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Cited by 1,394Science Education and PedagogyInnovative Teaching and Learning MethodsEducational Strategies and Epistemologies

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Inquiry, Modeling, and Metacognition: Making Science Accessible to All Students

White, Barbara Y., Frederiksen, John R.·1998·Cognition and Instruction
DOI 10.1207/s1532690xci1601_2
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Abstract

Our objective has been to develop an instructional theory and corresponding curricular materials that make scientific inquiry accessible to a wide range of students, including younger and lower achieving students. We hypothesized that this could be achieved by recognizing the importance of metacognition and creating an instructional approach that develops students' metacognitive knowledge and skills through a process of scaffolded inquiry, reflection, and generalization. Toward this end, we collaborated with teachers to create a computer enhanced, middle school science curriculum that engages students in learning about and reflecting on the processes of scientific inquiry as they construct increasingly complex models of force and motion phenomena. The resulting ThinkerTools Inquiry Curriculum centers around a metacognitive model of research, called the Inquiry Cycle, and a metacognitive process, called Reflective Assessment, in which students reflect on their own and each other's inquiry. In this article, we report on instructional trials of the curriculum by teachers in urban classrooms, including a controlled comparison to determine the impact of including or not including the Reflective Assessment Process. Overall, the curriculum proved successful and students' performance improved significantly on both physics and inquiry assessments. The controlled comparison revealed that students' learning was greatly facilitated by Reflective Assessment. Furthermore, adding this metac…

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  1. Brown, A., Bransford, J., Ferrara, R. & Campione, J. (1983). Learning, remembering, and understanding. In J. H. Flavell &E. M. Markman (Eds.), Handbookof childpsychology: Vol. 3. Cognitive development (4th ed., pp. 77-166). New York: Wiley.
  2. Bmer, J. (1993). ScPlools for thought: A science of learning in the classroom. Cambridge, MA: MIT Press.
  3. Campione, J. C. (1987). Metacognitive components of instructional research with problem learners. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 117-140). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
  4. Carey, S. (1985). Are children fundamentally different kinds of thinkers and learners than adults? In S. Chipman, J. Segal, & R. Glaser (Eds.), Thinking and learning skills: Vol. 2. Reasearch and open questions (pp. 485-517). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
  5. Chalmers, A. (1990). Science and its,fabrication. Minneapolis: University of Minnesota Press.
  6. Collins A., 1994, Educational Technology, 34, S32
  7. 10.1016/S0364-0213(82)80005-0 · DOI
  8. diSessa A, 1991, Journal of Mathematical Behavior, 10, 117
  9. 10.1037//0012-1649.14.3.268 · DOI
  10. Feuerstein, R., Jensen, M., Hoffman, M. &Rand, Y. (1985). Instrumental enrichment, an intervention program for structural cognitive modifiability: Theory and practice. In J. Segal, S. Chipman, & R. Glaser (Eds.), Thinking and learning skills: Vol. I. Relating instruction to research @p. 43-82). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
  11. 10.1037//0003-066X.34.10.906 · DOI · Paper page
  12. Frederiksen J., 1994, The Educator, 8, 2
  13. Gentner, D. &Stevens, A. (Eds.). (1983). Mental models. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc .
  14. Giere, R. (1991). Understanding scient@c reasoning (3rd ed.). Fort Worth, TX: Harcourt Brace Jovanovich.
  15. Ciunstone, R. (1990). Restructuring theory from practical experience. In B. Woolnough (Ed.), Practical science. Milton Keynes, England: Open University Press.
  16. Harms, N. & Yager, R. (Eds.). (1980). What research says to the science teacher (Vol. 3). Washington, DC: National Science Teachers Association (ERICDocument Reproduction Service No. ED205 367)
  17. Horwitz, P. (1989). Interactive simulations and their implications for science teaching. In The 1988 AETS Yearbook (pp. 59-70). Auburn, AL: Association for the Education of Teachers in Science.
  18. Kuhn T., 1964, Melanges Alexandre Koyre (Vol., 2, 307
  19. Kuhn, T. (1970). The structureif scientllfic revolutions (2nd ed.). Chicago: University of Chicago Press.
  20. Linn M., 1993, Merrill-Palmer Quarterly, 39, 47
  21. McCloskey, M. (1983). Naive theories of motion. In D. Gentner & A. L. Stevens (Eds.), Mental models @p. 299-324). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
  22. Metz K., 1995, Educational Researcher, 65, 93
  23. Miller M., 1991, Journal for the Education of the Gifed, 14, 178
  24. Minstrell, J. (1982). Conceptual development research in the natural setting of the classroom. In M. Budd Rowe (Ed.), Education in the 8O'sScience (pp. 129-143). Washington, DC: National Education Association.
  25. Minstrell, J. (1989). Teaching science for understanding. In L. Resnick & L. Klopfer (Eds.), Toward the thinking curriculum: Current cognitive research. Yearbook of the Association for Supervision and Curriculum Development (pp. 129-149). Alexandria, VA: Association for Supervision and Curriculum Development.
  26. IVersessian, N. (1992). How do scientists think? Capturingthe dynamics of conceptual change in science. In R. Giere (Ed.), Cognitive models of science (pp. 3-44). Minneapolis: University of Minnesota Press.
  27. Aikenhead G., 1992, Science Education, 76, 559, 10.1002/sce.3730760503 · DOI
  28. Scardarnalia, M., Bereiter, C. & Lamon, M. (1994). The CSlLE Project: Trying to bring the classroom into World 3. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and clmsroom practice (pp. 201-228). Cambridge, MA: MIT PressBradford Books.
  29. Schoenfeld, A. (1987). What's all the fuss about metacognition? In A. H. Schoenfeld (Ed.), Cognitive science and mathematics education (pp. 189-215). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
  30. Schomer M., 1993, Journal of Experimental Psychology, 85, 406
  31. Shute, V., Glaser, R. & Raghavan, K. (1989). Inference and discovery in an exploration laboratory. In P. Ackerrnan, R. Stemberg, & R. Glaser (Eds.), Learning and individual dzferences: Advances in theory and research (pp. 279-326). New York: Freeman.
  32. Smith, R. (1986). The Alternative Reality Kit: An animated environment for creating interactive simulations. In Proceedings of the I986 IEEE Computer Society Workshop on Visual Languages (pp. 99-106). Washington, DC: IEEE Computer Society Press.
  33. Sorensen, R. (1992). Thought experiments. Oxford, England: Oxford University Press.
  34. Tinker, R. (1993). Modelling and theory building: Technology in support of students' theorizing. In D. Ferguson (Ed.), Advanced educational technologies for mathematics and science (pp. 91-1 13). Berlin, Germany: Springer.
  35. Vygotsky, L. (1978). Mind in society: The development of higher psychologicalprocesses (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds. and Trans.). Cambridge, England: Cambridge University Press.
  36. Wellman, H. (1984). A child's theory of mind: The development of conceptions of cognition. In S. R. Yussen (Ed.), The growth of reflection in children (pp. 69-206). New York: Academic.
  37. White, B. (1981). Designing computer games to ,facilitate learning (Tech. Rep. No. AI-TR-619). Cambridge: Massachusetts Institute of Technology, Artificial Intelligence Laboratory.
  38. White B., 1994, The Educator, 8, 18
  39. White, B. & Honvitz, P. (1988). Computer microworlds and conceptual change: A new approach to science education. In P. Ramsden (Ed.), Improving learning: Newperspectives (pp. 69-80). London: Kogan Page.

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Metadata

Authors
White, B. Y., & Frederiksen, J. R.
Venue
Cognition and Instruction
Publisher
Informa UK Limited
Published
1998-03
Volume
16
Issue
1
Pages
3-118
ISSN
  • 0737-0008 (ISSN-L)
  • 1532-690X
DOI
10.1207/s1532690xci1601_2
OpenAlex
W2029930609
Cited by
1,394
References
73
Citations by year
2026: 21 · 2025: 31 · 2024: 30 · 2023: 35 · 2022: 55 · 2021: 38 · 2020: 67 · 2019: 53 · 2018: 49 · 2017: 65 · 2016: 62 · 2015: 82 · 2014: 57 · 2013: 75 · 2012: 79
FWCI
124.28
Type
journal article
Language
en
Open access
closed
Locations
  • Cognition and Instruction (publishedVersion)
  • HAL (submittedVersion)
Topics
  • Social Sciences › Education › Science Education and Pedagogy
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
Keywords
scientific inquiry, metacognition, reflective assessment, inquiry cycle, junior high school science, force and motion, modeling, scaffolding, low-achieving students, urban education, educational disadvantage

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