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Model-Based Methods for Assessment, Learning, and Instruction: Innovative Educational Technology at Florida State University

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Educational Media and Technology Yearbook

Abstract

In this chapter, we describe our research and development efforts relating to eliciting, representing, and analyzing how individuals and small groups conceptualize complex problems. The methods described herein have all been developed and are in various states of being validated. In addition, the methods we describe have been automated and most have been integrated in an online model-based set of tools called HIMATT (Highly Interactive Model-based Assessment Tools and Technologies; available for research purposes at http://himatt.ezw.uni-freiburg.de/cgi-bin/hrun/himatt.pl and soon to be available on a server at Florida State University). HIMATT continues to expand in terms of the tools and technologies included. Our methods and tools represent an approach to learning and instruction that is now embedded in many of the graduate courses at Florida State University and also at the University of Freiburg. We call our approach model-based because it integrates representations of mental models and internal cognitive processes with tools that are used to (a) assess progress of learning, and (b) provide the basis for informative and reflective feedback during instruction.

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Notes

  1. 1.

    To illustrate, your belief that Pluto is a planet likely changed in 2006 when the International Astronomical Union decided to re-classify Pluto as a “dwarf planet.”

References

  • Alpert, S. R. (2003). Abstraction in concept map and coupled outline knowledge representation. Journal of Interactive Learning Research, 14(1), 31–49.

    Google Scholar 

  • Bakeman, R., & Gottman, J. (1997). Observing interaction: An introduction to sequential analysis. Cambridge: University Press.

    Book  Google Scholar 

  • Bull, S., & Pain, H. (1995). Did I say what I think I said, and do you agree with me?: Inspecting and questioning the student model. Proceedings of the Artificial Intelligence in Education (AACE), Charlottesville, VA, pp. 501–508.

    Google Scholar 

  • Cannon-Bowers, J. A., & Salas, E. (Eds.). (1998). Making decisions under stress: Implications for individual and team training. Washington, DC: American Psychological Association.

    Google Scholar 

  • Dewey, J. (1915). The school and society.(2nd ed.). Chicago: University of Chicago Press.

    Google Scholar 

  • Ericsson, K. A., & Smith, J. (Eds.). (1991). Toward a general theory of expertise: Prospects and limits. New York: Cambridge University Press.

    Google Scholar 

  • Freeman, L. A., & Urbaczewski, A. (2001). Using concept to assess students’ understanding of IS. Journal of Information Systems Education, 12, 3–8.

    Google Scholar 

  • Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software – Practice and Experience, 21(11), 1129–1164.

    Article  Google Scholar 

  • Ganser, E. R., & North, S. C. (1999). An open graph visualization system and its applications to software engineering. SoftwarePractice and Experience, 00(S1), 1–5.

    Google Scholar 

  • Gentner, D., & Stevens, A. (1983). Mental Models. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Greeno, J. G. (1989). A perspective on thinking. American Psychologist, 44(2), 134–141.

    Google Scholar 

  • Grotzer. T. A., & Perkins, D. N. (2000). A taxonomy of causal models: The conceptual leaps between models and students’ reflections on them. Paper presented at the National Association of Research in Science Teaching (NARST), New Orleans, LA, 28 April–1 May 2000.

    Google Scholar 

  • Guzzo, R. A., & Salas, E. (1995). Team effectiveness and decision-making in organizations. San Francisco, CA: Jossey-Bass, Inc.

    Google Scholar 

  • Hackman, R. A. (1990). Groups that work (and those that don’t): Creating conditions for effective team work. San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Harary, F. (1974). Graphentheorie. München: Oldenbourg.

    Google Scholar 

  • Hartley, D., & Mitrovic, A. (2002). Supporting learning by opening the student model. Proceedings of ITS 2002, pp. 453–462.

    Google Scholar 

  • Herl, H. E., O’Neil, H. F., Jr., Chung, G. L. W. K., Bianchi, C., Wang, S., Mayer, R., et al. (1999). Final report for validation of problem solving measures (CSE Technical Report 501). Los Angeles: CRESST.

    Google Scholar 

  • Ifenthaler, D. (2006). Diagnosis of the learning-dependent progression of mental models. Development of the SMD-Technology as a methodology for assessing individual models on relational, structural and semantic levels. Freiburg: Universitäts-Dissertation.

    Google Scholar 

  • Ifenthaler, D. (2007). Relational, structural, and semantic analysis of graphical representations and concept maps. Paper presented at the Annual Convention of the AECT, Anaheim, CA.

    Google Scholar 

  • Ifenthaler, D., Masduki, I., & Seel, N. M. (2008). Tracking the development of cognitive structures over time. Paper presented at the AREA 2008, New York.

    Google Scholar 

  • Ifenthaler, D., & Seel, N. M. (2005). The measurement of change. Learning-dependent progression of mental models. Technology, Instruction, Cognition and Learning, 2(4), 317–336.

    Google Scholar 

  • Janetzko, D. (1996). Knowledge tracking. A method to analyze cognitive structures. Freiburg: IIG-Berichte 2.

    Google Scholar 

  • Jeong, A. (2005). A guide to analyzing message-response sequences and group interaction patterns in computer-mediated communication. Distance Education, 26(3), 367–383.

    Article  Google Scholar 

  • Jeong, A. (2007). The effects of intellectual openness and gender on critical thinking processes in computer-supported collaborative argumentation. Journal of Distance Education, 22(1), 1–18.

    Google Scholar 

  • Jeong, A. (2008). jMap. Retrieved May 5, 2008, from http://garnet.fsu.edu/∼ajeong

  • Jeong, J. C. (2004). Discussion Analysis Tool (DAT). Retrieved March 4, 2008, from http://garnet.fsu.edu/∼ajeong/DAT

  • Johnson, T. J., & O’Connor, D. L. (2008). Measuring team shared understanding using the analysis-constructed shared mental model methodology, Performance Improvement Quarterly, 21, 113–134.

    Google Scholar 

  • Kay, J. (1998). A scrutable user modelling shell for user-adapted interaction. Ph.D. Thesis, Basser Department of Computer Science, University of Sydney, Sydney, Australia.

    Google Scholar 

  • Klimoski, R., & Mohammed, S. (1994). Team mental model – Construct or metaphor. Journal of Management, 20(2), 403–437.

    Google Scholar 

  • Maedche, A., Pekar, V., & Staab, S. (2002). Ontology learning part one – On discovering taxonomic relations from the web. Proceedings of the Web Intelligence Conference, pp. 301–322, Springer.

    Google Scholar 

  • Mathieu, J. E., Heffner, T. S., Goodwin, G. F., Salas, E., & Cannon-Bowers, J. A. (2000). The influence of shared mental models on team process and performance. Journal of Applied Psychology, 85(2), 273–283.

    Article  Google Scholar 

  • O’Connor, D. L., & Johnson, T. E. (2004). Measuring team cognition: Concept mapping elicitation as a means of constructing team shared mental models in an applied setting. First International Conference on Concept Mapping, September 14–17, 2004, Pamplona, Spain.

    Google Scholar 

  • O’Neil, H. F., Wang, S., Chung, G., & Herl, H. E. (2000). Assessment of teamwork skills using computer-based teamwork simulations. In H. F. O’Neil & D. H. Andrews (Eds.), Aircrew training and assessment (pp. 244–276). Mahwah, New Jersey: Lawrence Erlbaum.

    Google Scholar 

  • Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufman Publishers.

    Google Scholar 

  • Pirnay-Dummer, P. (2006). Expertise und Modellbildung: MITOCAR. Freiburg: Universitäts-Dissertation.

    Google Scholar 

  • Pirnay-Dummer, P. (2007). Model inspection trace of concepts and relations. A heuristic approach to language-oriented model assessment. Paper presented at the AERA 2007, Division C, TICL SIG, April 2007, Chicago.

    Google Scholar 

  • Rowe, A. L., & Cooke, N. J. (1995). Measuring mental models: Choosing the right tools for the job. Human Resource Development Quarterly, 6(3), 243–255.

    Article  Google Scholar 

  • Ruiz-Primo, M. A., & Shavelson, R. J. (1996). Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33, 569–600.

    Article  Google Scholar 

  • Salas, E., & Cannon-Bowers, J. A. (2000). The anatomy of team training. In S. Tobias & J. D. Fletcher (Eds.), Training & retraining: A handbook for business, industry, government, and the military (pp. 312–335). New York: Macmillan Reference.

    Google Scholar 

  • Seel, N. M. (1991). Weltwissen und mentale Modelle [World knowledge and mental models]. Göttingen, Germany: Hogref.

    Google Scholar 

  • Seel, N. M. (1999a). Semiotics and structural learning theory. Journal of Structural Learning and Intelligent Systems, 14(1), 11–28.

    Google Scholar 

  • Seel, N. M. (1999b). Educational diagnosis of mental models. Assessment problems and technology-based solutions. Journal of Structural Learning and Intelligent Systems, 14(2), 153–185.

    Google Scholar 

  • Seel, N. M. (2003). Model-centered learning and instruction. Technology, Instruction, Cognition and Learning, 1(1), 59–85.

    Google Scholar 

  • Seel, N. M., & Winn, W. D. (1997). Research on media and learning: Distributed cognition and semiotics. In R. D. Tennyson, F. Schott, S. Dijkstra, & N. M. Seel (Eds.), Instructional design international perspectives: Vol. 1. Theories and models of instructional design (pp. 293–326). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.

    Google Scholar 

  • Shute, V. J., Jeong, A., & Zapata-Rivera, D. (2008). Assessing mental models and discourse patterns with evidence-based flexible belief networks. Paper presented at the American Educational Research Association conference for the Technology, Instructional, Cognition and Learning (TICL) Symposia, New York, NY.

    Google Scholar 

  • Shute, V. J., & Zapata-Rivera, D. (2008). Using an evidence-based approach to assess mental models. In D. Ifenthaler, P. Pirnay-Dummer, & J. M. Spector (Eds.), Understanding models for learning and instruction: Essays in honor of Norbert M. Seel. New York: Springer.

    Google Scholar 

  • Spector, J. M., Dennen, V. P., & Koszalka, T. (2006). Causal maps, mental models and assessing acquisition of expertise. Technology, Instruction, Cognition and Learning, 3, 167–183.

    Google Scholar 

  • Spector, J. M., & Koszalka, T. A. (2004). The DEEP methodology for assessing learning in complex domains (Final report the National Science Foundation Evaluative Research and Evaluation Capacity Building). Syracuse, NY: Syracuse University.

    Google Scholar 

  • Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327–352.

    Article  Google Scholar 

  • Wittgenstein, L. (1922). Tractatus logico-philosophicus (C. K. Ogden, Trans.). London: Routledge and Kegan Paul.

    Google Scholar 

  • Wittgenstein, L. (1953). Philosophical investigations (G. E. M. Anscombe, Trans.). London: Blackwell.

    Google Scholar 

  • Zapata-Rivera, D., & Greer, J. E. (2004). Interacting with inspectable Bayesian models. International Journal of Artificial Intelligence in Education, 14, 127–163.

    Google Scholar 

  • Zele, E. C. (2004). Improving the usefulness of concept maps as a research tool for science education. International Journal of Science Education, 26(9), 1043–1064.

    Article  Google Scholar 

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Correspondence to Valerie J. Shute .

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Shute, V.J., Jeong, A.C., Spector, J.M., Seel, N.M., Johnson, T.E. (2009). Model-Based Methods for Assessment, Learning, and Instruction: Innovative Educational Technology at Florida State University. In: Orey, M., McClendon, V.J., Branch, R.M. (eds) Educational Media and Technology Yearbook. Educational Media and Technology Yearbook, vol 34. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09675-9_5

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