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Article Dans Une Revue International journal of technology enhanced learning Année : 2018

Semi-Supervised Algorithm with Knowledge-based Features for Learner's Profiles Interoperability

Résumé

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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Dates et versions

hal-02562112 , version 1 (04-05-2020)

Identifiants

Citer

Leïla Ghorbel, Corinne Amel Zayani, Ikram Amous, Florence Sèdes. Semi-Supervised Algorithm with Knowledge-based Features for Learner's Profiles Interoperability. International journal of technology enhanced learning, 2018, 10 (1/2), pp.137-159. ⟨10.1504/IJTEL.2018.088343⟩. ⟨hal-02562112⟩
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