Title: Semi-supervised algorithm with knowledge-based features for learner's profiles interoperability
Authors: Leila Ghorbel; Corinne Amel Zayani; Ikram Amous; Florence Sèdes
Addresses: Department of Computer Science, MIRACL-ISIMS Sfax University, Tunis Road Km 10, 3021 Sfax, Tunisia ' Department of Computer Science, MIRACL-ISIMS Sfax University, Tunis Road Km 10, 3021 Sfax, Tunisia ' Department of Computer Science, MIRACL-ISIMS Sfax University, Tunis Road Km 10, 3021 Sfax, Tunisia ' Department of Computer Science, Paul Sabatier University, IRIT Laboratory, Toulouse, France
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.
Keywords: e-learning; interoperability; knowledge-based features; learner's profiles; mapping; matching; Tri-Training.
International Journal of Technology Enhanced Learning, 2018 Vol.10 No.1/2, pp.137 - 159
Available online: 23 Oct 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article