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Beyond intelligent tutoring systems: Using computers as METAcognitive tools to enhance learning?

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Abstract

Framed by the existing theoretical andempirical research on cognitive and intelligenttutoring systems (ITSs), this commentaryexplores two areas not directly or extensivelyaddressed by Akhras and Self (this issue). Thefirst area focuses on the lack of conceptualclarity of the proposed constructivist stanceand its related constructs (e.g., affordances,situations). Specifically, it is argued that aclear conceptualization of the novelconstructivist stance needs to be delineated bythe authors before an evaluation of theirambitious proposal to model situationscomputationally in intelligent learningenvironments (ILEs) can be achieved. The secondarea of exploration deals with the similaritiesbetween the proposed stance and existingapproaches documented in the cognitive,educational computing, and AI in educationliterature. I believe that the authors are at acrossroads, and that their article presents aninitial conceptualization of an important issuerelated to a constructivist-based approach tothe computational modeling of situations inILEs. However, conceptual clarity isdefinitively required in order for theirapproach to be adequately evaluated and used toinform the design of ILEs. As such, I invitethe authors to re-conceptualize their frameworkby addressing how their constructivist stancecan be used to address a particular researchagenda on the use of computers as metacognitivetools to enhance learning.

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References

  • Anderson, J.R. (1983). The Architecture of Cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Anderson, J.R. (1993). Rules of the Mind. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Anderson, J.R., Corbett, A.T., Koedinger, K.R. & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences 4(2): 167–207.

    Google Scholar 

  • Anderson, J.R. & Lebiere, C. (1998). The Atomic Components of Thought. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Azevedo, R., Guthrie, J.T., Seibert, D. & Wang, H. (in prep.). The Role of Learner-generated Goals in Regulating Learning from Hypermedia. Manuscript in preparation.

  • Azevedo, R., Guthrie, J.T., Wang, H. & Mulhern, J. (2001). Do Different Instructional Interventions Facilitate Students' Ability to Shift to more Sophisticated Mental Models of Complex Systems? Paper to be presented at the Annual Conference of the American Educational Research Association, Seattle, WA.

  • Azevedo, R. & Lajoie, S.P. (1998). The cognitive basis for the design of a mammography interpretation tutor. International Journal of Artificial Intelligence in Education 9(1/2): 32–44.

    Google Scholar 

  • Azevedo, R., Verona, M.E. & Cromley, J.G. (2001, May). Fostering Students' Collaborative Problem Solving with RiverWeb. Paper to be presented at the 10th International conference on Artificial intelligence in Education, San Antonio, TX.

  • Boekaerts, M., Pintrich, P, & Zeidner, M. (2000). Handbook of Self-regulation. San Diego, CA: Academic Press.

    Google Scholar 

  • Chi, M.T.H. (2000). Self-explaining: The dual processes of generating inference and repairing mental models. In R. Glaser, ed., Advances in Instructional Psychology: Educational Design and Cognitive Science (vol. 5), pp. 161–238. Mawah, NJ: Erlbaum.

    Google Scholar 

  • Cromley, J.G. (2001, May). Effective Human Tutoring in Reading: Precursor to the Design of an ITS. Paper to be presented at the 10th International Conference in Artificial Intelligence on Education, San Antonio, TX.

  • Derry, S.J. & Lajoie, S.P. (1993). A middle camp for (un)intelligent instructional computing: An introduction. In S.P. Lajoie & S.J. Derry, eds, Computers as Cognitive Tools, pp. 1–11. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Du Boulay, B., Luckin, R. & del Soldato, T. (1999). The plausibility problem: Human teaching tactics in the “Hands” of a machine. In S.P. Lajoie & M. Vivet, eds., Frontiers in Artificial Intelligence and Applications. Open Learning Environments: New Computational Technologies to Support Learning, Eploration and Collaboration, pp. 225–232. Amsterdam: IOS Press.

    Google Scholar 

  • Erickson, J. & Lehrer, R. (2000). What' in a link? Student conceptions of the rhetoric of association in hypermedia composition. In S.P. Lajoie, ed., Computers as cognitive Tools II: No more Walls: Theory Change, Paradigm Shifts and Their Influence on the Use of Computers for Instructional Purposes, pp. 197–226. Mawah, NJ: Erlbaum.

    Google Scholar 

  • Jacobson, M.J. & Kozma, R.B. (2000). Innovations in Science and Mathematics Education: Advanced Designs for Technologies of Learning. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Jonassen, D. (2000). Computers as Mindtools for Schools: Engaging Critical Thinking (2nd ed.). Englewood Cliffs, N.J.: Merrill.

    Google Scholar 

  • Jonassen, D. & Land, S.M. (Eds). (2000). Theoretical Foundations of Learning Environments. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Koedinger, K.R. & Anderson, J.R. (1997). Intelligent tutoring goes to school. International Journal of Artificial Intelligence in Education 8: 30–43.

    Google Scholar 

  • Koedinger, K.R. & Anderson, J.R. (1998). Illustrating principled design: The early evolution of a cognitive tutor for algebra symbolization. Interactive Learning Environments 5: 161–179.

    Google Scholar 

  • Kozma, R., Chin, E., Russell, J. & Marx, N. (2000). The roles of representations and tools in the chemistry laboratory and their implications for chemistry learning. Journal of the Learning Sciences 9(2): 105–144.

    Google Scholar 

  • Lajoie, S.P. (1993). Computer environments as cognitive tools for enhancing learning. In S.P. Lajoie & S.J. Derry, eds, Computers as Cognitive Tools, pp. 261–288. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Lajoie, S.P. (2000). Computers as Cognitive Tools II: No more Walls: Theory Change, Paradigm Shifts and Their Influence on the Use of Computers for Instructional Purposes. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Lajoie, S.P. & Azevedo, R. (2000). Cognitive tools for medical informatics. In S.P. Lajoie, ed., Computers as Cognitive Tools II: No more Walls: Theory Change, Paradigm Shifts and Their Influence on the Use of Computers for Instructional Purposes, pp. 247–271. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Lajoie, S.P., Azevedo, R. & Fleiszer, D.M. (1998). Cognitive tools for assessment and learning in a high flow information environment. Journal of Educational Computing Research 18(3): 203–233.

    Google Scholar 

  • Lajoie, S.P. & Derry, S.J. (1993). Computers as Cognitive Tools. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Lepper, M., Woolverton, M., Mumme, D. & Gurtner, J. (1993). Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. In S. Lajoie & S. Derry, eds, Computers as Cognitive Tools, pp. 75–105. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Mayer, R.E. & Wittrock, M.C. (1996). Problem solving transfer. In D. Berliner & R. Calfee, eds, Handbook of Educational Psychology, pp. 45–61. New York: Macmillan.

    Google Scholar 

  • Newell, A. (1989). Putting it all together. In D. Klahr & K. Kovosky, eds., Complex Information Processing: The Impact of Herbert A. Simon, pp. 399–440. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Newell, A. & Simon, H.A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Pintrich, P.R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. Pintrich & M. Zeidner, eds, Handbook of Self-regulation, pp. 451–502. San Diego, CA: Academic Press.

    Google Scholar 

  • Schunk, D. & Zimmerman, B. (1994).Self-regulation of Learning and Performance: Issues and Educational Applications. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Shute, V. & Psotka, J. (1996). Intelligent tutoring system: Past, present, and future. In D. Jonassen, ed., Handbook of Research for Educational Communications and Technology, pp. 570–600. New York: Macmillan.

    Google Scholar 

  • Winne, P. (1998). Experimenting to bootstrap self-regulated learning. Journal of Educational Psychology 89: 397–410.

    Google Scholar 

  • Winne, P.H. & Perry, N.E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich & M. Zeidner, eds., Handbook of Self-regulation, pp. 531–566. San Diego, CA: Academic Press.

    Google Scholar 

  • Zimmerman, B. (2000). Attaining self-regulation: A social-cognitive perspective. In M. Boekaerts, P. Pintrich & M. Zeidner, eds, Handbook of Self-regulation, pp. 13–35. San Diego, CA: Academic Press.

    Google Scholar 

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  1. Department of Human Development, University of Maryland, College of Education, College Park, MD, 20742-1131, USA

    Roger Azevedo

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  1. Roger Azevedo
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Azevedo, R. Beyond intelligent tutoring systems: Using computers as METAcognitive tools to enhance learning?. Instructional Science 30, 31–45 (2002). https://doi.org/10.1023/A:1013592216234

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