Cognitive Development and Child Education Research Paper

This research paper first presents a brief historical overview and summary of the assumptions underlying psycho-logical and especially cognitive developmental theories and research as they pertain to education, and then considers a set of current issues that highlight the link between the two and the potential for dynamic interaction. ‘Education’ is clearly a broad term that can encompass learning throughout the lifespan. In this research paper the central focus will be on education defined as formal instruction during the school years.

1. Historical Overview

Historically, the impact of understanding of cognitive development on educational practice can be traced to the influences of psychology in general. The great movements of functionalism and behaviorism that shaped psychology in the first half of the twentieth century also affected models of education and educational practice. The subsequent ‘cognitive revolution’ affected education in a variety of ways. Theory and research on cognitive skill development (e.g., strategy acquisition and use) and on conceptual structure (e.g., concept organization and change; logic mathematical reasoning) led to a concern with facilitating reasoning and problem-solving strategies, and with facilitating concept acquisition and retrieval, especially through active, hands-on problem solving.

Although educational practice has been influenced at the most general level by ideas about cognition and cognitive development, a dynamic interplay between cognitive developmental research and educational practice has been neither widespread nor systematic in the typical classroom. This is not surprising, as noted by Olson and Bruner (1996, p. 9): ‘Theoretical knowledge of how cognition develops continues to grow but just how to relate this knowledge to the practical contexts … to educate … remains almost as mysterious as when such efforts first began.’ This is beginning to change as new tools emerge. Over the past decades, cognitive science research has produced detailed analyses of tasks and learners that allow more sophisticated models of the knowledge that a learner brings to the educational setting, and of the component skills necessary for successful performance within specified domains such as mathematics and physics. In addition, there are powerful models of early and late conceptual skill development, and of reasoning processes that emerge within and outside of formal education. These models carry the promise of better specification of the design and implementation processes for successful learning environments.

1.1 Specific Influences

The influence of cognitive developmental theory and research on education has changed as models of cognition, learning, and education have altered. During the heyday of behaviorism, especially in the USA, the explanatory mechanisms underlying both development and education were based on elementary principles of associative learning; educational strategies were based on behavior analysis, association, and reinforcement contingencies; and educational outcome was assessed in quantitative terms. At the beginning of the twenty-first century, although general learning principles inform educational practice (e.g., the relative value of internal vs. external reinforcement, the effectiveness of spaced vs. massed trials, learning and forgetting curves, curricula organized as an accumulation of simple units) a strict learning model approach has largely been restricted to behavioral modification programs and some programmed instruction, most notably for foreign language learning.

The most general result of the cognitive revolution was a new focus on the active, problem-solving child and classroom. Although an oversimplification, two influences were particularly important. One influence is from the broad class of structuralist models, most notably Jean Piaget’s genetic epistemology. Most of these models have been based on the assumption that the child is an active participant in the acquisition and/organization of knowledge—that is, that there is a dynamic interplay between the knower’s current level of understanding and the information and problems presented by the environment. This perspective led to a focus on understanding the child’s view of events, on describing the nature and/organization of concepts and reasoning structures, and on matching instruction to developmental level. A second influence, arising from the broad array of information-processing models of cognitive functioning, led to a focus on specifying task structure and problem solving strategies, and on the information processes underlying memory, reasoning, and learning.

2. Cognitive Development

Examples of the specific influences of cognitive development theories and research can be captured by answers to the questions: who is the learner, what is being learned, and how does learning proceed?

2.1 Who Is The Learner?

Probably one of the most revolutionary ideas of scholarly study and research on childhood and education undertaken in the twentieth century was that neither education nor cognitive development can be understood without taking account of the active child learner. Although this view informed ideas about education from an early point (cf. White 1992), it was strengthened by widespread interest in the theories of Jean Piaget and their adaptation to educational issues, especially mathematics and science education. The central idea is that the child actively assimilates new information on the basis of general reasoning structures, which themselves change in regular ways over the course of development. Recent research on the cognitive abilities of the very young child and infant have complemented this idea to suggest that knowledge and knowledge acquisition in some domains (such as language, arithmetic, and the elementary natural and social worlds) is precocious. One conclusion from this work is that the child brings to the formal educational context a set of robust ideas (or ‘theories’) about the physical, social, and natural worlds.

The goal of information-processing cognitive science models is to develop descriptions of learners, tasks, processes, and performance that will allow a detailed specification of the componential skills and processes underlying cognitive activities. From this more information-processing perspective, the learner is seen as a consumer who gathers, stores, organizes, and uses information in problem solving, and who, with development and instruction, becomes increasingly adept at directing, monitoring, and manipulating these skills strategically across a variety of content areas.

2.2 What Is Being Learned?

There is of course no single ‘cognitive developmental’ answer to the question of what is learned, but rather a family of answers that have in common a focus on knowledge and mental representation as the products of experience and/or education.

The gist of most structuralist viewpoints is that cognitive development consists of qualitative changes in the ways that concepts (i.e., knowledge about the mathematical, material, and social worlds) are organized. These changes enable the child to reason in increasingly complex ways. The description of conceptual structure varies: according to Piagetian models, conceptual structure is described in terms of general and universal logical-mathematical relations; other models refer to causal or semantic structures that may vary across content domains.

The answer to ‘what is learned’ from an information-processing perspective on cognitive development is skill-based. A large number of cognitive developmental studies have documented changes in the speed and control of processing skills (perception, attention, and memory), and associated increases in cognitive performance, as well as changes in conceptual organization and rule use. Catalyzed by a seminal paper by Flavell (1979) on metacognition and cognitive monitoring, cognitive developmental re-search focused on the acquisition of cognitive processing strategies, and metacognitive skills and knowledge, as important mechanisms of developmental change in cognitive performance. Teaching these skills to improve general performance across content area (e.g., self-monitoring, strategy retrieval and use) has in-formed research and practice, as has teaching these skills within a variety of content areas, including reading, writing, mathematics, and science.

2.3 How Does Learning Proceed?: Implications For Education

The central tenet of constructivist models is that cognitive change learning is a reciprocal process of interpreting information according to current conceptual structure and adapting that structure to task demands. Learning does not proceed by recording received information directly or passively. Learning proceeds at best according to constructivist models, when the learner can experiment actively to discover, invent or infer the solutions to problems. This approach is reflected in active, hands-on classrooms, where a host of strategies attempt to foster active engagement: the discovery method, activity centers, and inquiry-based instruction that focuses on acquiring principles, not just facts (e.g., Hedegaard and Lompscher 1999).

From a cognitive science information-processing perspective, learning proceeds by acquiring declarative and procedural knowledge within content domains, domain-specific and domain-general rules for operating with that content, strategies and skills for interpreting and attending to new information, and an ability to monitor, access, and control these skills intentionally, including learning how to learn (cf. Bruer 1993, Bransford et al. 1999).

3. Contemporary Issues For Cognitive Development And Education

The following section discusses four complementary areas in cognitive developmental research that have a strong potential to affect educational models and practice: (a) the metaphor of the child as ‘universal novice’, (b) conceptual change models—the role of naive theories and ‘misconceptions’, (c) individual differences, (d) cognitive development and education as social phenomena.

3.1 The Development Of No Ice-To-Expert

The discovery that experts and novices, regardless of age, differ with respect not only to the sheer amount of knowledge they possess, but also with respect to the organization of that knowledge, offers a metaphor for the processes of education in which universal novices become educated experts. Many studies have demonstrated that prior knowledge predicts learning out-comes, and that the knowledge base of novices in any particular domain differs from that of experts in systematic ways (Bransford et al. 1999). This approach has the potential to make a powerful impact on educational practice because it suggests that the acquisition of domain-specific information, not just domain-general skills, is necessary for deep knowledge acquisition. Expert content knowledge can compensate for other skills that predict performance, such as age, aptitude or metacognitive skills. The expert– novice distinction and attempts to design curricula to move novices to become experts has become a catch-word of general education in math and science, and in specialized curricula such as medicine.

3.2 Intuitive Knowledge And Misconceptions

Studies of reasoning, concept formation, and conceptual understanding in young children have demonstrated convincingly that there is probably no period during development when the child is a ‘blank slate.’ Rather, even young children show rich knowledge about a variety of concept domains, including number, biological kinds, physics, social phenomena, and the like. Although this knowledge can provide a strong initial base for formal instruction, some early knowledge may clash with information presented in formal learning contexts for mathematical, literary, and scientific domains, because the concepts to be learned in these domains do not match everyday experience. For example, children’s difficulties in performing mathematical operations on fractions or negative numbers arise in part from inappropriately generalizing knowledge about natural, whole numbers (Gelman 1994). Similarly, informal knowledge about biological kinds, movement through space, physical causality, and even cosmology, can clash with learning about formal, complex systems in school (e.g., acceleration, force, gravity, and biology). Most clearly demonstrated in math and science, the study of ‘misconceptions’ has shown the power of children’s implicit and everyday concepts. It underscores the importance of basing instruction on a good diagnosis of current understanding, and of devising ways to promote conceptual change through active experimentation and confrontation in rich everyday con-texts. There is general agreement that ‘a logical extension of the view that new knowledge must be constructed from existing knowledge is that teachers need to pay attention to the incomplete understandings, the false beliefs, and the naive renditions of concepts that learners bring with them to a given subject’ (Bransford et al. 1999, p. 10).

3.3 Individual Differences

In its attempts to explain mental growth and change, mainstream cognitive developmental research has focused more on phenomena that are believed to characterize development universally, and less on individual differences. Nonetheless, a number of traditional and emerging areas suggest that systematic differences not only in cognitive style and cognitive strategies, but also in more basic conceptual structure, may provide a means to tailor educational practice more closely to children’s specific learning needs.

For example, well documented sex differences in spatial skills that may be tied to differences in representation mode or organization, not just to experiential differences, may be exploited to develop alternative methods of mathematics instruction. Analogously, descriptions of multiple forms of intelligence (cf. Gardner 1993) may offer to the teacher different approaches to a topic and different modes of presenting key concepts (Bransford et al. 1999).

Detailed studies tracing the processes of conceptual change, strategy acquisition and discovery, and the like, also illustrate the large range of individual differences in developmental rate, style, and pattern. Microgenetic studies, i.e., investigations that follow the emergence, development, and consolidation of cognitive skills at an intensive individual level over a period of time, have been applied to a variety of content domains such as language, mathematical skills and problem solving, scientific reasoning, and memory and concept development (cf. Siegler and Crowley 1991, Weinert and Schneider 1999). These studies have illustrated the large variability in skill learning and use, and have shown that average developmental functions do not characterize developmental change at the individual level. Insight into the conditions that facilitate problem solving and that help consolidate newly formed competencies may inform the development of more individualized learning assessment and curricula.

3.4 Cognition In Context

Researchers have recently returned to classic questions concerning the role of culture in cognition, the importance of context and motivation in explaining and understanding cognition in everyday contexts, and the influences of formal and informal learning contexts on cognitive development. Two phenomena have heightened such interest: national differences in cognitive performance, especially in mathematics and science; and research findings showing large discrepancies between cognitive performance in formal educational settings and informal everyday contexts. Both of these perspectives have motivated new re-search on the types and effects of formal and informal education and have amply illustrated the effects of schooling on a variety of cognitive tasks tapping mathematics, logic, classification, and memory strategies (cf. Rogoff and Chavajay 1995).

A cognitive model that has been highly influential in education is based on the theories of Lev Vygotsky. Vygotsky (1962) characterized cognition as the internalization of external and culturally transmitted structure, rules, and principles that are mediated by language. According to this model, development proceeds most effectively when there is adequate environmental support within the ‘zone of proximal development,’ a construct to indicate the difference between a child’s actual and potential performance. The zone of proximal development is usually measured as the difference between tasks a child can solve working independently, and those a child can solve with assistance from adults, instructors or other competent models. This approach underlies ‘reciprocal education’ and ‘reciprocal teaching’, in which the learner acquires strategies from expert models in social settings. The educational goal is to develop supporting social contexts in which a ‘community of learners’ collaborates in fostering learning outcomes (Brown and Campione 1994).

4. Conclusions

As noted above, mainstream cognitive developmental research and theory have influenced educational practice at only the most general levels. As the relatively new field of multidisciplinary cognitive science has become established and institutionalized, its methods and results are being tested in school contexts (cf. Bruer 1993, Bransford et al. 1999). The fields of cognitive development and education are ripe for forging collaborations that allow the science of learning to inform the practice of education in classroom contexts.

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