International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 8, No. 3, 8 Mar. 2016
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Learner Modeling in Adaptive Educational Systems: A Comparative Study
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Author(s)
Mouenis Anouar Tadlaoui
1,*
Souhaib Aammou
1
Mohamed Khaldi
1
Rommel Novaes Carvalho
2
Index Terms
Learner model, Adaptive Educational Systems, Overlay model, Stereotypes, Bayesian networks
Abstract
It’s worth noting that the present paper lies within the range of modeling the learner in adaptive educational system as a conceptual modeling of the learner. Thought they are several methods that deal with the learner model; like stereotypes methods or learner profile…, but they are likely unable to handle the uncertainty embedded in the dynamic modeling of the learner. The present paper aims at studding different models and approaches to model the learner in an adaptive educational systems, and coming up with the most appropriate method based on the dynamic aspect of this model.
The aim of this study is the argue that the learner model cannot be completely modeled based on one single method through the entire development process, but it needs a combination between several methods that will help for a complete modeling.
Cite This Paper
Mouenis Anouar Tadlaoui, Souhaib Aammou, Mohamed Khaldi, Rommel Novaes Carvalho, "Learner Modeling in Adaptive Educational Systems: A Comparative Study", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.3, pp.1-10, 2016. DOI:10.5815/ijmecs.2016.03.01
Reference
[1]Nicola Henze, Wolfgang Nejdl. A Logical Characterization of Adaptive Educational Hypermedia. New Review of Hypermedia and Multimedia (NRHM), 10 (1), 77-113. 2004.
[2]Judy Kay. User Interfaces for All, chapter User Modeling for Adaptation, P.p. 271–294. Human Factors Series. Lawrence Erlbaum Associates, Inc., 2000.
[3]Nora Koch. Software Engineering for Adaptive Hypermedia Systems. PhD thesis, Ludwig-Maximilians- University Munich/Germany, 2000.
[4]Peter Brusilovsky. Adaptive Hypermedia. User Modeling and User-Adapted Interaction, vol. 11, no. 1–2 p.p. 87–110, 2001.
[5]Ok Park and Jung Lee. Handbook of Research for Educational Communications and Technology, chapter Adaptive Instructional Systems, P.p. 651–660. Association for Educational Communications and Technology, 2003
[6]Yujian Zhou and Martha W. Evens. A Practical Student Model in an Intelligent Tutoring System. In Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’99), P.p. 13–18, 1999.
[7]Zoran Jeremi′c and Vladan Devedˇzi′c. Design Pattern ITS: Student Model Implementation. In Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT’04), P.p. 864–865, 2004.
[8]Gladys Castillo, Joao Gama, and Ana M. Breda. Adaptive Bayes for a Student Modeling Prediction Task based on Learning Styles. In Proceedings of the 9th International Conference on User Modeling (UM’03), P.p. 328–332, 2003.
[9]John Self. Student Modelling: the key to individualize knowledgebased instruction, chapter Formal Approaches to Student Modelling, P.p. 295– 352. Springer-Verlag Berlin, 1994.
[10]Binglan Han. Student Modelling and Adaptivity in web based Learning Systems. Master’s thesis, Massey University/New Zealand, 2001.
[11]Geoffrey I. Webb, Michael J. Pazzani, and Daniel Billsus. Machine Learning for User Modeling. User Models User-Adapted Interaction, vol. 11, no. 1-2 p.p. 19–29, 2001.
[12]Alfred Kobsa. User Modeling: Recent Work, Prospects and Hazards. Department of Computer Science, Columbia University, New York, USA. 1993.
[13]Pearl J. (1988). Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann.
[14]Mayo M., Mitrovic A. (2001). Optimising ITS behaviour with Bayesian networks and decision theory, International Journal of Artificial Intelligence in Education, n 12, pp 124-153.
[15]M. Anouar Tadlaoui, M. Khaldi, S. Aammou (2014) Towards a Learning model based on Bayesian Networks,EDULEARN14 Proceedings, pp. 3185-3193.
[16]M. Anouar Tadlaoui, M. Khaldi, S. Aammou (2014) Bayesian Networks for Learner Modeling, International Journal of Basic Sciences and Applied Computing 1 (1), 5-9.