Skip to main content
Log in

Task analysis and validation for a qualitative, exploratory curriculum in force and motion

Instructional Science Aims and scope Submit manuscript

Abstract

Many researchers have stressed the importance of qualitative understanding of physical phenomena, particularly in the context of exploratory learning environments. Qualitative understanding proves to be a major part of the expert's ability to solve complex problems in physics. Some researchers think that this kind of reasoning, far from being specific to experts' knowledge, also characterizes intuitive understanding and plays a part in the transition from intuitive knowledge to more expert knowledge. It is therefore important to help students develop their qualitative reasoning and extend their existing useful conceptions. This paper presents a task analysis of a computer microworld of force and motion problems that allows students to gain a qualitative understanding of some aspects of vector algebra. The aim of the task analysis being to develop a qualitative curriculum for exploratory learning, we tried to represent the knowledge to be acquired in such a way as to promote the progressive conceptual understanding of some basic aspects of Newton's laws of motion, taking into account students' intuitive knowledge about physics. The task analysis was undertaken prior to the experimental study in order to provide guidance for students in their exploration of the microworld. The experimental work allows us to validate and extend the a priori analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abelson, H. & diSessa, A. (1981). Turtle Geometry. Cambridge, MA: M.I.T.

    Google Scholar 

  • Anderson, R. C. (1977). The notion of schemata and the educational enterprise: General discussion of the conference, In R. C. Anderson, R. J. Spiro & W. E. Montague, eds., Schooling and the Acquisition of Knowledge. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Brna, P. (1987). Confronting dynamic misconceptions. Instructional Science 16: 351–379.

    Google Scholar 

  • Brna, P. (1988).Confronting misconceptions in the domain of simple electrical circuits. Instructional Science 17: 29–55.

    Google Scholar 

  • Caramazza, A., McCloskey, M. & Green, B. (1981). Naive beliefs in “sophisticated” subjects: Misconceptions about trajectories of objects. Cognition 9: 117–123.

    Google Scholar 

  • Champagne, A. B., Gunstone, R. & Anderson, J. H. (1980). Factors influencing the learning of classical mechanics. American Journal of Physics 48: 1074–1079.

    Google Scholar 

  • Champagne, A. B., Gunstone, R. & Klopfer, L. E. (1985). Instructional consequences of students' knowledge about physical phenomena. In L. H. T. West & L. E. Pines, eds., Cognitive Structure and Conceptual Change. New York: Academic Press.

    Google Scholar 

  • Chi, M. T. H., Feltovitch, P. J. & Glaser, R. (1982). Categorization and representation of physics problems by experts and novices. Cognitive Science 5: 121–152.

    Google Scholar 

  • Clement, J. (1982). Students' preconceptions in elementary mechanics. American Journal of Physics 50: 66–71.

    Google Scholar 

  • Clement, J. (1983). A conceptual model discussed by Galileo and used intuitively by physics students. In D. Gentner & A. L. Stevens, eds., Mental models. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Clement, J. (1988). Observed methods for generating analogies in scientific problems. Instructional Science 12: 563–586.

    Google Scholar 

  • Clement, J., Brown, D. E. & Zietsman, A. (1989). Not all preconceptions are misconceptions: Finding “anchoring conceptions” for grounding instruction on students' intuitions. International Journal of Science Education 1: 554–565.

    Google Scholar 

  • Collins, A., Hawkins & Frederiksen, J. R. (1993). Three different views of students: The role of technology in assessing student performance. The Journal of The Learning Sciences 3(2): 205–217.

    Google Scholar 

  • Confrey, J. (1990). A review of the research on student conceptions in mathematics, science and programming. In C. Cazden, ed., Review of Research in Education 16: 3–56.

  • diSessa, A. (1977). On “learnable” representation of knowledge: A meaning for the computational metaphor. Logo Memo, No. 47, M.I.T., A.I. Laboratory.

  • diSessa, A. (1980). Momentum flow as an alternative perspective in elementary mechanics. American Journal of Physics 48(5): 365–369.

    Google Scholar 

  • diSessa, A. (1982). Unlearning Aristotelian physics: A study of knowledge-based learning. Cognitive Science 6: 37–75.

    Google Scholar 

  • diSessa, A. (1983). Phenomenology and the evolution of intuition. In D. Genter & A. L. Stevens, eds., Mental Models. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • diSessa, A. (1985). Learning about knowing. In E. Klein, ed., Children and Computers: New Directions of Child Development (28). San Francisco: Jossey-Bass.

    Google Scholar 

  • diSessa, A. (1986). Artificial world and real experience. Instructional Science 14(3–4): 207–229.

    Google Scholar 

  • diSessa, A. (1988). Knowledge in pieces. In G. Forman & P. B. Pufall, eds., Constructivism in the Computer Age. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • diSessa, A. (1991) Epistemological micromodels: The case of coordination and quantities. In Cahiers de la Fondation Archives Jean Piaget, no. 11. Genève.

  • diSessa, A. (1993). Toward an epistemology of physics. Cognition and Instruction 10(2–3): 105–225.

    Google Scholar 

  • Dufresne, R. J., Gerace, W. J., Hardiman, P. T. & Mestre, J. P. (1992). Constraining novices to perform expertlike problem analysis: Effects on schema acquisition. The Journal of the Learning Sciences 2(3): 307–331.

    Google Scholar 

  • Duit, R. (1992). Students' conceptual frameworks: Consequences for learning science. In S. M. Glynn, R. H. Yeany & B. K. Britton, eds., The Psychology of Learning Science. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Elsom-Cook, M., ed. (1990). Guided Discovery Tutoring. A Framework for ICAI Research. Paul Chapman Publishing Ltd. London.

    Google Scholar 

  • Fredericksen, J. R. & White, B. (1992). Mental models and understanding: A problem for science education. In E. Scanlon & T. O'shea, eds., New Directions in Educational Technology. New York: Springer-Verlag.

    Google Scholar 

  • Glaser, R. (1992). Expert knowledge and processes of thinking. In D. H. Halpern, ed., Enhancing Thinking Skills in the Sciences and Mathematics. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Green, B. F., McCloskey, M. & Caramazza, A. (1985). The relation of knowledge to problem solving with examples from kinematics. In S. F. Chipman, J. Segal & R. Glaser, eds., Thinking and Learning Skills: Current Research and Open Questions 2. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Greeno, J. G. (1992).Mathematical and scientific thinking in classroom and other situations. In D. F. Halpern, ed., Enhancing Thinking Skills in the Sciences and Mathematics. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Gunstone, J.G. & White, R. (1981). Understanding of gravity. Science Education 65: 291–300.

    Google Scholar 

  • Halloun, I. A. & Hestenes, D. (1985a). The Initial knowledge state of college students. American Journal of Physics 53: 1043–1055.

    Google Scholar 

  • Halloun, I. A. & Hestenes, D. (1985b). Common sense concepts about motion. American Journal of Physics 53: 1056–1065.

    Google Scholar 

  • Halpern, D. F. (1992). A cognitive approach to improving skills in the sciences and mathematics. In D. F. Halpern, ed., Enhancing Thinking Skills in the Sciences and Mathematics. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Hegarty, M. (1991). Knowledge and processes in mechanical problem solving. In R. J. Sternberg & P. A. Frensch, eds., Complex Problem Solving: Principles and Mechanisms. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Hewson, P.W. & Posner, C. J. (1984). The use of schema theory in the design of instructional materials: A physics example. Instructional Science 13(2): 119–141.

    Google Scholar 

  • Jarret, D. G. (1987). Learning schemata: Methods of representing cognitive content and curriculum structures in higher education. Instructional Science 16: 187–211.

    Google Scholar 

  • Krajcik, J. S., Simmons, P. E. & Lunetta, V. N. (1986). Improving research on computers in science learning. Journal of Research in Science Teaching 23(5): 465–470.

    Google Scholar 

  • Krajcik, J. S (1991). Developing students' understanding of chemical concepts. In S.M. Glynn, R. H. Yeany & B. K. Britton, eds., The Psychology of Learning Science. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Larkin J. (1983). The role of representation in physics. In D. Genter & A. L. Stevens, eds., Mental Models. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Larkin, J. (1985).Understanding problem representation and skills in physics. In S. F. Chipman, J. Segal & R. Glaser, eds., Thinking and Learning Skills: Current Research and Open Questions 2. Hillsdale, NJ: Lawrence Erlbaum

    Google Scholar 

  • Larkin, J., McDermott, J., Simon, D. P. & Simon, H. E. (1980a). Expert and novice preformance in solving physics problems. Science 208: 317–345.

    Google Scholar 

  • Larkin J., McDermott J., Simon, D. P. & Simon, H. E. (1980b). Models of competence in solving physics problems. Cognitive Science 4: 317–345.

    Google Scholar 

  • Lawler R. (1982). Designing computer-based microworlds. Byte 7(8): 138–161.

    Google Scholar 

  • Lawler, R. (1985). Computer Experience and Cognitive Development. Ellis Howard series in cognitive science. New York: Halsted Press.

    Google Scholar 

  • Legendre, M-F. (1993). Étude du développement d'une compréhension qualitative de l'effet d'une force sur un mobile dans le contexte d'un micromonde de mouvement. Rapport de recherche, Publications de la Faculté des sciences de l'éducation, Université de Montréal, 320 pages.

  • Lemeignan, G. & Weil-Barais, A. (1994) A developmental approach to cognitive change in mechanics. International Journal of Science Education 16(1): 99–120.

    Google Scholar 

  • McCloskey, M., Caramazza, A. & Green, B. (1980). Curvilinear motion in the absence of external forces: Naive beliefs about the motion of objects. Science 210: 1139–1141.

    Google Scholar 

  • McCloskey, M. (1983a). Naive theories of motion. In D. Genter & A. Stevens, eds., Mental Models. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • McCloskey, M. (1983b). Intuitive physics. Scientific American (April): 122–130.

  • McDermott, L. C. (1984). Research on conceptual understanding in mechanics. Physics Today 37: 24–32.

    Google Scholar 

  • Minsky M. (1986) The Society of Mind. New York: Simon and Shuster.

    Google Scholar 

  • Ogborn, J. (1985). Understanding students' understandings: An example from dynamics. European Journal of Science Education 7(2): 141–150.

    Google Scholar 

  • Papert, S. (1980). Mindstorms: Childrens, Computer and Powerful Ideas. New York: Basic Books.

    Google Scholar 

  • Papert S. (1988). The conservation of Piaget: The computer as grist to the constructivist mill. In G. Forman & P. B. Pufall, eds., Constructivism in the Computer Age. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Perkins, D. N. & Unger, C. (1994). A new look in representations for mathematics and science learning. Instructional Science 22: 1–37.

    Google Scholar 

  • Piaget, J. & Inhelder, B. (1955). De la logique de l'enfant à la logique de l'adolescent. Paris: PUF.

    Google Scholar 

  • Piaget J. (1969). Psychologie et pédagogie. Paris, coll. Médiations, édition Denoël.

  • Piaget J. (1973a). (2nd. ed) La formation de la notion de force, Études d'épistémologie génétique 34. Paris: P.U.F.

    Google Scholar 

  • Piaget J. (1973b). (2nd. ed) La composition des forces et le problème des vecteurs, Études d'épistémologie génétique 30. Paris: P.U.F.

    Google Scholar 

  • Piaget, J. (1975). L'équilibration des structures cognitives: problème central du développement. In Études d'épistémologie génétique 33. Paris: PUF.

    Google Scholar 

  • Ploetzner, R., Spada, H., Stumpf, M. & Opwis, K. (1990). Learning qualitative and quantitative reasoning in a microworld for elastic impacts. European Journal of Psychology of Education 5(4): 501–516.

    Google Scholar 

  • Reif, F. (1985). Acquiring an effective understanding of scientific concepts. In L. H. T. West & A. L. Pines, eds., Cognitive Structure and Conceptual Change. New York: Academic Press.

    Google Scholar 

  • Reif, F. (1987). Interpretation of scientific or mathematical concepts: Cognitive issues and instructional implications. Cognitive Science 9(4): 395–417.

    Google Scholar 

  • Reif, F. & Allen, S. (1992). Cognition for interpreting scientific concepts: A study of acceleration. Cognition and Instruction 9(1): 1–44.

    Google Scholar 

  • Roschelle, J. (1991). Students' construction of qualitative physics knowledge: Learning about velocity and acceleration in a computer microworld. Unpublished Doctoral Disseration. University of California: Berkeley.

  • Roschelle, J. (1992). Learning by collaboration: Convergent conceptual change. The Journal of the Learning Science 2(3): 235–277.

    Google Scholar 

  • Rumelhart, D. E. & Ortony, A. (1977). The representation of knowledge in memory. In R. C. Anderson, R. J. Spiro & W. E. Montague, eds., Schooling and the Acquisition of Knowledge. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Rumelhart, D. (1980). Schemata: The building blocks of cognition. In R. J. Spiro, B. C. Bruce & W. F. Brewer, eds., Theoretical Issues in Reading Comprehension. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Simmons, P. E. (1991). Learning science in software microworlds. In S. M. Glynn, R. H. Yeany & B. K. Britton, eds., The Psychology of Learning Science. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Smith, J. P., diSessa, A., Roschelle, J. (1993). Misconceptions reconceived: Constructivist analysis of knowledge in transition. The Journal of the Learning Sciences 3(2): 115–163.

    Google Scholar 

  • Trowbridge, D. E. & McDermott, L. C. (1980). Investigation of students' understanding of the concept of velocity in one dimension. American Journal of Physics 48: 1020–1028.

    Google Scholar 

  • Trowbridge, D. E. & McDermott, L. C. (1981). Investigation of student's understanding of the concept of acceleration in one dimension. American Journal of Physics 49: 242–253.

    Google Scholar 

  • Viennot, L. (1979). Le raisonnement spontané en dynamique élémentaire. Paris: Hermann.

    Google Scholar 

  • Vygotsky, L. (1986). Thought and Language. Cambridge, MA: MIT Press.

    Google Scholar 

  • West, L. H. T. & Pines, A. L., eds. (1985). Cognitive Structure and Conceptual Change. New York: Academic Press.

    Google Scholar 

  • White, B. (1983). Sources of difficulty in understanding Newtonian dynamics. Cognitive Science 7: 41–65.

    Google Scholar 

  • White, B. & Horwitz, P. (1988). Computer microworlds and conceptual change: A new approach to science education. In P. Ramsden, ed., Improving Learning; New Perspectives. Kogan Page.

  • White, B. (1992) A microworld-based approach to science education. In E. Scanlon & T. O'shea, eds., New Directions in Educational Technology. New York: Springer-Verlag.

    Google Scholar 

  • White, B. (1993). Thinker Tools: Causal models, conceptual change and science education. Cognition and Instruction 10(1): 1–100.

    Google Scholar 

  • Yazdani M. & Lawler R. (1986). Artificial intelligence and education: An overview. Instructional Science 14(3–4): 197–207.

    Google Scholar 

  • Zietsman, A. I. & Hewson, P.W. (1986). Effect of instruction using microcomputer simulations and conceptual change strategies on science learning. Journal of Research in Science Teaching 23(1): 27–39.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

LEGENDRE, MF. Task analysis and validation for a qualitative, exploratory curriculum in force and motion. Instructional Science 25, 255–305 (1997). https://doi.org/10.1023/A:1002955823454

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1002955823454

Keywords

Navigation