Journal of Science Education and Technology

, Volume 13, Issue 1, pp 23–41

Model-Based Teaching and Learning with BioLogica™: What Do They Learn? How Do They Learn? How Do We Know?

  • Barbara C. Buckley
  • Janice D. Gobert
  • Ann C. H. Kindfield
  • Paul Horwitz
  • Robert F. Tinker
  • Bobbi Gerlits
  • Uri Wilensky
  • Chris Dede
  • John Willett
Article

Abstract

This paper describes part of a project called Modeling Across the Curriculum which is a large-scale research study in 15 schools across the United States. The specific data presented and discussed here in this paper is based on BioLogica, a hypermodel, interactive environment for learning genetics, which was implemented in multiple classes in eight high schools. BioLogica activities, data logging, and assessments were refined across this series of implementations. All students took a genetics content knowledge pre- and posttests. Traces of students' actions and responses to computer-based tasks were electronically collected (via a “log file” function) and systematically analyzed. An intensive 3-day field test involving 24 middle school students served to refine methods and create narrative profiles of students' learning experiences, outcomes, and interactions with BioLogica. We report on one high school implementation and the field test as self-contained studies to document the changes and the outcomes at different phases of development. A discussion of design changes concludes this paper.

genetics model-based learning interactive environments data logging technology-enhanced assessment 

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REFERENCES

  1. Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology 84: 261-271.Google Scholar
  2. Bereiter, C., and Scardamalia, M. (1989). Intentional learning as a goal of instruction. In Resnick, L. B. (Ed.), Knowing, Learning and Instruction: Essays in Honor of Robert Glaser, Erlbaum, Hillsdale, NJ, pp. 361-392.Google Scholar
  3. Bransford, J., Sherwood, R., Vye, N., and Rieser, J. (1986). Teaching thinking and problem solving. American Psychologist 41: 1078-1089.Google Scholar
  4. Brewer, W. F. (1987). Schemas versus mental models in human memory. In Morris, P. (Ed.), Modelling Cognition, Wiley, Chicester, UK, pp. 187-197.Google Scholar
  5. Buckley, B. C. (1992). Multimedia, Misconceptions and Working Models of Biological Phenomena: Learning About the Circulatory System, Unpublished Doctoral Dissertation, Stanford University.Google Scholar
  6. Buckley, B. C. (2000). Interactive multimedia and model-based learning in biology. International Journal of Science Education 22: 895-935.Google Scholar
  7. Buckley, B. C., and Boulter, C. J. (1999). Analysis of representations in model-based teaching and learning in science. In Paton, R., and Neilson, I. (Eds.), Visual Representations and Interpretation, Springer, Liverpool, England, pp. 289-294.Google Scholar
  8. Buckley, B. C., and Boulter, C. J. (2000). Investigating the role of representations and expressed models in building mental models. In Gilbert, J. K., and Boulter, C. J. (Eds.), Developing Models in Science Education, Kluwer, Dordrecht, Holland, pp. 105-122.Google Scholar
  9. Chinn, C. A., and Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research 63: 1-49.Google Scholar
  10. Christie, M. (1999). “We understood it more 'cause we were doin' it ourself:” Students self-described connections between participation and learning. Paper Presented at the American Educational Research Association, Montreal, Canada, April 20, 1999.Google Scholar
  11. Christie, M. (2001). Portraits of academic beliefs. Paper Presented at the Annual Meeting of the New England Educational Research Organization (NEERO), Portsmouth, NH, April 26, 2001.Google Scholar
  12. Christie, M. (2002). The role of classroom culture and learning contexts in achievement beliefs. Paper Presented at the Annual Meeting of the American Educational Research Association (AERA), New Orleans, LA, April 1–5, 2002.Google Scholar
  13. Clement, J. (1989). Learning via model construction and criticism: Protocol evidence on sources of creativity in science. In Glover, J. A., Ronning, R. R., and Reynolds, C. R. (Eds.), Handbook of Creativity: Assessment, Theory and Research, Plenum, New York, pp. 341-381.Google Scholar
  14. Deci, E., and Ryan, R. (1985). Intrinsic Motivation and Self-Determination in Human Behavior, Academic Press, New York.Google Scholar
  15. de Kleer, J., and Brown, J. S. (1983). Assumptions and ambiguities in mechanistic mental models. In Stevens, A. L., and Gentner, D. (Eds.), Mental Models, Erlbaum, Hillsdale, NJ, pp. 155-190.Google Scholar
  16. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist 41: 1040-1048.Google Scholar
  17. Gentner, D., and Stevens, A. L. (Eds.). (1983). Mental Models, Erlbaum, Hillsdale, NJ.Google Scholar
  18. Gobert, J. (2000). A typology of models for plate tectonics: Inferential power and barriers to understanding. International Journal of Science Education 22: 937-977.Google Scholar
  19. Gobert, J., and Buckley, B. C. (2003). Scaffolding Model-Based Reasoning: Representations and Cognitive Affordances, The Concord Consortium, Concord, MA.Google Scholar
  20. Gobert, J., and Discenna, J. (1997). The relationship between students' epistemologies and model-based reasoning. Paper Presented at the American Educational Research Association, Chicago.Google Scholar
  21. Gobert, J., Snyder, J., and Houghton, C. (2002). The influence of students' understanding of models on model-based reasoning. Paper Presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA, April 1–5, 2002.Google Scholar
  22. Gobert, J. D., and Buckley, B. C. (2000). Introduction to model-based teaching and learning in science education. International Journal of Science Education 22: 891-894.Google Scholar
  23. Gobert, J. D., and Clement, J. J. (1999). Effects of student-generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching 36: 39-53.Google Scholar
  24. Hickey, D. T., Kindfield, A. C. H., Horwitz, P., and Christie, M. A. (1999). Advancing educational theory by enhancing practice in a technology-supported genetics learning environment. Journal of Education 181: 25-55.Google Scholar
  25. Hickey, D. T., Kindfield, A. C. H., Horwitz, P., and Christie, M. A. (2003). Assessment-oriented scaffolding of student and teacher performance in a technology-supported genetics environment. American Educational Research Journal 40(2): 495-538.Google Scholar
  26. Hickey, D. T., and Kindfield, A. C. H. D. (1999). Assessment-oriented scaffolding of student and teacher performance in a technology-supported genetics environment. Paper Presented at the Annual Meeting of the American Educational Research Association, Montreal, Canada.Google Scholar
  27. Hickey, D. T., Wolfe, E. W., and Kindfield, A. C. H. (1998a). Assessing learning in a technology-supported genetics environment: Evidential and consequential validity issues. Paper Presented at the Annual Meeting of the American Educational Research Association, San Diego, April 1998.Google Scholar
  28. Hickey, D. T., Wolfe, E. W., and Kindfield, A. C. H. (1998b). Assessing learning in a technology-supported genetics environment: Evidential and systemic validity issues. Paper Presented at the Annual Meeting of the American Educational Research Association, San Diego, April 1998.Google Scholar
  29. Horwitz, P. (1995). Linking models to data: Hypermodels for science education. The High School Journal 79(2): 148-156.Google Scholar
  30. Horwitz, P., and Christie, M. A. (1999). Hypermodels: Embedding curriculum and assessment in computer-based manipulatives. Journal of Education 81(2): 1-23.Google Scholar
  31. Horwitz, P., Schwartz, J., Kindfield, A. C. H., Yessis, L. M., Hickey, D. T., Heidenberg, A. J., and Wolfe, E. W. (1998). Implementation and evaluation of GenScope and trade; learning environment: Issues, solutions, and results. Paper Presented at the Third International Conference of the Learning Sciences, Charlottesville, VA.Google Scholar
  32. Johnson-Laird, P. N. (1983). Mental Models, Harvard University Press, Cambridge, MA.Google Scholar
  33. Kindfield, A. C. H. (1993/1994). Biology diagrams: Tools to think with. Journal of the Learning Sciences 3: 1-36.Google Scholar
  34. Kindfield, A. C. H., and Hickey, D. T. (1999). Tools for scaffolding inquiry in the domain of introductory genetics. Paper Presented at the Annual Meeting of the American Educational Research Association, Montreal, Canada.Google Scholar
  35. Kozma, R., Jones, T., Wykoff, J., and Russell, J. (1992). Multimedia, multiple representations, and mental models in chemistry. Paper Presented at the Annual Meeting of the American Educational Research Association, San Francisco.Google Scholar
  36. Larkin, J. H. (1989). Display-based problem solving. In Klarh, D., and Kotovsky, K. (Eds.), Complex Information Processing: The Impact of Herbert A. Simon, Erlbaum, Hillsdale, NJ, pp. 319-341.Google Scholar
  37. Larkin, J. H., and Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science 11: 65-99.Google Scholar
  38. Lepper, M., and Chabay, R. W. (1985). Intrinsic motivation and instruction: Conflicting views of the role of motivational processes in computer-based education. Educational Psychologist 20: 217-230.Google Scholar
  39. Lepper, M., and Malone, T. (1987). Intrinsic motivation and instructional effectiveness in computer-based education. In Snow, R. E., and Farr, M. J. (Eds.), Aptitude, Learning and Instruction III: Conative and Affective Process Analyses, LEA, Hillsdale, NJ, pp. 255-286.Google Scholar
  40. Lowe, R. (1993). Successful Instructional Diagrams, Kogan Page, London.Google Scholar
  41. Monaghan, J., and Clement, J. (1994). Factors affecting the efficacy of computer simulation for facilitating relative motion concept acquisition and visualization. Paper Presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA.Google Scholar
  42. National Research Council. (2002). Technology and Assessment: Thinking Ahead: Proceedings of a Workshop, National Academy Press, Washington, DC.Google Scholar
  43. Nicholls, J. G. (1989). The Competitive Ethos and Democratic Education, Harvard University Press, Cambridge, MA.Google Scholar
  44. Norman, D. A. (1983). Some observations on mental models. In Stevens, A. L., and Gentner, D. (Eds.), Mental Models, Erlbaum, Hillsdale, NJ, pp. 7-14.Google Scholar
  45. Rouse, W. B., and Morris, N. M. (1986). On looking into the Black Box: Prospects and limits in the search for mental models. Psychological Bulletin 100: 349-363.Google Scholar
  46. Schauble, L., Glaser, R., Raghavan, K., and Reiner, M. (1991). Causal models and experimentation strategies in scientific reasoning. Journal of the Learning Sciences 1: 210-238.Google Scholar
  47. Schommer, M. (1993). Epistemological development and academic performance among secondary students. Journal of Educational Psychology 85: 406-411.Google Scholar
  48. Songer, N. B., and Linn, M. C. (1991). How do students' views of science influence knowledge integration? Journal of Research in Science Teaching 28: 761-784.Google Scholar
  49. Stewart, J., and Hafner, B. (1991). Extending the conception of problem solving. Science Education 75: 105-120.Google Scholar
  50. Vosniadou, S., and Brewer, W. F. (1992). Mental models of the earth: A study of conceptual change in childhood. Cognitive Psychology 24: 535-585.Google Scholar
  51. White, B. Y., and Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction 16: 3-118.Google Scholar

Copyright information

© Plenum Publishing Corporation 2004

Authors and Affiliations

  • Barbara C. Buckley
    • 1
  • Janice D. Gobert
    • 1
  • Ann C. H. Kindfield
    • 2
  • Paul Horwitz
    • 1
  • Robert F. Tinker
    • 1
  • Bobbi Gerlits
    • 1
  • Uri Wilensky
    • 3
  • Chris Dede
    • 4
  • John Willett
    • 4
  1. 1.The Concord ConsortiumConcord
  2. 2.Educational Designs Unlimited, Inc.Hillsborough
  3. 3.School of Eduation and Social PolicyNorthwestern UniversityChicago
  4. 4.Graduate School of EducationHarvard UniversityCambridge

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