrounds. I heard a story on TV recently explaining cultural differences in attitudes toward schooling. What can teachers do about those? Dr. Richards: You’ve made good points. All of these are possible influences on students feeling bored in school. And it’s true that some influences are easier to change than others. As teachers, you can make your classrooms interesting or boring. But you also can exert some effects on parents, peers, communities, and cultural beliefs. We’re going to be discussing these. Then you can look for examples at your internship sites. Many learning principles are covered in this text. It is easy to think that these principles operate uniformly in different contexts and are relatively unaffected by contextual variables. But this is not the case. Principles of learning are not context independent. Rather, they operate in specific situations and are subject to contextual influences. Although contextual factors are addressed by all learning theories, some theories (e.g., constructivism; Chapter 8) place great emphasis on the role of context. Contextual perspectives on learning are informed by cross-cultural comparisons showing variability in the effects of variables on learning and development. But even within societies, there is considerable variation in development and learning patterns (Meece, 2002). Clearly societal practices can affect learning. Context has been defined in various ways. With reference to human development, Bronfenbrenner (1979) formulated a contextual model comprising a set of concentric circles with the individual at the common point of three intersecting circles: school, peers, and family. Outside of these is a larger circle containing neighborhood, extended family, community, church, workplace, and mass media. The outermost circle contains such influences as laws, cultural values, political and economic systems, and social customs. Changes in one level can affect others. For example, physical changes in children can alter their social groups, which in turn are affected by cultural values. This model highlights the complexity of human development, and by implication of the learning that takes place in students of different ages. In this text, context is defined as the community or learning environment within which the individual is located (Cole, 2010). The community includes the people who spend time together in some institutionalized setting, such as schools, classrooms, and work settings. Contemporary researchers investigate various types of communities such as communities of learners and communities of practice (Brown & Campione, 1996; Lave & Wenger, 1991). These researchers believe that learning cannot be studied in controlled situations because learning includes not only skill acquisition but also developing an identity as a member of a community (Lave, 1993). One’s identity can motivate and give meaning to the learning that occurs. This chapter addresses types of contextual influences on students’ learning in school. Many influences come from teachers, classrooms, and schools. But other contextual influences are located outside of school structures. In the last several years researchers increasingly have shown that parents, peers, communities, and cultures affect students’ learning, motivation, and self-regulation. Educators need to understand as much as possible about these contextual influences so they can use them productively to create effective learning environments for students within and outside of schools. This chapter begins by discussing important contextual influences on student learning found within schools: teachers, classrooms, and schools. Within-school variables include classroom organization and structure, teacher–student interactions, teacher expectations, teacher support, developmentally appropriate instruction, school transitions, and school climate. Next the key roles in learning that peers, families, communities, and cultures play are addressed. A good understanding of the topics covered in previous chapters will help readers integrate learning principles with these various influences to determine applications that promote student learning. When you finish studying this chapter, you should be able to do the following: ■ Discuss how organization, management, and the TARGET dimensions can influence the effectiveness of learning environments for teaching and learning. ■ Explain how aspects of teacher–student interactions, including teacher feedback, support, and expectations, may affect students’ academic motivation and learning. ■ Explain what is meant by developmentally appropriate instruction and why transitions in schooling can affect teaching and learning. ■ Describe how peer modeling and peer networks may influence students’ academic learning. ■ Discuss the relationship of socioeconomic status, home environment, parental involvement, and media influence to development and learning. ■ Explain how community location and involvement may relate to students’ learning and achievement beliefs. ■ Describe how differences among students between and within cultures may affect their beliefs, behaviors, and learning. ■ Explain some instructional implications of the literature on teacher–student interactions, learning styles, and parental and familial involvement in schooling. TEACHERS, CLASSROOMS, AND SCHOOLS A discussion of contextual influences on student learning should rightly begin with teachers, classrooms, and schools, because those are key factors in students’ lives. Several aspects of teacher, classroom, and school influences on learning are discussed in this section: effective learning environments, teacher–student interactions, developmentally appropriate instruction, transitions in schooling, and classroom and school climate. Effective Learning Environments Students’ learning benefits from effective learning environments, and creating these is a primary responsibility of teachers. Effective learning environments reflect good organization and management, as well as the TARGET dimensions of task, authority, recognition, grouping, evaluation, and time (Levin & Nolan, 2000; Meece, Anderman, & Anderman, 2006). These topics are addressed in this section. Organization. Organization refers to how activities are established, students are grouped, performances are evaluated, authority is established and maintained, and time is scheduled (Stipek, 1996). Good organization of the classroom learning environment facilitates learning. Many researchers and practitioners believe that environments are complex and that to understand learning we must take into account many factors (Marshall & Weinstein, 1984; Roeser, Urdan, & Stephens, 2009). An important aspect of organization is dimensionality (Rosenholtz & Simpson, 1984). Unidimensional classrooms include a few activities that address a limited range of student abilities. Multidimensional classrooms have more activities and allow for diversity in student abilities and performances. Multidimensional classes are compatible with constructivist tenets about learning (Chapter 8). Classroom characteristics that indicate dimensionality include differentiation of task structure, student autonomy, grouping patterns, and salience of formal performance evaluations (Table 11.1). Unidimensional classrooms have undifferentiated task structures. All students work on the same or similar tasks, and instruction employs a small number of materials and methods (Rosenholtz & Simpson, 1984). The more undifferentiated the structure, the more likely the daily activities will produce consistent performances from each student and the greater the probability that students will socially compare their work with that of others to determine relative standing. Structures become differentiated (and classrooms become multidimensional) when students work on different tasks at the same time. Table 11.1 Characteristics of dimensionality. Characteristic Unidimensional Multidimensional Differentiation of task structure Undifferentiated; students work on same tasks Differentiated; students work on different tasks Student autonomy Low; students have few choices High; students have choices Grouping patterns Whole class; students are grouped by ability Individual work; students are not grouped by ability Performance evaluations Students are graded on same assignments; grades are public; much social comparison Students are graded on different assignments; less public grading and social comparison Student autonomy refers to the extent to which students have choices about what to do and when and how to do it. Classrooms are unidimensional when autonomy is low, which can hinder self-regulation and stifle motivation. Multidimensional classrooms offer students more choices, which can enhance intrinsic motivation. With respect to grouping patterns, social comparisons become more prominent when students work on whole-class activities or are grouped by ability. Comparisons are not as prevalent when students work individually or in mixed-ability groups. Grouping affects motivation and learning and has added influence over the long term if groups remain intact and students understand they are bound to the groups regardless of how well they perform. Salience of formal performance evaluations refers to the public nature of grading. In unidimensional classrooms, students are graded on the same assignments and grades are public, so everyone knows the grade distribution. Those receiving low grades may not be motivated to improve. As grading becomes less public or as grades are assigned for different projects (as in multidimensional classes), grading can motivate a higher proportion of students, especially those who believe they are progressing and capable of further learning (Schunk, Meece, & Pintrich, 2014). Unidimensional classrooms have high visibility of performance (Rosenholtz & Rosenholtz, 1981), which can motivate high achievers to learn but often has a negative effect on everyone else. Multidimensional classrooms are more likely to motivate more students because they feature greater differentiation and autonomy, less ability grouping, and more flexibility in grading with less public evaluation. Management. Good organization helps create an effective environment, but good management also is needed for learning. Management refers to the ways that teachers create conditions such that students behave acceptably and learning can occur. Effective classroom managers ensure that rules and procedures are established, and they organize activities to keep students productively engaged. These activities help prevent discipline problems. When problems occur, good managers deal with them quickly and fairly so they are stopped and do not interfere with other students. Collectively these activities promote learning (Levin & Nolan, 2000). A distinction can be drawn between proactive and reactive activities. Proactive activities are those teacher actions designed to prevent discipline problems from occurring; reactive activities are teacher actions designed to deal with problems when they occur, quickly return misbehaving students to academic activities, and minimize disruptions to others. Both proactive and reactive features of management are necessary. Seminal research by Kounin (1977) found that what distinguished classrooms with few problems and where students were involved in academic work from classrooms with more problems and less work involvement were the proactive techniques that teachers used to prevent problems. Proactive activities include the teacher being aware of everything that is occurring in the classroom at any time, being able to attend to more than one issue at a time, keeping the pace of activities moving along well, keeping students task focused, minimizing boredom, and giving students few opportunities to misbehave. To this list we might add fostering students’ motivation for learning by enhancing their self-efficacy for learning (Chapter 4), positive outcome expectations (Chapter 4), perceived value of learning (Chapter 9), interest in learning (Chapter 9), and a positive classroom climate (discussed later). Kounin found that when effective teachers used reactive techniques, they did so clearly (i.e., named the misbehaving student, stated the unacceptable behavior) and firmly (i.e., an “I-mean-it” attitude with follow through until misbehavior stopped). Good classroom management also requires that teachers establish rules and procedures and convey their expectations to students. The beginning of the school year is a desirable time to establish rules and procedures so students know them early on. A procedure for establishing rules and procedures is as follows: describe and demonstrate desired behaviors to students, have students practice behaviors repeatedly, provide students with feedback on whether they performed behaviors correctly, and suggest improvements where needed (Emmer, Evertson, & Worsham, 2000; Evertson, Emmer, & Worsham, 2000). Research studies support the importance of teachers establishing high expectations for classroom behavior and conveying those expectations to students (Emmer et al., 2000; Evertson et al., 2000; Levin & Nolan, 2000). Effective classroom managers expect students to obey rules and do not tolerate excuses for disobeying them. Teachers’ proactive efforts to create a productive classroom are critical for good management. Rules, procedures, and expectations are proactive techniques designed to prevent problems and promote student learning. TARGET. In addition to good organization and management, effective learning environments incorporate other variables that can affect learning and motivation. These variables can be summarized by the acronym TARGET: task design, distribution of authority, recognition of students, grouping arrangements, evaluation practices, and time allocation (Epstein, 1989; Table 11.2). Table 11.2 TARGET variables affecting learning and motivation. Factor Characteristics Task Design of learning activities and assignments Authority Extent that students can assume leadership and develop independence and control over learning activities Recognition Formal and informal use of rewards, incentives, praise Grouping Individual, small group, large group Evaluation Methods for monitoring and assessing learning Time Appropriateness of workload, pace of instruction, time allotted for completing work The task dimension involves the design of learning activities and assignments. Chapter 9 discusses ways to structure tasks to promote a mastery (learning) goal orientation in students—for example, by making learning interesting, using variety and challenge, assisting students to set realistic goals, and helping students develop organizational, management, and other strategic skills (Ames, 1992a, 1992b). Task structure is a distinguishing feature of dimensionality. In unidimensional classes, students have the same materials and assignments, so variations in ability can translate into differences in learning and motivation. In multidimensional classes, all students may not work on the same task simultaneously and thus they have fewer opportunities for social comparisons. Authority refers to whether students can assume leadership and develop independence and control over learning activities. Teachers foster authority by allowing students to participate in decisions, giving them choices and leadership roles, and teaching them skills that allow them to take responsibility for learning. Self-efficacy tends to be higher in classes that allow students some measure of authority (Ames, 1992a, 1992b). Recognition, which involves the formal and informal use of rewards, incentives, and praise, has important consequences for motivated learning (Schunk, 1995). Ames (1992a, 1992b) recommended that teachers help students develop mastery (learning) goal orientations by recognizing progress, accomplishments, effort, and self-regulation; providing opportunities for all learners to earn rewards; and using private forms of recognition that avoid comparing students or emphasizing the difficulties of others. The grouping dimension focuses on students’ ability to work with others. Teachers who use heterogeneous cooperative groups and peer interaction where possible help ensure that differences in ability do not translate into differences in learning and motivation. Low achievers especially benefit from small-group work because contributing to the group’s success promotes self-efficacy. Group work also allows more students to share in the responsibility for learning so that a few students do not do all of the work. At the same time, individual work is important because it provides clear indicators of learning progress. APPLICATION 11.1 TARGET in the Classroom Incorporating TARGET components into a unit can positively affect learning and motivation. As Ms. Underhill develops a unit on deserts for her elementary students, she plans part of the unit but also involves her students in planning activities. She sets up learning centers, plans reading and research assignments, organizes large- and small-group discussions, and designs unit pre- and posttests as well as tasks for checking mastery throughout the unit. The class helps her plan a field trip to a museum with an area devoted to life in the desert, develop small-group project topics, and decide how to create a desert in the classroom. Ms. Underhill and the students then develop a calendar and timeline for working on and completing the unit. These examples incorporate the six TARGET variables of task, authority, recognition, grouping, evaluation, and time. Evaluation involves methods for monitoring and assessing student learning, for example, evaluating students for individual progress and mastery, giving students opportunities to improve their work (e.g., revise a paper for a better grade), using different forms of evaluation, and conducting evaluations privately. Although normative grading systems are common in schools (i.e., students compared to one another), such normative comparisons can lower self-efficacy among students who do not perform as well as their peers. Time involves the appropriateness of workload, pace of instruction, and time allotted for completing work (Epstein, 1989). Effective strategies for enhancing learning and motivation are to adjust time or task requirements for those having difficulty and allowing students to plan their schedules and time lines for making progress. Giving students control over their time management helps allay anxiety about completing work and can promote self-efficacy for learning and the use of self-regulatory processes (Schunk & Pajares, 2009; Chapter 10). Application 11.1 lists classroom applications of TARGET. Teacher–Student Interactions In typical classrooms, teachers and students are continually interacting with one another. For example, teachers give directions, ask questions, provide feedback, respond to students’ questions, correct misbehavior, and offer assistance as needed. How teachers interact with students is affected by teachers’ beliefs about their teaching capabilities and students’ learning capabilities (Davis, 2003). Teachers’ interactions with students can have important effects on students’ learning and motivation (Martin & Dowson, 2009; Wentzel, 2010). This section addresses three aspects influencing interactions: teacher feedback, support, and expectations. Teacher Feedback. Teachers provide different types of feedback to students. One type is performance feedback on the accuracy of their work (e.g., “That’s correct.”) and may include corrective information (e.g., “Try applying this formula.”). Performance feedback is informative because from it students learn about their progress in learning. Feedback indicating accuracy and use of good strategies (e.g., “That’s correct. You’re using the steps well.”) helps to build students’ self-efficacy and motivation, which can lead to further learning. When students make errors, reteaching and guiding students to correct answers are effective ways to promote learning (Rosenshine & Stevens, 1986). Such corrective feedback also can raise self-efficacy and motivation because it conveys that students are capable of performing better by using a better strategy. Teachers often give motivational feedback. One type is attributional, where teachers link students’ performances to one or more attributions (e.g., “That’s correct. You’re really working hard.”). In Chapter 9we saw that attributional feedback that students perceive as credible is an effective motivator. Another type of motivational feedback is using social comparison by providing vicarious information (e.g., “See how well Tanya is doing? You can do that well too.”). Pointing out the performances of similar others can raise self-efficacy and motivation in observers (Schunk & Pajares, 2009). A third type is persuasory feedback, such as, “I know you can learn this.” Such vicarious self-efficacy information can raise self-efficacy in students, but it is important that students subsequently experience success. Finally, teachers might provide feedback on the effectiveness of students’ strategies (e.g., “See how much better you’re doing now that you’re using the method we discussed?”). Effective strategies promote learning because they reflect sound learning principles designed to keep students task focused. Feedback indicating that students can perform better by using a different strategy can motivate them to do so. Teacher Support. Teacher support refers to the social, psychological, and emotional dimensions of teacher–student relationships. Teacher support affects the classroom climate; for example, teachers who are warm, learner-centered, and democratic create a positive atmosphere for learning. Teacher support is complex. Cornelius-White (2007) conducted a meta-analysis of research studies examining the relationship of teacher interaction variables (e.g., empathy, warmth, genuineness, encouraging learning, adapting to student differences) to student cognitive and affective outcomes. The affective set included student motivation, self-efficacy, satisfaction, participation, and social connection. The overall correlation was +.35, which is a moderate and positive relation and suggests that teachers who provide a more supportive environment have students who are motivated and engaged, thereby leading to better learning. Another critical dimension may be the extent to which the teacher directs the group’s activities. Effective teaching requires that teachers steer a middle ground between student autonomy and classroom structure (Davis, 2003). Teachers who provide strong affective and instructional support promote teacher–student relationships, student engagement in learning, and student achievement, including for students at risk for school failure (Hamre & Pianta, 2005; Sakiz, 2011). Students in learner-centered classrooms tend to display greater interest in learning and better learning compared with students in non-learner-centered classrooms (Daniels, Kalkman, & McCombs, 2001). In short, although sound instructional content and good pedagogy are necessary for learning, the relationships that teachers form and develop with their students add a key dimension. Teacher Expectations. Another aspect of teacher–student interactions that is relevant to student learning involves teacher expectations, which have been the subject of research for several years. Theory and research suggest that teachers’ expectations for students relate to teacher actions and student motivation and learning (Cooper & Good, 1983; Cooper & Tom, 1984; Dusek, 1985; Jussim, Robustelli, & Cain, 2009; Rosenthal, 2002). The impetus for exploring expectations came from a study by Rosenthal and Jacobson (1968), who gave elementary school students a test of nonverbal intelligence at the start of the academic year. Teachers were told that this test predicted which students would bloom intellectually during the year. The researchers actually randomly identified 20% of the school population as bloomers and gave these names to the teachers. Teachers were not aware of the deception: The test did not predict intellectual blooming and names bore no relation to test scores. Teachers taught in their usual fashion, and students were retested one semester, 1 year, and 2 years later. For the first two tests, students were in the classes of teachers given bloomers’ names; for the last test, students were in new classes with teachers who did not have these names. After the first year, significant differences in intelligence were seen between bloomers and control students (those not identified as bloomers); differences were greater among children in the first and second grades. During the subsequent year, these younger children lost their advantage, but bloomers in upper grades showed an increasing advantage over control students. Differences were greater among average achievers than among high or low achievers. Similar findings were obtained for grades in reading. Overall the differences between bloomers and control students were small, both in reading and on the intelligence test. Rosenthal and Jacobson (1968) concluded that teacher expectations can act as self-fulfilling propheciesbecause student achievement comes to reflect the expectations. They suggested that results are stronger with young children because they have close contact with teachers. Older students may function better after they move to a new teacher. This study is controversial: It has been criticized on conceptual and methodological grounds, and many attempts at replication have not been successful (Cooper & Good, 1983; Jussim et al., 2009). Nonetheless, teacher expectations exist and have been found to relate to various student outcomes. A model to explain self-fulfilling prophecies is as follows: ■ First, teachers develop erroneous expectations. ■ Then these expectations lead teachers to treat high-expectancy students differently than they treat low-expectancy students. ■ Eventually students may react to this differential treatment in such a manner as to confirm the originally erroneous expectation (Jussim et al., 2009). Early in the school year teachers form expectations based on initial interactions with students and information in records. Teachers then may begin to treat students differently consistent with these expectations. Teacher behaviors are reciprocated; for example, teachers who treat students warmly are apt to receive warmth in return. Student behaviors begin to complement and reinforce teacher behaviors and expectations. Effects will be most pronounced for rigid and inappropriate expectations. When they are appropriate or inappropriate but flexible, student behavior may substantiate or redefine expectations. When expectations are inappropriate or not easily changed, student performance might decline and become consistent with expectations. Once teachers form expectations, they can convey them to students through climate, verbal input, verbal output, and feedback (Rosenthal, 1974). The climate includes smiles, head nods, eye contact, and supportive and friendly actions. Teachers may create a warmer climate for students for whom they hold high expectations than for those for whom expectations are lower (Cooper & Tom, 1984). Verbal input, or opportunities to learn new material and difficulty of material, varies when high-expectation students have more opportunities to interact with and learn new material and be exposed to more difficult material. Verbal output refers to number and length of academic interactions. Teachers engage in more academic interchanges with high- than with low-expectation students (Brophy & Good, 1974). They also are more persistent with highs and get them to give answers by prompting or rephrasing questions. Feedback refers to use of praise and criticism. Teachers praise high-expectation students and criticize low-expectation students more (Cooper & Tom, 1984). Although these factors are genuine, wide differences exist between teachers (Schunk et al., 2014). Most teachers try to encourage lower achievers and treat them much like the patterns described above for high achievers (e.g., give more praise, get them to answer more questions). Appropriate teacher expectations for students can improve learning. Tailoring difficulty of material and level of questioning to students based on their prior performances is instructionally sound. Expecting all students to learn with requisite effort also is reasonable. Greatly distorted expectations are not credible and typically have little effect on learning. Most elementary teachers (when expectation effects may be strongest) hold positive expectations for students, provide for a lot of successes, and use praise often (Brophy & Good, 1974). It seems likely that students construct implicit theories about what their teachers think and expect of them. How these theories might influence their achievement actions is less predictable. Our beliefs about what others expect of us may motivate (“She thinks I can do it, so I’ll try”), demotivate (“She thinks I can’t do it, so I won’t try”), or lead us to act contrary to our theories (“She thinks I can’t do it, so I’ll show her I can”). The best advice is to expect that all students can learn and support them, which should help them construct appropriate expectations for themselves. Application 11.2 gives suggestions for conveying positive expectations to students. Developmentally Appropriate Instruction A critical contextual influence on student learning is developmentally appropriate instruction, or instruction that is matched (compatible) with learners’ developmental levels (Eccles & Midgley, 1989). That idea sounds basic, but unfortunately instructional activities and developmental levels often are mismatched. Teaching may involve nothing more than lecturing and presenting information to students (as noted in the opening vignette). The content might be presented in such a way that students have difficulty processing it, and they also might process it in ways that produce learning different from what the teacher desires. APPLICATION 11.2 Teacher Expectations Expectations that teachers hold for students can positively and negatively affect their interactions with students. The following practices may help promote positive effects: ■ Enforce rules fairly and consistently. ■ Assume that all students can learn, and convey that expectation to them. ■ Do not form differential student expectations based on qualities unrelated to performance (e.g., gender, ethnicity, parents’ background). ■ Do not accept excuses for poor performance. ■ Realize that upper limits of student ability are unknown and not relevant to school learning. A college English professor told her class that they would be expected to do a lot of writing throughout the semester. Some of the students looked apprehensive, and the professor assured them that it was a task they could do. “We can all work together to improve our writing. I know some of you have had different experiences in high school with writing, but I will work with each of you, and I know by the end of the semester you will be writing well.” One student waited after class, told the professor that he had been in a special-education class in school, and said, “I can hardly write a good sentence; I don’t think you can make a writer out of me.” To which the professor replied, “Well, sentences are a good place to begin. I’ll see you Wednesday morning in class.” For example, many high school students take precalculus. Much of the content of precalculus is abstract (e.g., conic sections, trigonometric relations, limits of functions). Although high school students increasingly are able to function at a Piagetian formal operational level and cognitively handle abstract content, many students are primarily concrete operational thinkers. Teachers who make little effort to provide concrete referents for precalculus topics create a mismatch between the content and students’ thinking. It is little wonder that so many students have difficulty with precalculus, which in turn can adversely affect their mathematics self-efficacy and motivation for further study of mathematics. Developmentally appropriate instruction relies upon several assumptions. For one, students construct knowledge based on their prior experiences and present schemas. Knowledge never is transmitted automatically; the construction of knowledge and integration with current mental structures are the means whereby learning proceeds. This requires that instruction be designed to foster such knowledge construction. Piaget (Chapter 8) recommended active exploration, a notion that is compatible with instructional methods such as discovery learning and small-group projects (which Rick in the opening vignette would like to see). For another, the social environment is important. This notion is seen clearly in Vygotsky’s theory (Chapter 8). When interacting with others, children receive ideas and opinions that conflict with their own; this sets the Piagetian equilibration process into motion (Meece, 2002). The cognitive conflict that ensues is considered the impetus behind learning in many developmental theories. Table 11.3 Developmentally appropriate instructional practices. ■ Teachers structure the learning environment to include adults, other children, materials, and opportunities for children to engage in active exploration and interaction. ■ Children select many of their own activities from a variety. ■ Children stay active as they engage in much self-regulated learning. ■ Children work most of the time in small groups or individually. ■ Children work with concrete, hands-on activities. ■ Teachers actively monitor children’s progress to ensure continued involvement. ■ Teachers focus on the process children use and do not insist always on one right answer. Third, conflict is created when the material to be learned is just beyond students’ present understandings. This creates the zone of proximal development (Chapter 8), within which learning can occur through cognitive conflict, reflection, and conceptual reorganization (Meece, 2002). Little conflict exists when material is too far advanced beyond current understandings; conflict similarly is minimized when learning is at children’s current levels. Finally, developmentally appropriate instruction incorporates active exploration and hands-on activities. Bruner’s theory (Chapter 8) recommends that enactive learning occurs first, followed by iconic and symbolic. Although children’s learning is based largely on what they do, hands-on learning is beneficial at all developmental levels. Students who are learning computer skills benefit from observing teachers demonstrate them (iconic) and explain them (symbolic), as well as by performing the skills themselves (enactive). What would a developmentally appropriate classroom look like? Meece (2002) suggested several appropriate practices that are summarized in Table 11.3. Some classroom applications of developmentally appropriate instruction are provided in Application 11.3. Transitions in Schooling Transitions in schooling can have a major impact on student learning. In the U.S. educational system, natural transitions occur when children change schools or experience major shifts in curricula and activities; for example, preschool to elementary, elementary to middle/junior high, middle/junior high to senior high, and senior high to college. Transitions are important because they can produce disruptions in routines and ways of thinking and because of students’ developmental levels at the times they occur (Eccles & Midgley, 1989). For example, the transition from elementary school to middle school/junior high would be disruptive for anyone, but it becomes especially so for students at that age given the bodily changes they are undergoing and their typical insecurities about their sense of self and appearance. Transitional variables and development most likely interact in reciprocal fashion. Developmental variables can make a transition smooth or rough, but in turn factors associated with the transition might affect students’ personal, social, and cognitive development (Wigfield & Wagner, 2005). APPLICATION 11.3 Developmentally Appropriate Instruction Students learn best in a classroom where instruction is developmentally appropriate. Beginning in preschool and kindergarten, teachers should ensure that students have the opportunity to learn in different ways to address the learning mode that is most appropriate for each child’s developmental level. Ms. Thompson is a kindergarten teacher. For a unit on magnets, she designed a learning station where students individually use magnets of different sizes and shapes. She divided the students into small groups and had them work cooperatively to discover the differences between items that can and cannot be picked up by magnets. She worked with each small group to complete a chart looking at the differences between items attracted by magnets. For story time that day, she read a book about the uses of magnets; while she read, each student had a magnet and items to test. For homework, she asked students to bring two items to class the next day, one of which can be picked up by a magnet and one that cannot. The next day in small groups students tested their items and then discussed why some items were and others were not attracted; she moved around the room and interacted with each group. The transition to middle school/junior high school is especially problematic (Eccles & Midgley, 1989; Wigfield, Byrnes, & Eccles, 2006). This transition occurs at a significant period of physical change in young adolescents with its attendant personal and social changes. Furthermore, numerous changes occur in school and class structures and subject areas. In elementary school, children typically are with the same teacher and peers for most of the school day. The teacher often has a warm and nurturing relationship with the children. Instruction frequently is individualized, and teachers track and report individual progress in content areas. Ability-level differences within a class may be wide, with students ranging from those with learning disabilities to gifted. In contrast, in middle and junior high schools students typically change classes for each subject, which results in different teachers and peers. Teachers develop close relationships with few, if any, students. Instruction is provided to the entire class and rarely individualized. Grades—whether based on absolute or normative standards—do not reflect individual progress, nor is that generally reported. Ability-level within-class differences may be minimal if students are tracked. In general, middle school and junior high classes are more formal, impersonal, evaluative, and competitive (Meece, 2002). Eccles and her colleagues (Eccles & Midgley, 1989; Eccles, Midgley, & Adler, 1984; Wigfield et al., 2006) contended that these structural and curricular changes produce changes in students’ achievement-related beliefs and motivation, often in a negative direction. The opening classroom vignette highlights the problem of student boredom, which negatively affects motivation and learning. APPLICATION 11.4 Transitions in Schooling Making the transition from one school level to another is difficult for many students. Ability and socioemotional levels vary widely, and students differ in their ability to cope with the numerous organizational changes that occur. The transition from elementary to middle school/junior high level can be especially troublesome. Ms. Appleton is a sixth-grade social studies teacher at a middle school. She understands that students become accustomed to having one teacher for most content areas. She works with fifth-grade teachers to suggest activities that they might incorporate (e.g., using assignment notebooks) that will help students when they are faced with changing classes and being responsible for remembering and completing assignments for each class. She also spends time at the start of the school year helping her students set up their assignment books and organize their materials. She makes herself available during lunch and after school to give students assistance they might need about transition issues. Ms. Vanaman, a high school science teacher, asks eighth-grade science teachers about their policies for assigning class work and homework, giving tests, grading projects, receiving late work, allowing students to make up missed work, and so forth. She tries to incorporate some of the same approaches in her ninth-grade science classes so that these class procedures will be familiar and will reduce student concerns that could impede learning. School transitions can be improved. The middle school configuration should help ease the transition. Although some middle schools resemble junior high schools except for a different grade organization (typically grades 6 to 8 in middle schools and grades 7 to 9 in junior high schools), many middle schools attempt to ease the transition by keeping students together for much of the day and using interdisciplinary teams of teachers (e.g., four teachers, one each for language arts, social studies, mathematics, and science). Thus, although the teachers change, most of the peers do not. These teachers work to ensure an integrated curriculum. Greater efforts also may be made to report individual progress. Less emphasis on evaluative comparisons among peers helps to lighten young adolescents’ self-concerns so typical at this time. Application 11.4 gives additional suggestions for ways to ease transitions in schooling. Classroom and School Climate The climate that exists in classrooms and schools can have a major impact on students’ learning and achievement (Bryk, Sebring, Allensworth, Luppescu, & Easton, 2010; Lee & Shute, 2010). Climate refers to the atmosphere, tone, or culture associated with the classroom or school environment (Wolters & Gonzalez, 2008). Climate arises from the shared understandings and interactions, common practices, and accepted routines within the classroom and school. Climate is a function of the teachers, students, curricula, and other important elements in the environment. Most of the six TARGET dimensions (covered earlier in this chapter) would be included in climate. Discussed in this section are three affective aspects of climate that are discussed relevant to learning: sense of community, warmth and civility, and safety and security. Sense of Community. A sense of community includes individuals’ feelings that they belong to a group or organization, that they are committed to the organization’s goals and values, and that there is reciprocity in the relation such that people in the organization care about and are concerned about individual group members. Lee, Bryk, and Smith (1993) stressed the importance of a community perspective on school organization that focuses on the quality of the social relations among the individuals in the organization. They noted that schools where the administrators, faculty, and students demonstrate mutual respect and concern for each other are linked to positive outcomes for teachers and students. Noddings (1992) proposed that caring for others is an important component that should be present in all relations in a school and taught in the curriculum. The student dropout and at-risk literatures also suggest that the absence of teacher concern and care is a major factor mentioned by students who leave school (Connell, Halpern-Felsher, Clifford, Crichlow, & Usinger, 1995; Lee & Smith, 1999; National Research Council, 2004; Natriello, 1986; Rumberger & Lim, 2008). Deci and Ryan (1991) proposed that all individuals have a basic need for belongingness or relatedness and that organizational structures that support or satisfy this need will result in more intrinsic motivation and engagement (see Chapter 9). Research studies document the contributions of students’ sense of school belonging and connection to learning, motivation, school engagement, and academic performance (Connell et al., 1995; Juvonen, 2006; Osterman, 2000; Voelkl, 1997). A sense of school belonging may be particularly important for students entering new school environments or attending schools with diverse student populations (Eccles & Roeser, 2011; Garcia-Reid, Reid, & Peterson, 2005). Warmth and Civility. Warmth and civility are aspects of the relations among individuals in the classroom and school. Researchers have shown that civil and collegial relations between teachers and administrators are associated with positive outcomes relating to organizational effectiveness (Lee et al., 1993). Bryk et al. (2010) refer to this school dimension as relational trust, and they view it as critical for experimenting with instructional practices to improve student engagement and achievement. In addition, friendly and collegial relations among teachers help ease the isolation of teaching and are associated with greater teacher satisfaction (Lee et al., 1993). As discussed earlier, feelings of concern, care, support, and respect between teachers and students are associated with better learning, motivation, and achievement (Wentzel, Battle, Russel, & Looney, 2010). Lee et al. (1993) suggested that a concern for the welfare of others or the creation of a caring community can have positive effects on all students including those at risk for school failure. The literature on middle schools shows that they are more bureaucratic, less personal, and engender fewer positive teacher–student interactions than elementary schools. These differences have been linked to declines in student motivation as the students make the transition to middle schools (Eccles, Midgley, Wigfield, Reuman, Mac Iver, & Feldlaufer, 1993). In their review of research on teaching, Brophy and Good (1986) noted that the emotional climate of the classroom (as defined by teacher criticism and teacher and student negative affect) often is associated with student achievement. Although a negative emotional climate can decrease achievement, a warm emotional climate is not necessarily associated with better achievement. Neutral climates may be just as supportive of achievement as warm climates. Overall, however, the research suggests that positive teacher–student interactions can create a positive climate for all members of the school community. Safety and Security. Feelings of safety and security can refer to individuals’ feelings about taking risks and feeling secure in expressing different ideas and opinions. Given the tragic school violence in recent years, this aspect of climate also refers to feelings of physical safety and being free from the fear and anxiety of physical harm. Despite efforts to increase school safety over the last decades, a significant number of U.S. youth (20–30%) enrolled in public schools report involvement in physical fights or bullying incidents at least once during the school year (Simone, Zhang, & Truman, 2010). Concerns about physical or psychological safety at school has important consequences for students’ perceptions of the school climate, as well as for school engagement, learning, and academic achievement (Crosnoe, Johnson, & Elder, 2004; Eccles & Roeser, 2011; Wentzel et al., 2010). Accordingly, it is important for schools to offer a safe and secure environment for all staff and students. PEERS Researchers have been increasingly investigating the influence of peers on learning, motivation, and other achievement-related outcomes (Ladd, Herald-Brown, & Kochel, 2009; Wentzel, 2005). This section reviews theory and research on peers and student learning, the role of peer networks, and how peers may affect school adjustment and dropout. Peers and Learning Peer influence operates largely through modeling, or behavioral, cognitive, and affective changes that result from observing one or more models (Schunk, 2012; Chapter 4). Three important functions of modeling are inhibition/disinhibition, response facilitation, and observational learning. Observers’ inhibitions about engaging in certain acts can be strengthened and weakened by observing models. When models are punished for their actions, observers’ inhibitions may be strengthened and they are unlikely to perform the same actions because they believe they will be punished if they do. When models go unpunished or are rewarded, observers’ inhibitions may be weakened, and they may perform the same actions. In these cases, models convey information about consequences, and the modeled effects are motivational. Response facilitation occurs when modeled actions serve as social prompts for observers to behave accordingly. As with inhibition and disinhibition, response facilitation actions have been learned; models convey information, and their effects on observers are motivational. Response facilitation can be seen in forms of dress. Students who aspire to be valued by a certain peer group may wear the same type of clothes as those worn by members of that group. Whereas response facilitation behaviors typically are neutral, inhibited/disinhibited actions are rule governed or have moral or legal overtones. A student walking down a hall who sees a group of students looking into a classroom might stop and also look into the classroom. This is a response facilitation effect; the behavior is neutral. Conversely, inhibition occurs when a teacher disciplines one misbehaving student and misbehavior among others stops. Misbehavior is not neutral; it is prohibited. Another difference is that inhibition and disinhibition are more likely to involve emotions (e.g., anxiety, exhilaration), whereas response facilitation rarely does. Observational learning through modeling occurs when observers display new behaviors that prior to modeling had no likelihood of occurring, even with motivational inducements in effect (Schunk, 2012). Observational learning expands the range and rate of learning over what could occur if each response had to be performed and reinforced. Observational learning comprises attention, retention, production, and motivation. Observers who are motivated to learn modeled actions are likely to attend to models, attempt to retain the modeled actions, perform them when necessary, and be motivated to do so. These three forms of modeling are easily discerned among students and highlight peer effects on students’ learning and achievement. Altermatt and Pomerantz (2003) found high consistency among best friends’ report card grades. When a child’s friends value school and engage actively in scholastic activities, these models can have a positive effect on academic learning and motivation. The opposite may occur when peers express negative attitudes and withdraw from school (Ladd et al., 2009). Model similarity affects observers’ self-efficacy. Observing similar others (e.g., peers) succeed can raise observers’ self-efficacy and motivate them to perform the task; they believe that if others can succeed, they can as well. Observing others fail can lead students to believe that they lack the competence to succeed and dissuade them from attempting the task. Similarity is most influential when students are uncertain about their performance capabilities, when they lack task familiarity, have little information to use in judging self-efficacy, or when they previously experienced difficulties and hold self-doubts (Bandura, 1986; Schunk, 2012). Students who have positive peer relations perform better in school, and these associations are found across the school years (Wentzel, 2005). Jones, Audley-Piotrowski, and Kiefer (2012) found that 10th-grade students’ mathematics self-concepts aligned well with their perceptions of their friends’ academic behaviors. Mathematics self-concepts, in turn, related positively to mathematics performance. Teachers also attribute positive academic characteristics (e.g., high achievement, confidence) to children who are popular with peers (Wentzel, 2005). By contrast, less popular or socially rejected children are at risk for lower academic achievement. To explain the potential influence of peer relations on learning, Wentzel (2005) postulated that social competence involves displaying such behaviors as helping, sharing, cooperating, and avoiding problem behaviors. These behaviors involve self-regulatory processes (e.g., goal setting, self-monitoring; see Chapter 10) that contribute to learners’ academic and intellectual development (Wentzel, 2005). Research studies indicate that school engagement increases when students feel a sense of belonging (Juvonen, 2006; Osterman, 2000). This sense also acts as a protective factor against nonacademic risk behaviors such as depression and pregnancy (Anderman, 2002). Given this research evidence, measures of classroom social climate often include items to assess feelings of support, care, and encouragement from peers. Students’ perceptions of peer emotional and academic support are positively related to several achievement-related behaviors, including mastery goals, selection of self-regulation strategies, and classroom engagement and learning (Patrick, Ryan, & Kaplan, 2007). It seems that supportive peer relations provide a secure base for learners to engage in academic learning and take risks without fear of ridicule. Peer Networks Researchers have examined the role of peer networks, or large groups of peers with whom students associate. Students in peer networks tend to be similar to one another in many ways (Cairns, Cairns, & Neckerman, 1989), which enhances the likelihood of influence by modeling (Schunk, 1987). Networks help define students’ opportunities for social interactions and access to activities (Ryan, 2000). Over time, network members become more similar to one another. Kindermann (1993; Kindermann, McCollam, & Gibson, 1996; Sage & Kindermann, 1999) examined motivation in peer selection and socialization among children (grades 4–5) and adolescents (grades 9–12). Adolescent peer networks were more complex than children’s. Among children, most peer networks were dyads; average network size was 2.2 students. Large networks were uncommon. Among adolescents there were many dyads and triads, as well as larger networks (average size was 3.2 students). Among both children and adolescents, some students were not connected with a network. Kindermann’s research also discovered gender differences. Among children, groups were composed exclusively of members of the same sex. Among adolescents, there were some groups that included boys and girls. A significant decline in motivation was reported by students, although teachers reported comparable levels of student motivation across grades. Older students expressed lower motivation than did younger ones. Comparisons of individual academic motivation scores with peer group motivation scores showed that among ninth graders, students who were more academically motivated had larger peer networks. Adolescents who were less motivated had fewer classmates in their peer networks. Across the school year and grade levels, students’ motivation scores remained consistent. There was evidence of motivational selection and socialization through peer groups. Changes in children’s motivational engagement across the school year were predicted by their peer group membership at the start of the year. There also were effects due to peer networks containing students from different grades. Students in highly motivated peer groups that contained members from across grades tended to increase in motivation across the school year. Students in low-motivation peer networks that had little grade diversity tended to decrease in motivation across time. Sage and Kindermann (1999) found that peer groups approved or disapproved of peers’ behaviors depending on whether the behaviors were consistent with group norms. Students with higher academic motivation were likely to be members of groups more motivated for academics, and they received group approval for positive academic behaviors. Students with lower motivation tended to be members of less motivated groups; their approval for positive behaviors mostly came from teachers. Children affiliated with highly motivated groups changed positively across the school year; children in less motivated groups changed negatively. Among adolescents, the evidence for change was strongest in peer groups that included peers from different grades. Kindermann (2007) found that sixth-graders’ peer networks were homogeneous in their academic (behavioral and emotional) engagement and that such homogeneity persisted during the school year even where there was member turnover. Students who initially shared networks with engaged peers maintained or increased their engagement; those in less engaged networks showed less engagement. Other studies have obtained similar findings (Ryan, 2000). Ryan (2001) found that students typically end up in peer networks with similar motivational beliefs to theirs at the beginning of a school year. During the year the peer group influenced the members of the group, so that group members became more homogeneous. The peer group socialization influence depended on the nature of the outcome. Students’ intrinsic interest in school and their academic performances (grades) were influenced by the peer group. The utility value that students had for school (how useful they though school work was) was not related to peer influence but rather was more a function of selection into certain peer groups from the beginning of the year. These findings are supported by longitudinal research by Steinberg, Brown, and Dornbusch (1996), who followed students over 3 years from when they entered high school until their senior year. Steinberg et al. determined whether students who began high school equivalent academically (grades) but who became affiliated with different crowds (i.e., like-minded students who have some common attributes but are not friends with everyone else) remained stable academically. Crowds influenced academic performance and delinquency. Children in higher academically oriented crowds achieved better during high school compared with those in lower academically oriented crowds. Students in crowds in which delinquency occurred more often became increasingly delinquent (i.e., more conduct problems and drug and alcohol use). Students in less delinquent crowds did not develop the same problems. Steinberg et al. also found developmental patterns in the influence of peer pressure on many activities including academic motivation and performance. Peer pressure rose during childhood and peaked around the eighth or ninth grade, but then declined through high school. A key time of influence was roughly from age 12 through 16. Interestingly, it is around this time that parental involvement in children’s activities declines. As parents’ role declines and peers’ role ascends among adolescents in grades 6 through 10, they become especially vulnerable to peer pressure. Steinberg et al. noted that parents typically set their children onto a particular trajectory by establishing goals for them and involving them in groups and activities. Thus, parents who want their child to be academically oriented are likely to involve the child in activities that stress academics. If the peer crowd in those settings also is academically focused, the peer influence complements that of the parents. But if there are other types of crowds in those settings, the child may come under the influence of a less academically oriented crowd, with negative consequences for academic learning. Peers and School Adjustment School adjustment often is defined in terms of students’ academic progress or achievement (Birch & Ladd, 1996). On a broader level, adjustment can be viewed as comprising not only students’ progress and achievement but also their attitudes toward school, anxieties, loneliness, social support, and academic motivation (e.g., engagement, avoidance, absences; Birch & Ladd, 1996; Roeser, Eccles, & Strobel, 1998). Involvement refers to the quality of a student’s relationships with peers and teachers. Ryan and Powelson (1991) contended that school learning can be promoted by learning environments that enhance student involvement with others. Research shows that children’s loneliness and social dissatisfaction relate negatively to school achievement (Galanaki & Kalantzi-Azizi, 1999). Berndt and Keefe (1992) found that peer pressure can affect adjustment and that peer pressure operated more often in a positive rather than a negative manner. Friends often discourage negative behavior, drug and alcohol use, and poor academic performance, and encourage prosocial behavior, good studying behaviors, and academic motivation (Berndt & Keefe, 1996). Friendships can affect students’ success in the transition from elementary to junior high school. Berndt, Hawkins, and Jiao (1999) found that students with high-quality friendships across the transition demonstrated increased leadership and sociability. But students’ behavior problems increased across the transition if they had stable friendships with peers high in behavior problems. Researchers have shown that children and adolescents whose friendships have a positive quality display greater prosocial behavior, are more popular, hold higher self-esteem, have fewer emotional problems, have better attitudes toward school, and achieve at a higher level in school, compared with other students (Berndt & Keefe, 1996). Wentzel, Barry, and Caldwell (2004) found that friends’ prosocial behaviors predicted changes in peers’ prosocial behaviors as a function of changes in goals to behave prosocially. Friendships with negative qualities led to less student classroom involvement and more disruptive behavior. Interestingly, number of friends is weakly correlated with school adjustment, which implies that relationship quality is more influential than quantity. Poor school adjustment also can lead to dropout, which is a major problem confronting schools and society today (Rumberger, 2010). Many researchers have investigated the influence of such variables as early academic achievement, socioeconomic status, and family influences, but peers also play a role. Feelings of relatedness contribute to motivation and learning, and students’ relations with peers are part of this influence. Hymel, Comfort, Schonert-Reichl, and McDougall (1996) suggested that students’ involvement and participation in school depend partly on how much the school environment contributes to their perceptions of autonomy and relatedness (Chapter 9), which in turn influence perceptions of competence (self-efficacy; Chapter 4) and academic achievement. Hymel et al. (1996) identified four critical aspects of peer influence. One is prior social acceptance within a peer group. Students rejected by peers are at a greater risk for adjustment problems than those who are socially accepted. Research studies also show that students who are not socially accepted by peers are more likely to drop out of school than those with greater social acceptance (Hymel et al., 1996; Jimerson, Egeland, Sroufe, & Carlson, 2000). A second factor is social isolation versus involvement. Not all socially rejected youth drop out of school. What seems more important is students’ perception of rejection or isolation within the peer group. Students who are socially rejected but do not perceive themselves that way are at lower risk for dropping out. A third factor is the negative influence of peers. The peer crowd can affect students’ motivation and learning (Newman, 2000). Students who quit school are more likely than others to be part of a crowd that is at risk for dropping out (Cairns et al., 1989). It seems that the crowd may collectively disengage from school. Even when students are not socially isolated, they are affected by negative peer influence. Finally, aggression and antisocial behavior contribute to dropping out. Compared with students who graduate, those who drop out are rated by teachers and peers as displaying more aggressive behaviors (Farmer et al., 2003; Hymel et al., 1996). Strong associations between poor peer relations and later high school dropout are found as early as elementary school (Jimerson et al., 2000). Research that explores the link between adjustment and learning will contribute to our understanding of this relation and potentially offer guidelines for educators to improve students’ adjustment and diminished chances for dropping out. FAMILIES Of the are many contextual factors that can influence learning, several are found in the family environment. Although common sense tells us that families have profound influences on children’s development and learning, some critics contend that the family’s role has been overstated (Harris, 1998). Researchers are increasingly showing, however, that families make a difference and often a great one (Collins, Maccoby, Steinberg, Hetherington, & Bornstein, 2000; Masten & Coatsworth, 1998). Some of the key family influences on learning are socioeconomic status, home environment, parental involvement, and electronic media. Socioeconomic Status Definition. Socioeconomic status (SES) has been defined in various ways, with definitions typically comprising social status (position, rank) and economic indicators (wealth, education). Many researchers consider three prime indicators in determining SES: parental income, education, and occupation (Sirin, 2005). Increasingly investigators are stressing the idea of capital (resources, assets; Bradley & Corwyn, 2002). Capital includes such indices as financial or material resources (e.g., income and assets), human or nonmaterial resources (e.g., education), and social resources (e.g., those obtained through social networks and connections; Putnam, 2000). Each of these would seem to potentially affect children’s learning. However SES is defined, it is important to remember that it is a descriptive variable, not an explanatory one (Schunk et al., 2014). To say that students have learning difficulties because they are from poor families does not explain why they have learning difficulties. Rather, the conditions that often are found in poor families may be responsible for the learning difficulties. Conversely, not all children from poor families have learning difficulties. There are countless stories of successful adults who were raised in impoverished conditions. It therefore is more meaningful to speak of a relation between SES and learning and then look for the responsible conditions. SES and Learning. Much correlational research evidence shows that poverty and low parental education relate to poorer development and learning (Bradley & Corwyn, 2002). What is less clear is which aspects of SES are responsible for this relation. Family resources seem critical. Families with less education, money, and social connections cannot provide many resources that can promote students’ cognitive development and learning. For example, students from wealthier families have greater access to computers, books, games, travel, and cultural experiences, compared with students from poorer families. These and other resources can stimulate cognitive development and learning. Another important factor is socialization. Schools and classrooms typically have a middle-class orientation with accepted rules and procedures that children must follow to succeed (e.g., pay attention, do your work, study, work cooperatively with others). Socialization influences in lower-SES homes may not prepare students adequately for these conditions (Schunk et al., 2014). As a consequence, lower-SES children may have more discipline and behavior problems in school and not learn as well. SES also relates to school attendance and years of schooling (Bradley & Corwyn, 2002). SES is related positively to school achievement (Sirin, 2005) and is, unfortunately, one of the best predictors of school dropout. Lower SES children may not understand the benefits of schooling (Meece, 2002); they may not realize that more education leads to better jobs, more income, and a better lifestyle than they have experienced. They may be drawn by immediate short-term benefits of leaving school (e.g., money from working full-time) and not be swayed by potential long-term assets. In their home environments, they may not have positive role models displaying the benefits of schooling or parental encouragement to stay in school. The relation of SES to cognitive development seems complex, with some factors contributing directly and others serving a moderating influence (Bradley & Corwyn, 2002). Its predictive value also may vary by group. For example, SES is a stronger predictor of academic achievement for White students than for minority students (Sirin, 2005). SES has been implicated as a factor contributing to the achievement gap between White and minority children. Gaps exist when children enter kindergarten. The White–Hispanic American gap narrows in kindergarten and first grade (perhaps because of Hispanic American children’s increasing English language proficiency) and then stays steady through fifth grade; however, the White–African American gap continues to widen through fifth grade (Reardon & Galindo, 2009). While the effects of material, human, and social capital seem clear, the influence of other factors may be indirect. For example, large families are not inherently beneficial or harmful to cognitive development and learning. But in deprived conditions they may be harmful as already scarce resources are spread among more children. The literature suggests that early educational interventions for children from low-SES families are critical to ensuring their preparation for schooling. One of the best known early intervention efforts is Project Head Start, a federally funded program for preschool children (3- to 5-year-olds) from low-income families across the United States. Head Start programs provide preschool children with intensive educational experiences, as well as social, medical, and nutritional services. Most programs also include a parent education and involvement component (Washington & Bailey, 1995). Early evaluations of Head Start indicated that programs were able to produce short-term gains in intelligence test scores. Compared to comparable groups of children who had not attended Head Start, participants also performed better on cognitive measures in kindergarten and first grade (Lazar, Darlington, Murray, Royce, & Snipper, 1982). Although Head Start children lost this advantage by ages 10 and 17, other measures of program effectiveness indicated that participants were less likely to be retained, to receive special education, and to drop out of high school than nonparticipants (Lazar, Darlington, Murray, Royce, & Snipper, 1982). Providing Head Start teachers with training and professional development on practices to enhance children’s literacy and socioemotional skills can lead to gains in children’s social problem-solving skills (Bierman et al., 2008). Home and family factors can affect outcomes for Head Start participants. Robinson, Lanzi, Weinburg, Ramey, and Ramey (2002) identified at the end of third grade the top-achieving 3% of 5,400 children in the National Head Start/Public School Early Childhood Transition Demonstration Project. Compared with the remaining children, these children came from families that had more resources (capital). These families also endorsed more positive parenting attitudes, more strongly supported and encouraged their children’s academic progress, and volunteered more often in their children’s schools. Teachers reported these children as more motivated to succeed academically. Although there were not strong differences in children’s ratings of motivational variables, fewer children in the top 3% group rated school negatively compared with the remaining children. Thus, among low-income groups as well as the general population, greater parental support and better home resources are associated with achievement and motivational benefits for children. Encouraged by the success of Head Start, states operate prekindergarten programs for 3- and 4-year-olds under the auspices of public schools to reduce the number of children failing in the early grades (Clifford, Early, & Hill, 1999). Most programs are half day and vary with regard to teacher–student ratios, socioeconomic and ethnic diversity, quality, and curricula. Early evaluations of these programs are promising. Children enrolled in prekindergarten programs tend to improve on standardized measures of language and mathematics skills (FPG Child Development Institute, 2005). The long-term benefits of these programs are not yet known. One highly effective preschool program for low-income children was the High/Scope Perry Preschool Project. Initiated in 1962, this program predated Head Start. In this two-year program, 3- and 4-year-old children received a half-day cognitively oriented program based on Piaget’s principles (Oden, Schweinhart, & Weikart, 2000). Teachers also made weekly 90-minute home visits to each mother and child to review classroom activities and to discuss similar activities in the home. Longitudinal data collected over 25 years revealed that the High/Scope program improved children’s school achievement, reduced their years in special education, reduced the likelihood of grade retention, and increased the years of school completed (Oden et al., 2000; Schweinhart & Weikart, 1997). Unfortunately, the effects of such early interventions do not always persist over time as children progress in school, but there are promising results. Campbell, Pungello, Miller-Johnson, Burchinal, and Ramey (2001) evaluated the Abecedarian Project, a full-time educational child-care project for children from low-income families. These researchers found that the benefits of the intervention persisted through the last evaluation when many of the children had attained age 21. Given the longitudinal nature of this project (it began when the participants were infants), it is difficult to determine when and how it prepared them to be successful in educational environments. SES is an active area of research, and we are sure to learn more about the roles of SES variables in learning. Home Environment There is much variability in the richness of home environments, and usually (but not always) this richness matches SES. Some homes provide experiences replete with economic capital (computers, games, and books), human capital (parents help children with homework, projects, and studying), and social capital (through social contacts parents get children involved in activities and teams). Other homes lack in one or more of these respects. The effects of the home environment on cognitive development seem most pronounced in infancy and early childhood (Meece, 2002). Children’s social networks expand as they grow older, especially as a consequence of schooling and participation in activities. Peer influence becomes increasingly important with development. The quality of children’s early home learning relates positively to the development of intelligence (Schunk et al., 2014). Important home factors include mother’s responsiveness, discipline style, and child involvement; organization present in the home; availability of stimulating materials; and opportunities for interaction. Parents who provide a warm and responsive home environment tend to encourage children’s explorations and stimulate their curiosity and play, which accelerate intellectual development (Meece, 2002). As discussed earlier, the increasing role of peer influence was found in longitudinal research by Steinberg et al. (1996). Over a 10-year period, these researchers surveyed more than 20,000 adolescents from high schools in different states and interviewed many teachers and parents. Although parents do not have full control over the crowds with which their children associate, they can exert indirect influence by steering children in appropriate directions. Parents who urge their children to participate in activities in which the children of other like-minded parents participate steer them toward appropriate peer influence regardless of whom they select as friends. Parents who offer their home as a place where friends are welcome further guide their children in positive directions. Parental Involvement Harris (1998) downplayed the influence of parents on children past infancy and concluded that peers exert a much greater effect; however, there is substantial evidence that parental influence continues to be strong well past infancy (Vandell, 2000). This section considers the role of parental involvement in children’s cognitive development and learning. Parental involvement occurs in and away from the home, such as in school and activities. Research studies show that parental involvement in schools has a positive impact on children, teachers, and the school itself (Englund, Luckner, Whaley, & Egeland, 2004; Gonzalez-DeHass, Willems, & Doan Holbein, 2005; Hill & Craft, 2003; Sénéchal & LeFevre, 2002). These effects may vary by group; parent involvement effects seem stronger among White students than among minority students (Lee & Bowen, 2006). One effect of parent involvement, as noted above, is that parents can be influential in launching their children onto particular trajectories by involving them in groups and activities (Steinberg et al., 1996). For example, parents who want their children to be academically focused are likely to involve them in activities that stress academics. Fan and Chen (2001) conducted a meta-analysis of research on the relation of parental involvement to children’s academic attainments. The results showed that parents’ expectations for their children’s academic successes bore a positive relation to their actual cognitive achievements. The relation was strongest when academic attainment was assessed globally (e.g., GPA) than by subject-specific indicators (e.g., grade in a particular class). There also is evidence that parental involvement effects on children’s achievement are greatest when there is a high level of parental involvement in the neighborhood (Collins et al., 2000). Parental involvement is a critical factor influencing children’s self-regulation, which is central to the development of cognitive functioning. Research by Stright, Neitzel, Sears, and Hoke-Sinex (2001) found that the type of instruction parents provide and how they provide it relate to children’s self-regulated learning in school. Children of parents who provided understandable metacognitive information displayed greater classroom monitoring, participation, and metacognitive talk. Children’s seeking and attending to classroom instruction also were related to whether parents’ instruction was given in an understandable manner. These authors suggested that parental instruction helps to create the proper conditions for their children to develop self-regulatory competence. Some suggestions for parents working with their children are given in Application 11.5. Positive effects of parental involvement have been obtained in research with ethnic minority children and those from impoverished environments (Hill & Craft, 2003; Masten & Coatsworth, 1998; Masten et al., 1999). Some forms of parents’ involvement that make a difference are contacting the school about their children, attending school functions, communicating strong educational values to their children, conveying the value of effort, expecting children to perform well in school, and monitoring or helping children with homework and projects. Miliotis, Sesma, and Masten (1999) found that after families left homeless shelters, high parent involvement in children’s education was one of the best predictors of children’s school success. Researchers have investigated the role of parenting styles on children’s development. Baumrind (1989) distinguished three styles: authoritative, authoritarian, permissive. Authoritative parents provide children with warmth and support. They have high demands (e.g., expectations for achievement), but support these through good communication, explanations, and encouragement of independence. Authoritarian parents are strict and assert power. They are neither warm nor responsive. Permissive parents are moderately responsive but are lax in demands (e.g., expectations) and tolerant of misbehavior. Not surprisingly, many research studies have found a positive relation between authoritative parenting and student achievement (Spera, 2005). APPLICATION 11.5 Parental Involvement McGowan Elementary School holds an open house for parents early in the school year. When meeting with parents, McGowan teachers explain the many ways that parents can become involved. Teachers ask for volunteers for three groups: school learning, out-of-school learning, and planning. School-learning parents volunteer a half-day per week to work in class, assisting with small-group and individual work. Out-of-school-learning parents accompany the class on field trips and organize and work with children on community projects (e.g., a walk through the neighborhood to identify types of trees). Planning-group parents periodically meet as a group with teachers who explain upcoming units and ask parents to help design activities. McGowan’s goal is 100% involvement of at least one parent or guardian per child. Parents can be a valuable resource in history classes because they have lived through some of the events students study. Mr. Sakizch contacts parents at the start of the year and provides them with a list of events in the past several years that students will study in class (e.g., Vietnam War, fall of the Berlin wall, World Trade Center terrorist attack). He seeks the assistance of every family on at least one event, such as by the parent coming to class to discuss it (i.e., what they remember about it, why it was important, how it affected their lives). When several parents volunteer for the same event, he forms them into a panel to discuss the event. If there are living grandparents in the area, Mr. Sakizch asks them to share their experiences about such events as the Great Depression, World War II, and the Eisenhower presidency. His students set up a website containing information about key events and excerpts from parents and grandparents about them. One of the strongest advocates of community and parental involvement in education is James Comer. Comer and his colleagues began the School Development Program in two schools, and it has now spread to more than 500. The SDP (or Comer Program) is based on the principles shown in Table 11.4 (Comer, 2001; Comer & Haynes, 1999; Emmons, Comer, & Haynes, 1996). Students need positive interactions with adults because these help to form their behaviors. Planning for student development should be a collaborative effort between professionals and community members. Three guiding principles of SDP are consensus, collaboration, and no-fault (Schunk et al., 2014). Decisions are arrived at by consensus to discourage taking sides for critical votes. Collaboration means working as part of a team. No-fault implies that everyone is responsible for change. School staff and community members are grouped into teams. The School Planning and Management Team includes the building principal, teachers, parents, and support staff. This team plans and coordinates activities. The Parent Team involves parents in all school activities. The Student and Staff Support Team is responsible for schoolwide prevention issues and individual student cases. Table 11.4 Principles of the School Development Program. ■ Students’ behaviors are determined by their interactions with the physical, social, and psychological environments. ■ Students need positive interactions with adults to develop adequately. ■ Student-centered planning and collaboration among adults facilitate positive interactions. ■ Planning for student development should be done collaboratively by professional and community members. At the core of the SDP is a comprehensive school plan with such components as curriculum, instruction, assessment, social and academic climate, and information sharing. This plan provides structured activities addressing academics, social climate, staff development, and public relations. The School Planning and Management Team establishes priorities and coordinates school improvement. Comer and his colleagues report impressive effects on student achievement due to implementation of the SDP (Haynes, Emmons, Gebreyesus, & Ben-Avie, 1996). Comer schools typically show gains in student achievement and often outperform school district averages in reading, mathematical, and language skills. Cook, Murphy, and Hunt (2000) evaluated the Comer SDP in 10 inner-city Chicago schools over four years. Using students in grades 5 through 8, these authors found that by the last years, Comer Program students showed greater gains in reading and mathematics compared with control students. Cook et al. (1999) found that Comer schools do not always implement all of the program’s elements, which can limit gains. Regardless of whether schools adopt the Comer Program, it contains many points that should facilitate students’ cognitive development and learning. Electronic Media The advent of electronic media began in the middle of the 20th century when televisions became common household items. In recent years, the potential influence of electronic media has expanded with increased television programming (i.e., cable channels), audio and video players, radios, video game players, computers (e.g., applications, Internet), and handheld devices (e.g., cell phones, iPods). The amount of time that children spend engaged with electronic media daily can seem daunting. In 2005, children age 6 and younger averaged over 2.25 hours per day (Roberts & Foehr, 2008). In 2004, children ages 8 to 18 reported an average of almost 8 hours of daily electronic media exposure that consumed almost 6 hours of their time (i.e., about 25% of the time they were using more than one media source at once—multitasking; Roberts & Foehr, 2008). Researchers have investigated the potential ways that exposure to electronic media relates to children’s cognitive development, learning, and achievement. Most research has investigated the link between children’s television viewing and measures of cognitive development and achievement and found no relationship or a negative relationship between the time children spend watching television and their school achievement (Schmidt & Vandewater, 2008). When negative associations are found, they typically are weak. But these results are misleading because the relation may not be linear. Compared with no television viewing, moderate viewing (1–10 hours) per week is positively associated with achievement, whereas heavier viewing is negatively associated. The relation between television viewing and achievement also is difficult to interpret because the data are correlational and therefore causality cannot be determined. Several causal explanations are possible. It is possible that heavy television viewing lowers achievement because it takes children away from studying and completing assignments. It also is possible that children with academic problems are less motivated to learn academic content and thus are drawn more strongly to television. The link between television viewing and achievement may be mediated by a third variable, such as SES. In support of this possibility, children from lower-class homes tend to watch more television and demonstrate lower achievement (Kirkorian, Wartella, & Anderson, 2008). Young children’s learning from television also may depend on the extent that they interact with the televised characters and believe that these characters exist outside of television. The latter belief is associated with children believing the characters are socially relevant and thus are reliable sources of information (Richert, Robb, & Smith, 2011). Examining the relation between time spent watching television and academic achievement does not consider the content of what children watch. Television programming varies; some programs are educational, whereas others are entertaining or violent. A general finding from research is that watching educational programming is positively related to achievement, whereas watching entertainment is negatively linked (Kirkorian et al., 2008). This varies as a function of amount of television watched, because moderate viewers are more apt to watch educational programming whereas heavier viewers watch extensive entertainment. Correlational research has demonstrated a positive relation between exposure to Sesame Street and school readiness (Kirkorian et al., 2008). Ennemoser and Schneider (2007) found a negative association between the amount of entertainment television watched by children at age 6 and reading achievement at age 9, after controlling for intelligence, SES, and prior reading ability. Watching educational television was positively associated with reading achievement. Other research has shown that educational television can help prepare at-risk children in low-SES families to read (Linebarger & Piotrowski, 2010), especially when the televised content is integrated with learning strategies that help children process the content (e.g., showing a word that breaks into individual sounds, then reassembles into the word). The findings on the link between interactive media (e.g., video games, Internet) and school achievement are mixed. Some research has obtained a positive relation between computer use and achievement and a negative association between video game use and achievement (Kirkorian et al., 2008). The same result obtained with television also may hold for other media; that is, educational content may link positively with achievement and entertaining content negatively. With respect to measures of cognitive development, research has identified a video deficit among infants and toddlers such that they learn better from real-life experiences than from video. This deficit disappears by around age 3, after which children can learn just as well from video experiences (Kirkorian et al., 2008). It may be that young children are less attentive to dialogue and do not fully integrate content portrayed across different scenes, which may change rapidly. This does not imply that viewing is negatively associated with the development of attention. Again, the critical variable may be the content of the programming. Educational programs have been shown to actually help children develop attention skills, in contrast to entertaining programs (Kirkorian et al., 2008). Some research has investigated links between electronic media and the development of spatial skills. Most of this research has involved video games. There is some evidence that video games can have short-term benefits on spatial reasoning and problem-solving skills (Schmidt & Vandewater, 2008). However, long-term benefits depend on whether students generalize those skills to learning contexts outside of game play. To date, evidence does not support the point that such transfer occurs (Schmidt & Vandewater, 2008). Video game use is associated with increased aggression among boys (Hofferth, 2010). Parents and other adults can have important influences on children’s learning and cognitive development from electronic media. Adults can control what media children interact with and how much time they do so. This control can help ensure that children do not spend an excessive amount of time engaged with media, but rather only a moderate amount (1–10 hours per week; Schmidt & Vandewater, 2008). Further, parent coviewing seems to be a critical variable. Adults who interact with media while their children are engaged (e.g., watch television programs together) can enhance benefits from electronic media by pointing out important aspects of the program and linking those with what children previously have learned. Some research studies have found benefits from coviewing on children’s learning and development of attention (Kirkorian et al., 2008). In summary, it is clear that use of electronic media is associated with children’s learning, achievement, and cognitive development. Determining causal links is difficult because data are correlational and there are potential mediating variables. Content is of utmost importance. Moderate exposure to televised educational content is associated with benefits for children, entertaining content is not, and the same results may hold for other forms of media (Kirkorian et al., 2008). Coviewing adults can further enhance the educational links. While games may have some benefits for spatial and problem-solving skills, evidence does not show transfer to academic learning settings. Although electronic media can be a valuable means of learning, they will be effective to the extent that they are designed with sound instructional principles in mind, just like any other teaching method. Some applications of instructional uses of electronic media are given in Application 11.6. APPLICATION 11.6 Electronic Media At the parent meeting at the start of the school year, fourth-grade teacher Mr. Simonian discusses how parents can help their children. He explains research findings showing that children who watch television for a moderate amount of time per week (up to 10 hours) and whose television viewing is primarily educational content actually can benefit from it. Engaging with other educational media (e.g., computers) is similarly beneficial. He advises parents to monitor children’s use of electronic media. He also demonstrates how parents might interact with children while they view television programs together. Mr. Simonian presents film clips from children’s shows and then demonstrates to parents the types of questions to ask children. At individual parent meetings later in the school year he asks parents how they are engaging with their children with media. Middle school science teacher Ms. Wolusky gives students assignments to watch science programs on television (e.g., PBS). For each program, students are to write a short essay that answers questions that she gives them in advance. By giving these assignments, she feels that she can help to focus their attention on those aspects of the programs that are most germane to the content of the course and thereby promote students’ learning. COMMUNITIES Many contextual influences on students’ learning arise from communities. Although the influence of communities is typically acknowledged, until recently there has been little systematic research examining its effects. Fortunately, this topic is drawing increased research attention. This section discusses contextual influences from school location and involvement. Location Students’ experiences with schools are influenced by the schools’ physical (geographic) locations. Most students in the United States attend urban or suburban schools (Provasnik et al., 2007). When these two types of school communities are compared, urban students generally trail their suburban peers in standardized achievement, school attendance, high school completion, and college attendance. Urban schools present many challenges for learning (Bryk et al., 2010). They tend to be large and often serve a high percentage of ethnic minority, non-native-English language speaking, and low-income students. Urban students also may face challenges regarding school safety, access to highly qualified teachers, and teacher absenteeism and turnover (National Research Council, 2004). These and other factors can put students at risk for poor school performance, school disengagement, and educational attainment. For these reasons, educational reform efforts have focused largely on urban schools (Balfanz, Herzog, & Mac Iver, 2007; Bryk et al., 2010; Legters, Balfanz, Jordan, & McPartland, 2002; National Research Council, 2004). But rural communities also face challenges in meeting the academic learning needs of their students, although rural schools have received less attention in the national conversation on school reform. One-third of America’s public schools are located in rural areas (Provasnik et al., 2007). Poverty rates actually are higher for students in rural than in nonrural areas, and poverty tends to be intergenerational, long term, and concentrated in ethnic minority populations in remote geographical areas. Like their urban peers, rural students encounter economic hardship, but they also experience difficulties related to geographical location, limited community resources, low parental education, and teacher recruitment and retention. Rural students in impoverished communities have high dropout rates (Provasnik et al., 2007). Researchers investigating the role of school location on student learning are facilitating the discussion on how community resources, values, and norms may affect students’ schooling experiences related to important learning and motivation variables such as engagement, belonging, self-efficacy, and school valuing. The availability of large national datasets should stimulate further research and understanding of ways to offset potential negative effects of school location on students’ educational outcomes. Community Involvement Community involvement in education is not new, although there is renewed interest in the topic today. Most schools attempt to involve their communities in many ways. The social institutions within a community (e.g., families, schools, churches, workplaces) are considered forms of community social capital (Israel & Beaulieu, 2004), which may help alleviate family and school resource constraints. For example, research indicates that participation in school-based extracurricular activities positively influences students’ educational aspirations, especially for those who struggle to maintain a connection to their school (Finn, 1989). There are various forms of community involvement. As discussed earlier in this chapter, the most common form is parent volunteers who work in schools, are active in parent organizations, assist with after-school activities, and help organize events. Community members may be invited to talk with children and visit classrooms. Research suggests that to improve student motivation, learning, and achievement, community involvement must go beyond assisting schools with tutoring and field trips. It is important for community members to serve on school boards, share in school governance, and support school improvement initiatives. Community involvement in school governance is a critical component of the School Development Program (Comer, 2001). Shared community-school governance contributed to important achievement gains in efforts to improve Chicago’s elementary schools (Bryk et al., 2010). Another form of involvement is taking students into the community, such as when students go on field trips. Field trips seem most beneficial when there are clear learning goals and when teachers help prepare students in advance of the trips (e.g., by providing information and engaging students in hands-on activities), which reduces novelty on the trips and helps students focus on the learning objectives (Pugh & Bergin, 2005). Apprentice programs with community businesses often are established, where students spend part of the day receiving training in business procedures. Various community agencies have provided programs for children and youth when they are not in school; for example, Boy or Girl Scouts of America, YMCA/YWCA, and 4-H clubs. Over the last several years, community-sponsored organizations have expanded to include Little League teams, soccer teams, church youth groups, and so on. Youth programs can be found in libraries, museums, and community centers. Many schools provide before- and after-school programs with an explicit academic focus. With federal funding for 21st Century Community Learning Centers, these initiatives help create partnerships between schools and nonprofit community agencies to provide safe, drug-free, supervised environments for students during nonschool hours. Many of these programs include tutoring and educational enhancements, as well as opportunities for enrichment and recreation (Mahoney, Parente, & Zigler, 2010). Whether students benefit from these out-of-school activities depends on the quality of the program and its content (National Research Council & Institute of Medicine, 2002). Community-based programs are linked to positive student development outcomes, academic learning and achievement, attitudes toward schools, and classroom behaviors, when the following features are present: safety, structure, skill-building opportunities, supportive relations, positive social norms, opportunities to belong, and integration of family, school, and community initiatives (National Research Council & Institute of Medicine, 2002). Out-of-school activities should be most beneficial when they are linked with academic material and promote students’ identification with the school (Valentine, Cooper, Bettencourt, & DuBois, 2002). Such activities are apt to improve students’ beliefs (e.g., self-efficacy), which can raise academic motivation and learning (Schunk & Pajares, 2009). The benefits of participating in high-quality community- or school-based programs are particularly strong for low-income children (Mahoney et al., 2005). CULTURES As societies become increasingly diverse, schools become less homogeneous. Contextual influences stemming from students’ cultural backgrounds can affect learning and other educational outcomes. Culture refers to the shared norms, traditions, behaviors, languages, and perceptions of a group (King, 2002). Cultural differences are found not only between communities but also within them. Cultural differences can arise from many factors discussed in this text including ethnicity, SES, home environments, and group identities and experiences. Students also can be affected by different cultures when they identify with overlapping groups. Researchers often have investigated differences in students’ learning, motivation, and other outcomes, as a function of cultural background, but many studies have found little if any evidence of them. A lack of obtained cultural differences can be found when cultural variables are treated as control variables; that is, their effects are controlled statistically so that the effects of other variables on educational outcomes can be studied. Cultural identities and differences are often merged, and researchers provide general interpretations of data (Portes, 1996). It is important to examine potential cultural differences in motivation and learning. Such research contributes to our understanding and provides a basis for offering suggestions for teaching diverse learners. Although researchers have shown that many of the findings discussed in this text are robust across cultures, this is not always the case. Thus, we should not assume that findings obtained with students in Western cultures apply to those from other cultural backgrounds. With respect to goal orientations (Chapter 9), for example, we might ask whether students from other cultures are as concerned with appearing competent to others, performing better than peers, and pursuing academic and social goals. McInerney, Hinkley, Dowson, and Van Etten (1998) assessed mastery, performance, and social goals among three groups of Australian high school students: Anglo Australian, Aboriginal Australian, and immigrant-background Australian. The results showed that the three groups were similar in their goal beliefs. Groups placed greatest emphasis on satisfying mastery needs, whereas satisfying social and performance-goal needs were judged as less important. But the effects were greatest for the Anglo and immigrant-background Australian students; Aboriginal Australian students were less likely to believe that their success depended on satisfying mastery and performance-goal needs. The Aboriginal Australian group was more socially oriented and less individually oriented than the other groups. These findings can be interpreted in light of cultural knowledge. Many Australian Aboriginal students come from families that emphasize traditional values (e.g., affiliation, social concern). Thus, it is not surprising that these students might place greater importance on that goal. The implications for education are that for these students (and for others who might respond similarly) more activities should be incorporated into instruction that include social links (e.g., cooperative learning). This does not mean that the emphasis on mastery should be abandoned; McInerney et al. (1998) found that all groups espoused a mastery goal orientation. Rather, the two goal orientations can be linked in creative ways. Another study on cross-cultural differences was conducted by Kinlaw, Kurtz-Costes, and Goldman-Fraser (2001), who compared the attribution (Chapter 9) beliefs of European- and Chinese-American mothers of preschool children. These researchers found that Chinese-American mothers placed greater emphasis on effort-related beliefs. On a test of school readiness, Chinese-American children scored higher on readiness and autonomy. Although these data are correlational and thus do not imply causality, the results show that cultural differences in mothers’ attribution beliefs are present before children enter school and suggest that when children get to school their attribution beliefs may relate to their motivation and learning. Future research on cultures will contribute greatly to the learning literature. It is imperative that principles of learning be tested for cross-cultural consistency to promote a better understanding of learning in all contexts and cultures (McInerney, 2008). We must remember that there is much variability within any culture (Zusho & Clayton, 2011). Further, it is well known that cultures change—often in the course of one’s lifetime—as a result of societal elements becoming incorporated into cultural norms (Gauvain & Munroe, 2012). Thus, it may be misleading to compare learners from different cultures and discuss between-culture differences. Unfortunately this often is done, as there is much research comparing Eastern and Western cultures. Even if between-group differences are obtained, it is difficult to determine the causes of the differences. To better understand learning differences among learners in different cultures and within the same culture, researchers may need to focus more on contextual variations including those found in the experiences, settings, and ecological conditions of individuals (Zusho & Clayton, 2011). INSTRUCTIONAL APPLICATIONS Learning theories and principles suggest many ways to take contextual variables into account in instruction. Instructional applications derive from theory and research on teacher–student interactions, learning styles, and parental and familial involvement. Teacher–Student Interactions Teacher–student interactions are critically important for effective teaching and learning. Interactions will vary with students’ developmental levels. Young children’s attention can be captured by novel, interesting displays while minimizing unnecessary distractions. It helps to provide opportunities for physical movement and to keep activities short to maintain children’s concentration. Young students also benefit from physical objects and visual displays (e.g., manipulatives, pictures). Teachers may need to point out how the knowledge students are learning relates to what they already know. Children should be encouraged to use outlines and pictures to help them organize information. The opening vignette suggests that making learning meaningful, such as by relating it to real-life experiences, helps to build children’s memory networks. Other important aspects of interactions involve feedback and classroom climate. Feedback. Rosenshine and Stevens (1986) recommended that teachers provide performance feedback (e.g., “Correct,” “Good”) and maintain lesson momentum when students make mistakes by giving corrective feedback but not completely re-explaining the process. Reteaching is called for when many students do not understand the material. When leading lessons, teachers should keep interactions with younger students brief (30 or fewer seconds) when such interactions are geared to leading them toward the correct answer with hints or simple questions. Longer contacts lose other students’ attention. Reteaching and leading students to correct answers are effective ways to promote learning (Rosenshine & Stevens, 1986). Asking simpler questions and giving hints are useful when contacts can be kept short. Reteaching is helpful when many students make errors during a lesson. Feedback informing students that answers are correct motivates because it indicates the students are becoming more competent and are capable of further learning (Schunk, 1995). Feedback indicating an error also can build self-efficacy if followed by corrective information on how to perform better. Younger students benefit from frequent feedback. Similarly, other interactions involving rewards, goals, contracts, and so forth must be linked with student progress. For example, rewards linked to learning progress build self-efficacy (Schunk, 1983e). With children, progress is best indicated with short-term tasks. Rewards given merely for participation regardless of level of performance actually may convey negative efficacy information. Students may wonder whether they are capable of performing better. Classroom Climate. As discussed earlier, teachers help to establish a climate that affects interactions. Emotional aspects of teacher–student interactions are important for children. A positive classroom climate that reflects teacher warmth and sensitivity is associated with higher achievement and better self-regulation among elementary students (Pianta, Belsky, Vandergrift, Houts, & Morrison, 2008). A classic study by Lewin, Lippitt, and White (1939) showed that a democratic (collaborative) leadership style is effective. The teacher works cooperatively with students, motivating them to work on tasks, posing questions, and having them share their ideas. Although an authoritarian style (strict with rigid rules and procedures) can raise achievement, high anxiety levels characterize such classrooms, and productivity drops off when the teacher is absent. A laissez-faire style with the teacher providing little classroom direction results in wasted time and aimless activities. Democratic leadership encourages independence and initiative in students, who continue to work productively in the teacher’s absence. Teacher–student interactions often include praise and criticism. Praise goes beyond simple feedback on accuracy of work or appropriateness of behavior because it conveys positive teacher affect and provides information about the worth of students’ behaviors (Brophy, 1981). Thus, a teacher who says, “Correct, your work is so good,” is providing both performance feedback (“Correct”) and praise (“Your work is so good”). Brophy (1981) reviewed research on teacher praise and found that it does not always reinforce desirable behavior (Chapter 3) because teachers often do not give it based on student responses. Rather, it may be infrequent, noncontingent, general, and highly dependent on teachers’ perceptions of students’ need for praise. Many studies also show that praise is not strongly related to student achievement. The effects of praise may depend on SES and ability level. In the early elementary grades, praise correlates weakly but positively with achievement among low-SES and low-ability students but weakly and negatively or not at all with achievement among high-SES and high-ability students (Brophy, 1981). After the first few grades in school, praise is a weak reinforcer. Up to approximately age 8, children want to please adults, which makes praise effects powerful; but this desire to please weakens with development. Praise also can have unintended effects. Because it conveys information about teachers’ beliefs, teachers who praise students for success may convey that they do not expect students to learn much. Students might believe that the teacher thinks they have low ability, and this negatively affects motivation and learning (Weiner, Graham, Taylor, & Meyer, 1983). When linked to progress in learning, praise substantiates students’ beliefs that they are becoming more competent and raises self-efficacy and motivation for learning. Praise used indiscriminantly carries no information about capabilities and has little effect on behavior (Schunk & Pajares, 2009). Criticism provides information about undesirability of student behaviors. Criticism (“I’m disappointed in you”) is distinguished from performance feedback (“That’s wrong”). Criticism is not necessarily bad. We might expect that criticism’s effect on achievement will depend on the extent to which it conveys that students are competent and can perform better with more effort or better use of strategies. Thus, a statement such as, “I’m disappointed in you. I know you can do better if you work harder” might motivate students to learn because it contains positive self-efficacy information. As with praise, other variables temper the effects of criticism. Some research shows that criticism is given more often to boys, African American students, students for whom teachers hold low expectations, and students of lower SES status (Brophy & Good, 1974). As a motivational technique to aid learning, criticism probably is not a good choice because it can have variable effects. Younger children may misinterpret academic criticism to mean that the teacher does not like them or is mean. Some students respond well to criticism. In general, however, teachers are better advised to provide positive feedback about ways to improve performance than to criticize present performance. Application 11.7 offers ways to use praise and criticism in learning settings. APPLICATION 11.7 Using Praise and Criticism The praise and criticism teachers use as they interact with their students can affect student performance. Teachers must be careful to use both appropriately and remember that criticism generally is not a good choice because it can have variable effects. Praise is most effective when it is simple, direct, and linked with accomplishment of specific actions. For example, a teacher who is complimenting a student for sitting quietly, concentrating, and completing his or her work accurately that day should not say, “You really have been good today” (too general). Instead, the teacher might say something such as, “I really like the way you worked hard at your seat and finished all of your math work today. It paid off because you got all of the division problems correct. Great job!” When a student answers a question in a class during a discussion about a chapter, it is desirable that the teacher let him or her know why the answer was a good one. Instead of simply replying, “Good answer,” the teacher might add, “You outlined very well the main points in that chapter.” If criticism is used, it should convey that students are competent and can perform better, which may motivate performance. For example, assume that a capable undergraduate student submitted a poor project that did not fulfill the assignment. The professor might say to the student, “John, I am disappointed in your project. You are one of the best students in our class. You always share a great deal in class and perform well on all the tests. I know you are capable of completing an outstanding project. I want you to work some more on this and try harder as you redo this project.” Learning Styles Taking learning style differences into account helps make instruction more developmentally appropriate. Learning styles (also known as cognitive styles or intellectual styles) are stable individual variations in perceiving, organizing, processing, and remembering information (Shipman & Shipman, 1985). Styles are people’s preferred ways to process information and handle tasks (Sternberg & Grigorenko, 1997; Zhang & Sternberg, 2005). Although some have questioned whether learning styles really exist (Riener & Willingham, 2010), it is important to note that styles are not synonymous with abilities. Abilities refer to capacities to learn and execute skills; styles are habitual ways of processing and using information. Styles are inferred from one’s preferred ways of organizing and processing information on different tasks (Dunn & Honigsfeld, 2013). To the extent that styles affect cognition, affects, and behavior, they help link cognitive, affective, and social functioning (Messick, 1994). In turn, stylistic differences are associated with differences in learning and receptivity to various forms of instruction (Messick, 1984). This section discusses three styles (field dependence–independence, categorization, cognitive tempo) that have substantial research bases and educational implications. There are many other styles including leveling or sharpening (blurring or accentuating differences among stimuli), risk taking or cautiousness(high or low willingness to take chances to achieve goals), and sensory modality preference (enactive or kinesthetic, iconic or visual, symbolic or auditory; Sternberg & Grigorenko, 1997). A popular style inventory is the Myers-Briggs Type Indicator (Myers & McCaulley, 1988), which purports to identify individuals’ preferred ways of seeking learning environments and attending to elements in them. Its four dimensions are extroversion–introversion, sensing–intuitive, thinking–feeling, and judging–perceiving. Readers are referred to Zhang and Sternberg (2005) for in-depth descriptions of other styles. Styles provide important information about cognitive development. One also can relate styles to larger behavioral patterns to study personality development (e.g., Myers-Briggs). Educators investigate styles to determine ways to provide developmentally appropriate instruction and to teach students more adaptive styles to enhance learning and motivation. Styles also are relevant to brain development and functions (Chapter 2). Field Dependence–Independence. Field dependence–independence (also called psychological differentiation, global and analytical functioning) refers to the extent that one depends on or is distracted by the context or perceptual field in which a stimulus or event occurs (Sternberg & Grigorenko, 1997). The construct was identified and researched by Witkin (1969; Witkin, Moore, Goodenough, & Cox, 1977). Various measures determine reliance on perceptual context. One is the Rod-and-Frame test, in which the individual attempts to align a tilted luminous rod in an upright position within a tilted luminous frame inside a dark room with no other perceptual cues. Field independence originally was defined as aligning the rod upright using only an internal standard of upright. Other measures are the Embedded Figures test, in which one attempts to locate a simpler figure embedded within a more complex design, and the Body Adjustment test, in which the individual sits in a tilted chair in a tilted room and attempts to align the chair upright. Participants who can easily locate figures and align themselves upright are classified as field independent (Application 11.8). Young children primarily are field dependent, but an increase in field independence begins during preschool and extends into adolescence. Children’s individual preferences remain reasonably consistent over time. The data are less clear on gender differences. Although some data suggest that older male students are more field independent than older female students, research on children shows that girls are more field independent than boys. Whether these differences reflect cognitive style or some other variable that contributes to test performance (e.g., activity–passivity) is not clear. APPLICATION 11.8 Learning Styles To ensure that instruction is developmentally appropriate, elementary teachers should address the cognitive differences of their children in designing classroom activities, particularly because young children are more field dependent (global) than field independent (analytical). For early elementary children, emphasis should be placed on designing activities that address global understanding, while at the same time taking analytical thinking into account. For example, when Ms. Banner implements a unit on the neighborhood, she and her third graders might initially talk about the entire neighborhood and all the people and places in it (global thinking). The children might build replicas of their homes, the school, churches, stores, and so forth—which could tap analytical thinking—and place these on a large floor map to get an overall picture of the neighborhood (global). Children could think about people in the neighborhood and their major features (analytical thinking) and then put on a puppet show portraying them interacting with one another without being too precise about exact behaviors (global). Ms. Banner could show a real city map to provide a broad overview (global) and then focus on that section of the map detailing their neighborhood (analytical). Secondary teachers also can take style differences into account in instructional planning. In teaching about World War II, Mr. Teague should emphasize both global and analytical styles by discussing overall themes and underlying causes of the war and by creating lists of important events and characters. Student activities can include discussions of important issues underlying the war (global style) and making time lines showing dates of important battles and other activities (analytical style). If Mr. Teague were to stress only one type of style, students who process and construct knowledge differently may doubt their ability to understand the material, which will have a negative impact on their self-efficacy and motivation for learning. Because field dependent persons may be more sensitive to and attend carefully to aspects of the social environment, they are better at learning material with social content; however, field independent learners can easily learn such content when it is brought to their attention. Field dependent learners seem sensitive to teacher praise and criticism. Field independent persons are more likely to impose structure when material lacks organization; field dependent learners consider material as it is. With poorly structured material, field dependent learners may be at a disadvantage. They use salient features of situations in learning, whereas field independent learners also consider less salient cues. The latter students may be at an advantage with concept learning when relevant and irrelevant attributes are contrasted. These differences suggest ways for teachers to alter instruction to make it developmentally appropriate. If field dependent learners miss cues, teachers should highlight them to help students distinguish relevant features of concepts. This may be especially important with children who are beginning readers as they focus on letter features. Field dependent learners may have more trouble during early stages of reading. Categorization Style. Categorization style refers to criteria used to perceive objects as similar to one another (Sigel & Brodzinsky, 1977). Style is assessed with a grouping task in which one must group objects on the basis of perceived similarity. This is not a cut-and-dried task because objects can be categorized in many ways. From a collection of animal pictures, one might select a cat, dog, and rabbit and give as the reason for the grouping that they are mammals, have fur, run, and so forth. Categorization style reveals information about how the individual prefers to organize information. Three types of categorization styles are relational, descriptive, and categorical (Kagan, Moss, & Sigel, 1960). A relational (contextual) style links items on a theme or function (e.g., spatial, temporal); a descriptive (analytic) style involves grouping by similarity according to some detail or physical attribute; a categorical (inferential) style classifies objects as instances of a superordinate concept. In the preceding example, “mammals,” “fur,” and “run,” reflect categorical, descriptive, and relational styles, respectively. Preschoolers’ categorizations tend to be descriptive; however, relational responses of the thematic type also are prevalent (Sigel & Brodzinsky, 1977). Researchers note a developmental trend toward greater use of descriptive and categorical classifications along with a decrease in relational responses. Style and academic achievement are related, but the causal direction is unclear (Shipman & Shipman, 1985). Reading, for example, requires perception of analytic relations (e.g., fine discriminations); however, the types of discriminations made are as important as the ability to make such discriminations. Students are taught the former. Style and achievement may influence each other. Certain styles may lead to higher achievement, and the resulting rewards, perceptions of progress, and self-efficacy may reinforce one’s continued use of the style. Cognitive Tempo. Cognitive (conceptual, response) tempo was extensively investigated by Kagan (1966). Kagan was investigating categorization when he observed that some children responded rapidly whereas others were more thoughtful and took their time. Cognitive tempo refers to the willingness “to pause and reflect upon the accuracy of hypotheses and solutions in a situation of response uncertainty” (Shipman & Shipman, 1985, p. 251). Kagan developed the Matching Familiar Figures (MFF) test to use with children. The MFF is a 12-item match-to-standard test in which a standard figure is shown with six possible matches, one of which is perfect. The dependent variables are time to the first response on each item and total errors across all items. Reflective children score above the median on time (longer) but below the median on errors (fewer), whereas impulsive children show the opposite pattern. Two other groups of children are fast-accurate (below the median on both measures) and slow-inaccurate (above the median on both measures). Children become more reflective with development, particularly in the early school years (Sigel & Brodzinsky, 1977). Evidence suggests different rates of development for boys and girls, with girls showing greater reflectivity at an earlier age. A moderate positive correlation between scores over a 2-year period indicates reasonable stability (Brodzinsky, 1982; Messer, 1970). Differences in tempo are unrelated to intelligence scores but correlate with school achievement. Messer (1970) found that children not promoted to the next grade were more impulsive than peers who were promoted. Reflective children tend to perform better on moderately difficult perceptual and conceptual problem-solving tasks and make mature judgments on concept attainment and analogical reasoning tasks (Shipman & Shipman, 1985). Reflectivity bears a positive relationship to prose reading, serial recall, and spatial perspective-taking (Sigel & Brodzinsky, 1977). Impulsive children often are less attentive and more disruptive than reflective children, oriented toward quick success, and demonstrate low performance standards and mastery motivation (Sternberg & Grigorenko, 1997). Given the educational relevance of cognitive tempo, many have suggested training children to be less impulsive. Meichenbaum and Goodman (1971; Chapter 4) found that self-instructional training decreased errors among impulsive children. Modeled demonstrations of reflective cognitive style, combined with student practice and feedback, seem important as a means of change. Cognitive styles seem important for teaching and learning, and a fair amount of developmental research exists that may help guide attempts by practitioners to apply findings to make instruction more developmentally appropriate. For example, learners with a visual-spatial style are better able to process and learn from graphical displays (Vekiri, 2002). At the same time, drawing instructional conclusions from the literature can be difficult. The distinction between cognitive styles and abilities is tenuous and controversial (Tiedemann, 1989); field independence may be synonymous with aspects of intelligence (Sternberg & Grigorenko, 1997). Researchers have investigated the organization of styles within information processing frameworks and within the structure of human personality (Messick, 1994; Sternberg & Grigorenko, 1997; Zhang & Sternberg, 2005). Ideally the conditions of instruction will match learners’ styles; however, this match often does not occur. Learners may need to adapt their styles and preferred modes of working to instructional conditions involving content and teaching methods. Self-regulation methods (Chapter 10) help learners adapt to changing instructional conditions. Instructional conditions can be tailored to individual differences to provide equal learning opportunities for all students despite differences in aptitudes, styles, and so forth (Snow, Corno, & Jackson, 1996). Teachers control many aspects of the instructional environment, which they can tailor to student differences. These aspects include organizational structure (whole-class, small-group, individual), regular and supplementary materials, use of technology, type of feedback, and type of material presented (tactile, auditory, visual). Teachers also make adaptations when they provide remedial instruction to students who have difficulty grasping new material. Parental and Familial Involvement As we have seen in this chapter, parents and other family members can affect their children’s learning directly through such activities as helping them with homework and studying and offering advice. But they also can affect children’s learning indirectly; for example, by steering them to desirable activities and people. Some instructional applications are given in this section. One application is for parents and others to encourage children to take part in activities in which most participants will display positive achievement beliefs (e.g., self-efficacy) and behaviors (e.g., studying), such as school clubs, athletic teams, and musical groups. For students to remain involved in activities they must maintain good grades, which can help students develop time management and study skills. Parents and other family members cannot control who will be their children’s friends, but they can help steer children into groups where the participants value learning and achieving. Parents can assist children with course planning. Especially with high school students, parents can discuss courses and electives and encourage children to talk with school counselors. Learning is aided because such course counseling helps ensure that children end up in the proper courses: courses that are neither too easy nor too difficult. Children who have a sense of self-efficacy for learning will be motivated to learn in the courses. Children’s learning will benefit when parents and family members help them determine their work requirements and schedule the appropriate amount of time to complete them. Goal-setting research underscores the importance of setting realistic goals (Locke & Latham, 2002). Parents can establish a routine; for example, before dinner children write down what they need to accomplish that evening, then set a rough schedule for completing the work. Children may be unrealistic about how much time is required; parents can help them set realistic time limits. Children can check off tasks as they complete them. The perception of progress helps build their self-efficacy for learning. Parental participation in school activities is important. Parents who do this convey the attitude that school is important and that they are willing to spend some of their time engaged in these activities. The belief that activities are valued is a key motivational variable, and higher motivation leads to better learning (Schunk & Pajares, 2009). As children get older they may not want parents too overtly involved in the school, but there are many forms of involvement that do not draw attention to parents’ presence (e.g., attend PTA meetings, school performances, athletic events). Media effects on learning are beneficial when activities are limited and of the educational variety. It also helps for parents to coview televised programs. Through coviewing, parents can discuss the program with children and ask them questions to help stimulate their learning (e.g., “Why do you think she did that?”). Coviewing also conveys the attitude that programs are beneficial and that parents think learning is important because they are spending time doing it. By checking channel listings, parents might identify some programs that sound educationally important and plan time to watch the programs with their children. Parental and familial involvement is important during periods when children are not in school, such as summer vacations. Children can lose learning gains and motivation for learning during these times, especially when there are many activities competing for their time. It is desirable for parents to talk with children about ways they might keep up some academic learning. Setting a few goals (e.g., number of books to read) can help sustain children’s academic focus and motivation for learning. SUMMARY The context of learning, or the community or learning environment within which the student is located, can have important effects on student learning. Context comprises many aspects such as teachers, classrooms, schools, peers, families, communities, and cultures. Teachers are responsible for creating effective learning environments where teaching and learning can proceed well. Good classroom organization and management practices enhance the effectiveness of learning environments. Also important are the implementation of the six TARGET variables: task, authority, recognition, grouping, evaluation, and time. Another critical aspect of classrooms is how teachers and students interact. To facilitate positive interactions and student motivation and learning, teachers should provide feedback indicating student progress and ways to improve, support students’ learning, and hold reasonable expectations based on the idea that all students are capable of learning. Peers can have important effects on students’ achievement beliefs, motivation, and learning. Peers exert their influence through modeling with its functions of inhibition/disinhibition, response facilitation, and observational learning. Similarity to models enhances their influence. Peers’ similarity in background and experiences can affect observers’ motivation and learning. Peer networks, or large groups of peers with whom students associate, are composed of students similar to one another in many ways. Networks help define opportunities for social interactions, allow students to observe others’ interactions, and provide access to activities. Members of networks tend to become more similar over time. Parents may try to direct their children into activities where the members hold similar beliefs about the importance of learning. Family influences on learning include socioeconomic status (SES), home environment, parental involvement, and electronic media. SES relates to school socialization, attendance, and years of schooling. Higher SES families have greater capital and provide more and richer opportunities for children. Early interventions for low-SES families help prepare children for school. Home environment effects are most pronounced in infancy and early childhood. As children become older, their social networks expand and peers become more important. Parents can launch children onto trajectories by involving them in groups and activities. Parents’ expectations for children relate positively to their achievement. Comer’s School Development Program involves parents and community members in school planning. Children learn from electronic media, and moderate exposure to educational media is associated with better cognitive development and achievement. Parents and caregivers who view media with children can help to promote children’s learning. Families are critical for children’s motivation and learning. Children benefit from authoritative parenting practices that provide guidance and limits while assisting children to regulate and assume responsibility for their behaviors. Family involvement in children’s education is important. Homes that are rich in resources and in which parents assist children with educational activities bear a positive relation to student learning and achievement. Community influences include location and involvement. Learning benefits when students’ communities have access to resources and stimulating educational experiences. Community members often are involved in education by participating in school events and field trips in the community. It is important that schools partner with community agencies to enhance involvement. Cultural differences often are found in learning and achievement. The attitudes, beliefs, and practices of cultures must be examined to determine causes of differences. There often are wide differences within cultures, so generalizations about cross-cultural differences may be misleading. When reliable cultural differences are found, students may benefit from programs aimed at enhancing their learning potential. Some important instructional applications pertain to the areas of teacher–student interactions, learning styles, and parental and familial involvement. Teachers who structure feedback and provide a positive classroom climate—which includes effectively using praise and criticism—help motivate students and improve their learning. Students differ in their preferred learning styles. Teachers can take stylistic differences into account by ensuring that information is conveyed in multiple ways and that student activities are varied. Parents can be involved in children’s schooling in and out of school by participating in school activities, ensuring that children complete work, assisting with planning, and monitoring media usage to help ensure that it facilitates academic learning. The Transfer Process for Learning Multimedia: BBC Worldwide (1996). 20 Steps to Better Management, 1, Making the Most of Yourself (20 mins) [Full video], Academic Video Online. Intelecom (2006). The Learning Machine (29 mins) [Full video], Academic Video Online. Uniview Worldwide (1996). Cognitive Psychology (04:48) [Video file] in Further Approaches to Learning, Films on Demand. TVF International (2001). Criminal Profiling Research Project (05:19) [Video file] in To Catch a Killer: The Use and Abuse of Criminal Profiling, Films on Demand. Uniview Worldwide (1996). Learned Helplessness (03:18) [Video file] in Further Approaches to Learning, Films on Demand. Uniview Worldwide (1996). Learning Sets (06:10) [Video file] in Further Approaches to Learning, Films on Demand. Uniview Worldwide (1996). Mental Processes (03:28) [Video file] in Further Approaches to Learning, Films on Demand. Uniview Worldwide (1996). Neuroscience (01:55) [Video file] in Further Approaches to Learning, Films on Demand. Copyright © 2016, 2012 by University of Phoenix. All rights reserved.