Semi-supervised algorithm with knowledge-based features for learner's profiles interoperability Online publication date: Mon, 04-Dec-2017
by Leila Ghorbel; Corinne Amel Zayani; Ikram Amous; Florence Sèdes
International Journal of Technology Enhanced Learning (IJTEL), Vol. 10, No. 1/2, 2018
Abstract: Nowadays, the user can have several profiles found in different adaptive systems relative to various fields. In particular, adaptive e-learning systems respond to a strong need to adapt to each learner their proposed activities based on the data stored in his/her profile (learning-style, interest, etc.). However, each system can have incomplete data as far as the learner is concerned. Hence, the exchange of the learner's profile data is extremely important in order to enhance his/her learning experience. The exchange requires a matching process so as to resolve the large number of a learner's profiles differences whether in syntax, structure or semantics. In this context, we propose a matching process to automatically detect the similarity between the profile elements. The originality of this process resides in the fact that it rests on a new semi-supervised Tri-Training algorithm which significantly improves the state of the art approaches.
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