Tag: prior knowledge

Hatano & Inagaki (1986)

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Hatano, G., & Inagaki, K. (1986). Two courses of expertise. In H.W. Stevenson, H. Azuma, & K. Hakuta (Eds.), Child development and education in Japan: A series of books in psychology (pp. 262-272). New York NY: W.H. Freeman.

Hatano and Inagaki’s chapter unpacks the processes of achieving two kinds of expertise: adaptive and routine. Both require the accumulation of experience and consist of using prior knowledge to assimilate/accommodate domain-specific problems under the supervision of more capable experts. Whereas routine experts demonstrate exceptional speed, accuracy, and automation of skills in particular contexts but lack the flexibility to extend their skills to new domains, adaptive experts use conceptual knowledge to invest procedures with meaning, make predictions about unfamiliar situations, and invent new strategies.

The authors begin by contrasting procedural knowledge with conceptual knowledge to understand how novices become adaptive experts. Procedural knowledge involves the rules, executive strategies, and skills that people learn through daily life; indeed, procedural knowledge can lead to effective behavior even without depth of understanding. In considering the development of expertise, Hatano and Inagaki here accept two Piagetian assumptions: that humans have an intrinsic motivation for understanding, and that knowledge is acquired through reflexive abstraction (reorganizing prior knowledge). Hatano and Inagaki argue that the dissatisfaction with procedural competence drives people’s desire to find meaning, explain why things work, judge the appropriateness of various options, and modify skills according to changes in the environment. [It is noteworthy that this drive to understand is not age-dependent: according to the authors, what distinguishes adults and children as learners is the depth of domain knowledge they have.] Although inquiry can precipitate the development of conceptual knowledge, individuals also need data (empirical knowledge) and models (preconceptual knowledge) to build a more complete picture.

In teasing out the distinction between adaptive and routine expertise, the authors acknowledge that routine expertise can lead to some “generalized consequences” like the development of related procedural skills (in the same domain), increased efficiency, and the production of new mental models which support the completion of tasks (physical abacus → mental abacus). They then identify three key factors that encourage individuals to engage in the kinds of experimentation that lead to adaptive expertise. First, randomness inherent to specific problem-spaces requires people to modify their approaches after observing variable outcomes. Second, stakes matter: people are more willing to engage in active and playful experimentation when the results are not rewarded or evaluated. Third, group-cultures that value experimentation over efficiency demonstrate increased levels of adaptive expertise. Hatano and Inagaki end here with an implicit critique of assessment culture in schools (inhibiting playful experimentation) and an explicit call to compare children from efficiency-oriented cultures (Japan) to understanding-oriented cultures regarding flexibility and adaptability.

Bransford, Brown, & Cocking (2000)

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Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). From speculation to science.In How People Learn: Brain, Mind, Experience, and School, Expanded Edition (pp. 3–27). Washington DC: National Academies Press.

This introductory chapter to Bransford, Brown, and Cocking’s second edition of their National Research Council report summarizes the impacts of the “revolution in the study of the mind” since the 1970s on theories of learning and highlights implications for educational practice in formal and informal settings across the lifespan. The authors’ key claim is that, by following teaching strategies aligned with the emerging science of learning, educators will make it possible for more students to develop a deeper understanding of traditional disciplines.  Driven by the growth of interdisciplinary inquiries, new collaborations between researchers and educators, and advances in the tools of cognitive science and related disciplines, a new theory of learning has emerged that challenges standard approaches to curriculum design, teaching, assessment, and policy.

The chapter begins with a short historiography of the learning sciences, from Wilhelm Wundt’s analysis of human consciousness through behaviorism (both radical and moderate) to the emergence of cognitive science in the 1950s. This new field has made it possible for scientists to test theories about thinking and learning through rigorous qualitative and experimental research methodologies with attention to the social and cultural contexts of learning. The authors then turn to some of the hallmarks of the new science of learning and their implications for teaching:

  • Understanding (juxtaposed with but not alienated from “facts”) is defined as “usable knowledge” that is connected to and organized around concepts, conditionalized to context, and transferable across disciplines. Students must have a solid base of factual information in order to build understanding; teachers must have a disciplinary expertise themselves,  a lived experience of “the process of coming to see” (John Armstrong) in that discipline, a commitment to depth over breadth, and assessments that are aligned with evidence of deep understanding.
  • The authors identify the significance of prior knowledge in the process of knowing, wherein goal-directed learners bring pre-existing knowledge, skills, beliefs, and concepts into new environments. These prior conditions determine how new information is perceived and processed by learners. Students bring both predictable and idiosyncratic preconceptions about the world to the classroom; teachers must actively inquire about students’ thinking, be aware of common preconceptions and, through formative assessment, make thinking visible.
  • Active learning is defined as the process of assessing understanding and acquiring additional information and skills. Metacognition, or the ability to predict performance and monitor current understanding, is supported by teaching practices that encourage sense-making, self-assessment, and reflection, leading to greater instances of transfer. Inquiry-based metacognition skills must be modeled, contextualized, and taught across disciplines.

Finally, the authors turn toward the implications of these findings for the design of classroom environments. These areas of cultivation include: making schools “learner-centered” (aware of preconceptions, sensitive to students’ cultural contexts and theories of intelligence), creating “knowledge-centered” classrooms that emphasize the what (information) along with the why (understanding) and the how-do-we-know-when-we-get-there (mastery), creating formative assessments that accurately monitor understanding, and taking a “community-centered” approach to norms and culture in order to cultivate excitement. They conclude with a nod toward adult learning, suggesting that teacher professional development programs could benefit from a redesign with learning sciences in mind.