Abstract
With the aim to individualise human-computer interaction, an Intelligent Tutoring System (ITS) has to keep track of what and how the student has learned. Hence, it is necessary to maintain a Student Model (SM) dealing with complex knowledge representation, such as incomplete and inconsistent knowledge and belief revision. With this in view, the main objective of this paper is to present and discuss the student modelling approach we have adopted to implement Pitagora 2.0, an ITS based on a co-operative learning model, and designed to support teaching-learning activities in a Euclidean Geometry context. In particular, this approach has led us to develop two distinct modules that cooperate to implement the SM of Pitagora 2.0. The first module resembles a “classical” student model, in the sense that it maintains a representation of the current student knowledge level, which can be used by the teacher in order to tune its teaching strategies to the specific student needs. In addition, our system contains a second module that implements a virtual partner, called companion. This module consists of a computational model of an “average student” which cooperates with the student during the learning process. The above mentioned module calls for the use of machine learning algorithms that allow the companion to improve in parallel with the real student. Computational results obtained when testing this module in simulation experiments are also presented.
Similar content being viewed by others
References
Anderson, J.R., Boyle, C.F., Corbett, A.T. and M.W. Lewis: 1991, ‘Cognitive Modelling and Intelligent Tutoring’,Artificial Intelligence 42, 7–49.
Carbonaro, A., Casadei, G., Salomoni, P. and F. Ruggeri: 1994a, ‘Pitagora 2.0: A teaching enviroment for Euclidean Geometry’ (in Italian). In: A. Andronico, G. Casadei, G. Sacerdoti (eds.):DIDAMATICA'94. Cesena (IT): May pp. 77–85.
Carbonaro, A., Roccetti, M., Salomoni, P. and V. Maniezzo: 1994b, ‘A Bayesian Network based Student Model for Pitagora 2.0’, Technical Report n. 94/2, Cesena Laboratory for Computer Science, Cesena Italy.
Carbonell, J.G.: 1983, ‘Learning by Analogy: Formulating and Generalizing Plans from Past Experience’, In: R.S. Michalski, J.G. Carbonell and T.M. Mitchell (eds.):Machine learning: An Artificial Intelligence Approach 2. Morgan Kaufmann, San Mateo, CA pp. 371–392.
Chan, T.W. and A.B. Baskin: 1990, ‘Learning Companion Systems’, In: Frasson and Gauthier (eds.):Intelligent Tutoring Systems: At the crossroads of AI and Education. Norwood, NJ: Ablex Pub. pp. 6–33.
DeJong, G.: 1988, ‘An introduction to Explanation-Based Learning’. In: H.E. Shrobe (ed.):Exploring Artificial Intelligence: survey talks from the National Conferences on Artificial Intelligence. California, Morgan Kaufmann Publishers, pp. 45–82.
Ellman, T.: 1989, ‘Explanation-Based Learning: A survey of Programs and Perpectives’.ACM Computing Surveys,21(2), 163–221.
Hirashima, T., Nii-Tsu, T., Kashihara, A. and J. Toyoda: 1993, ‘An indexing framework for adaptive setting of problems in I.T.S.’, World Conference on Artificial Intelligence in Education 1993, (Brna P., Ohlsson S. and Pain H. (eds.), Edinburgh, Scotland, 23–27, pp. 90–97.
Hoskin, N. and R.M. Aiken: 1992, ‘Co-explainer: A Machine Learning Companion’, International Conference on Education and Society, Elsevier Science Publishers B.V., pp. 188–196.
Kambouri, M., Koppen, M., Villano, M. and J.C. Falmagne: 1994, ‘Knowledge assessment: tapping human expertise by the QUERY routine’,Int. J. Human Computer Studies 40, 119–151.
Maniezzo, V., Carbonaro, A., Casadei, G. and P. Salomoni: 1994, ‘A Co-operative Teaching Enviroment for Euclidean Geometry’, Technical Report n. 94/1, Cesena Laboratory for Computer Science, Cesena Italy, (also submitted to IFIP World Conference on Computers in Education 1995).
Minton, S. and J.G. Carbonell: 1989, ‘Explanation-Based Learning: A problem Solver Perpesctive’,Artificial Intelligence,40, 63–118.
Mitchell, T.M., Utgoff, P.E. and R.B. Banerji: 1983, ‘Learning by experimentation: Acquiring and Refining Problem -Solving Heuristics’, In: R.S. Michalski, J.G. Carbonell and T.M. Mitchell (eds.):Machine Learning. Tioga Pub., Palo Alto, CA pp. 163–190.
Morawsky, P.: 1988, ‘Understanding Bayesian Belief Networks’,AI Expert, pp. 44–48.
Mostow, D.J.: 1987, ‘Design by derivational analogy: Issue in the Automated Replay of Design Plans’,Artificial Intelligence,40, 119–184
Pearl, J.: 1988, ‘Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference’. San Mateo, CA: Morgan Kaufmann.
Petrushin, V.A. and K.M. Sinitsa: 1993, ‘Using Probabilistic Reasoning Techniques for Learner Modelling’, World Conference on AI in Education, Edinburgh, Scotland, pp. 418–425.
Self, J.A. and G. Cumming: 1989, ‘Collaborative Intelligent Educational Systems’, In: Bierman, Breuker and Sandberg (eds.):Artificial Intelligence and Education: Synthesis and Reflection, North-Holland.
Self, J.A.: 1990, ‘Bypassing the intractable problem of student modelling’. In: C. Frasson and G. Gauthier (eds.):Intelligent Tutoring System: At the crossroads of artificial intelligence and education. Ed. Norwood, Ablex NJ, pp. 107–123.
Villano, M.: 1992, ‘Probabilistic student models: bayesian belief networks and knowledge space theory’,Second International Conference Intelligent Tutoring System, Montreal, Canada, pp. 491–498.
Vygotsky, L.: 1978,Mind in Society Cambridge, MA: Harvard University Press.
Woolf, B.: 1988, ‘Intelligent Tutoring System: A survey’. In: H. E. Shrobe (ed.):Exploring Artificial Intelligence: survey talks from the National Conferences on Artificial Intelligence. California, Morgan Kaufmann Publishers, pp. 1–43.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Carbonaro, A., Maniezzo, V., Roccetti, M. et al. Modelling the student in Pitagora 2.0. User Model User-Adap Inter 4, 233–251 (1994). https://doi.org/10.1007/BF01099820
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF01099820