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Article Excerpt
In this article we attempt to appropriate relevant neuroscience research findings and draw possible implications to learning and instruction. In such an attempt, we also complement findings from the cognitive and learning sciences with relevancy from social-cultural perspectives of the mind. In essence, although we recognize that direct links or bridges from neuroscience to learning may still be difficult, we conjecture that current pedagogical approaches such as problem-based learning and case-based reasoning are congruent to neuroscience findings. These approaches have roots in theories such as situated cognition where context, problems, activities, emotions, and cognition are interwoven. Thus, the aim of this article is not primarily to draw new implications to educational practice from neuroscience research but rather to support current and recent pedagogical approaches.
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Neuroscience has provided fascinating glimpses into the human brain's development and function (Ratey, 2001) and educators could take advantage of these findings. For example, many educators have known intuitively that children's capacities develop in tandem, and the findings of neuroscience seem to support the educational researchers' pleas for integrated, contextual instruction in mathematics, reading, spelling, and science (e.g., Duffy & Cunningham, 1996). However, the bridge between neuroscience and education is still "unsteady" but the integrated disciplines of cognitive science, learning sciences, and other disciplines related to human functioning and behavior could provide us with possible frameworks for learning and instruction (Bruer, 1997). In other words, neuroscience has yet to make significant progress in its research findings before education can translate or transform these findings into policies into areas such as mathematics and science. Moreover, the present findings in neuroscience appear to suggest possible trends towards recent pedagogical approaches such as problem-based learning, situated learning, discovery learning, case-based instruction, and similar others (Goldberg, 2001; also see the working report of Blakemore & Frith, 2000). Thus, the aim of this article is not to draw new implications to educational practice from neuroscience research but rather to support the more current and recent pedagogical approaches as previously mentioned.
To reiterate, the objectives of this article are to: (a) synthesize some of the major neuroscience research findings and draw relevancy to learning processes; (b) discuss social-cultural factors influencing learning in the brain; (c) put in context some of the types of learning in terms of neuroscience research, and (d) discuss some of the recent pedagogical approaches such as problem-based learning with support from neuroscience findings. Before we begin to discuss neuroscience findings, we find it useful to describe the recent notions of the brain as a biological rather than as a mechanistic computer-like entity (inherited from the information processing paradigm of cognition).
THE BIOLOGICAL BRAIN
The mechanistic worldview and the information processing computer metaphor appear to have dominated our conception of the brain (Newell, 1990). Such a mechanistic worldview had resulted in many explanations of the brain being like a computer and that each part of the brain does a particular function. Predominately, brain imaging techniques (such as Magnetic Resonance Imaging [MRI], Positron Emission Tomography [PET], etc.) are also largely dependent on attempting to find which parts of the brain are activated in the event of certain actions (Squire & Kosslyn, 2000). To date, we could attempt to isolate certain brain areas more dedicated to particular operations, but we have to emphasize that these more focused areas are interconnected to neurons all over the brain (which are not necessarily picked up by brain imaging techniques). Current brain imaging techniques have advanced neuroscientists' work but many of the findings are yet subjected to different interpretations (Pinker, 1999). With all the imaging techniques trying to understand language use (for example), the answer should come from the local circuitry that actually does the computing. "Methods such as aphasiology and neuroimaging are a bit like using bomb craters and blurred satellite photos to understand the long-distance telephone networks" (Pinker, 1999, p. 127).
Edelman (1992) argued that the mechanistic worldview is an inappropriate model because the computer is preprogrammed and automated by an external force--in other words, a prior (see also Clancey, 1997). Furthermore, the powerful role that emotion plays in regulating brain activity, and the preponderance of parallel (rather than linear) processing in our brains, suggested to Edelman that a useful model for our brain must come out of biology, not technology (Sylwester, 1995). Edelman discovered that the immune system does not operate through a rule-based instruction through the memory model but rather through evolutionary natural-selection procedures. Edelman (1989, 1992) subsequently studied the brain to see if it also operates principally on natural selection, rather than on "programmed instruction." His controversial theory (Neuronal Darwinism) argued that our brains do operate on the basis of natural selection--or at least that natural selection is the process that explains learning. Edelman's theory currently appears to be the most completely developed biological brain theory (see Edelman, 1992).
Edelman's (1992) model suggested that the electrochemical dynamics of the brain's development and operation resembled the rich, layered ecology of a jungle environment metaphor. A jungle has no external developer or predetermined goals. It is a messy place characterized more by organic excess than by goal-directed objectives and efficiency. Such views are congruent with the notions of situated cognition where memory should be seen as a process-memory rather than as a storehouse (Clancey, 1997). Each recollection is a re-construction of the past memory, activity, or event rather than a retrieval (Clancey, 1997). The jungle environment does not instruct organisms how to behave in an ecologically appropriate manner, for example, by teaching trees how to position their branches and roots to get sunlight and soil nutrients (Sylwester, 1995). It is more a matter of natural selection. The vast network of highly interconnected neural networks could be seen as the neural equivalent of the complex set of jungle organisms that respond variously to environmental challenges. The natural selection processes that shape a jungle over periods of time also shape the human brain over time--the brain's neural networks. Just as each type of immune antibody responds to a specific environmental antigen, so each sensory network processes a specific element of the external world. Various interconnected combinations of these neural networks process complicated but related phenomena--from sounds to words, to actions, and so forth. Thus, we have an interconnected brain, in that a relatively small number of standard, nonthinking components combine their information to create an amazing complex cognitive structure (Edelman, 1989, 1992). Edelman's theories cannot be "proven," but they provide a conceptual basis that is worth considering in contrast to the computational and information processing world-view.
The theory of neural Darwinism (Edelman, 1989) argued that genetic processes that evolved over time created a generic human brain that is fully equipped at birth with the basic sensory and motor components a human needs to hardwire its basic survival networks (e.g., circulation, respiration, reflexes), but humans also need the flexibility of adaptable or "softwired" networks to be able to respond to specific environmental challenges (e.g., to learn a new language). Thus, one implication is that learning becomes a delicate but powerful dialogue between genetics and the environment (Bateson, 1979). The human brain is powerfully shaped by genetics, development, and experience--but it also then actively shapes the nature of our own experience and of the culture in which we live (Ratey, 2001). Stimulating experiences create complex reciprocal connections among neural structures. Instruction may be perceived more like facilitating the learners' interaction with appropriate environmental and social contexts and teachers. These notions are similar to the pedagogies advocated by situated cognitivists (Bredo, 1994). Situated cognition emphasized the contextual dimensions of knowledge where meanings are considered inseparable from its relations among situations and verbal or gestural actions (Bredo, 1994; Brown, Collins, & Duguid, 1989). In other words, meanings are perceived as inseparable from interpretation, and knowledge is linked to the relations of which it is a product (Clancey & Roschelle, 1991; Dewey, 1910/1981). According to Brown, Collins, and Duguid (1989), knowing, and not just learning is inextricably situated in the physical and social context of its acquisition and use. It cannot be extracted from these without being irretrievably transformed. In other words, one key notion of situated cognition is the emphasis of context including social relationships.
At the most general level, it would appear by implication that behavioral, cognitive, motivational, and emotional systems are designed by natural selection to achieve some level of control over the environment (Sylwester, 1995). There is a crucial distinction, however, between biologically primary and secondary cognitive abilities. It appears that children are prepared emotionally, motivationally, cognitively, and neurobiologically to acquire biologically primary competencies (van der Veer & Valsiner, 1994). Examples of such primary competencies are the acquisition of languages and forms of social cognition, and gauging the physical and biological environment. Learning develops more fully as children are exposed to their environment. Children are biologically motivated to seek out situations, often through play, that help develop their primary competencies such as language and social skills (Vygotsky, 1994). But much of what children learn in school is biologically secondary. Children do not appear to be compelled by biology to learn what they need to learn to function in a technologically complex society like ours. They are primed to acquire language and basic skills, but not to learn to read and solve complex arithmetic problems. Strong cultural support is needed to help children learn those secondary skills, for example, the process of scientific inquiry. These notions are congruent to Vygotsky's thoughts on the genetic laws of cultural development and higher order thinking (Vygotsky, 1981). Vygotsky posits that cognition is grounded on two planes--the biological and the social. The basic biological functions--which are genetically endowed...
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