New approaches in Intelligent Tutoring Systems imply a more active participation of the learner in the learning process. The motivation of the learner can be increased by interaction with a companion who strengthens the knowledge acquisition in a cooperation climate. In this article we introduce a new learning strategy called learning by disturbing intended to improve student self-confidence. We compare it to directive learning and peer learning, discussing the advantage and the inconvenience of each one. We present some experiments realized to show in which condition a strategy can be useful or not. We analyze and discuss results obtained.
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References
1.
HoltP.DubsS.JonesM.GreerJ., The State of Student Model, in Student Modelling: The Key to Individualized Knowledge-Based Instruction Montreal, GreerJ.McCallaG. (eds.), NATO ASI Serie, 125, pp. 3–35, 1991.
2.
GilmoreD.SelfJ., The Application of Machine Learning to Intelligent Tutoring Systems, in Artificial Intelligence and Human Learning, Intelligent Computer-Assisted Instruction, SelfJ. (ed.), Chapman and Hall, New York, pp. 179–196, 1988.
3.
ChanT. W.BaskinA. B., Learning Companion Systems, in Intelligent Tutoring Systems: At the Crossroads of Artificial Intelligence and Education, Chapter 1, FrassonC.GauthierG. (eds.), Ablex Publishing Corporation, New Jersey, 1990.
4.
PalthepuS.GreerJ.McCallaG., Learning by Teaching, The Proceedings of the International Conference on the Learning Sciences, AACE, 1991.
5.
Van LehnK.OhlssonS.NasonR., Application of Simulated Students: An Exploration, Journal of Artificial Intelligence in Education, 5:2, pp. 135–175, 1994.
6.
ChiM. T. H.BassokM.LewisM. W.ReimannP.GlaserR., Self Explanations: How Students Study and Use Examples in Learning to Solve Problems, Cognitive Science, 5, pp. 121–152, 1989.
7.
FrassonC.KaltenbachM., Strengthening the Novice-Expert Shift using the Self-Explanation Effect, Journal of Artificial Intelligence in Education, special issue on student modeling, 3:4, pp. 477–494, 1993.
8.
SelfJ., A Perspective on Intelligent Computer-Assisted Learning, Journal of Computer Assisted Learning, 1, pp. 159–166, 1985.
9.
ChanT. W.ChungY. L.HoR. G.HouW. J.LinG. L., Distributed Learning Companion Systems—WEST Revisited. The 2nd International Conference of Intelligent Tutoring Systems, Lecture Notes in Computer Science, 608, Springer-Verlag, pp. 643–650, 1992.
10.
AïmeurE.FrassonC.Sthiaru-AlexeC., Towards New Learning Strategies in Intelligent Tutoring Systems, Proceedings of Brazilian Conference of Artificial Intelligence SBIA '95, 1995.
11.
GagnéR. M., The Conditions of Learning (4th Edition), Les éditions HRW Ltée, Montréal, 1984.
12.
AïmeurE.FrassonC., Eliciting the Learning Context in Co-Operative Tutoring Systems, IJCAI-95 Workshop on Modelling Context in Knowledge Representation and Reasoning, August, pp. 1–11, 1995.
13.
BellengerL., La confiance en soi, ESF éditeurs, 1994.
14.
KolodnerJ., Case Based Reasoning, Morgan Kaufman, San Mateo, California, 1993.
15.
GecseiJ.FrassonC., SAFARI: An Environment for Creating Tutoring Systems in Industrial Training, Ed-Media '94, International Conference, AACE, Vancouver, 1994.
