Abstract
We discuss the implications of making learner models that can be inspected by learners within the context of a sensorimotor control task—that of balancing a pole hinged to a cart. We argue that the requirement of producing models that are comprehensible by learners limits the options of modelling strategy, constrains model structure and calls for further refinement of model contents. We discuss the issues of modularity of model contents, modality and interactivity of model presentation, and present results from a preliminary evaluation of a graphical interface to learner models for pole balancing.
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Supported by CONACYT and the Instituto de Investigaciones Electricas, Mexico, under scholarship 64999/111091.
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References
Anderson J.R.: Rules of the Mind. Lawrence Erlbaum Associates (1993)
Ayala G., Yano Y.: Learner models for supporting awareness and collaboration in a CSCL environment. In: Frasson C., Gauthier G. (eds.), Intelligent Tutoring Systems (ITS’96), vol. 1086 of Lecture Notes in Computer Science. Springer-Verlag (1996) 158–167
Barr A., Feigenbaum E.A. (eds.): The Handbook of Artificial Intelligence, vol. I. Pitman, London (1981)
Barto A.G., Sutton R.S., Anderson C.W.: Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man and Cybernetics SMC-13 (1983) 834–846
de Buen P.R., Vadera S., Morales E.F.: A collaborative approach to user modeling within a multi-functional architecture. In: Kay [17] (1999) 291–293
Bull S.: See yourself write: A simple student model to make students think. In: Jameson A., Paris C., Tasso C. (eds.), User Modeling (UM’97). Springer Wien New York (1997) 315–326
Bull S., Brna P., Pain H.: Extending the scope of the student model. User Modeling and User-Adapted Interaction 5 (1995) 45–65
Bull S., Pain H.: “Did I say what I think I said, and do you agree with me?” inspecting and questioning the student model. In: Greer [14] (1995) 501–508
Cohen W.W.: Fast effective rule induction. In: Prieditis A., Russell S. (eds.), Machine Learning (ML’95). Morgan Kaufmann (1995)
Conlon T.: Alternatives to rules for knowledge-based modelling. Instructional Science 27 (1999) 117–146
Cook R., Kay J.: The justified user model: A viewable, explained user model. In: Proceedings of the Fourth International Conference on User Modeling. Hyannis, MA (1994) 145–150
Cox R., Brna P.: Supporting the use of external representations in problem solving: the need for flexible learning environments. Journal of Artificial Intelligence in Education 6 (1995) 239–302
Dimitrova V., Self J., Brna P.: The interactive maintenance of open learner models. In: Lajoie S.P., Vivet M. (eds.), Artificial Intelligence in Education (AIEd’99). IOS Press (1999) 405–412
Greer J.E. (ed.): Proceedings of the Seventh World Conference on Artificial Intelligence in Education. Washington, DC (1995)
Greer J.E., Zapata-Rivera J.D., Ong-Scutchings C., Cooke J.E.: Visualization of Bayesian learner models. In: Morales R., Pain H., Bull S., Kay J. (eds.), Proceedings of the Workshop on Open, Interactive, and Other Overt Approaches to Learner Modelling. Le Mans, France (1999) 9–13
Hayes-Roth F.: Rule-based systems. Communications of ACM 28 (1985) 921–932
Kay J. (ed.): User Modeling (UM’99). Springer Wien New York (1999)
Michie D., Chambers R.A.: BOXES: An experiment in adaptive control. In: Dale E., Michie D. (eds.), Machine Intelligence, vol. 2. Oliver and Boyd, Edinburgh (1968) 137–152
Mitchell T.M.: Machine Learning. McGraw-Hill (1997)
Morales R., Pain H.: Modelling of novices’ control skills with machine learning. In: Kay [17] (1999) 159–168
Paiva A., Self J.A., Hartley R.: Externalising learner models. In: Greer [14] (1995)
Pineda L., Garza G.: A model for multimodal representation and inference. In: Paton R., Neilsen I. (eds.), Visual Representations and Interpretations. Springer (1999)
Self J.A.: Bypassing the intractable problem of student modelling. In: Proceedings of ITS’88. Montreal, Canada (1988) 18–24
Sison R., Shimura M.: Student modeling and machine learning. International Journal of Artificial Intelligence in Education 9 (1998) 128–158
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Morales, R., Pain, H., Conlon, T. (2000). Understandable Learner Models for a Sensorimotor Control Task. In: Gauthier, G., Frasson, C., VanLehn, K. (eds) Intelligent Tutoring Systems. ITS 2000. Lecture Notes in Computer Science, vol 1839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45108-0_26
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DOI: https://doi.org/10.1007/3-540-45108-0_26
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