| Autor: |
Ghorbel, Leila, Zayani, Corinne Amel, Amous, Ikram, SÁ¨des, Florence |
| Zdroj: |
International Journal of Technology Enhanced Learning; 2018, Vol. 10 Issue: 1-2 p137-159, 23p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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