Modeling and Evaluating Tutors' Function using Data Mining and Fuzzy Logic Techniques

Modeling and Evaluating Tutors' Function using Data Mining and Fuzzy Logic Techniques

Safia Bendjebar (LabSTIC Laboratory, Guelma University, Guelma, Algeria), Yacine Lafifi (LabSTIC Laboratory, Guelma University, Guelma, Algeria) and Hamid Seridi (LabSTIC Laboratory, Guelma University, Guelma, Algeria)
DOI: 10.4018/IJWLTT.2016040103
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Abstract

In e-learning systems, the tutors play many roles and carry out several tasks that differ from one system to another. The activity of tutoring is influenced by many factors. One factor among them is the assignment of the appropriate profile to the tutor. For this reason, the authors propose a new approach for modeling and evaluating the function of the tutors. This technique facilitates the classification among tutors for adapting tutoring to student's problems. The component of the proposed tutor model is a set of profiles which are responsible for representing the necessary information about each tutor. A fuzzy logic technique is used in order to define tutor's tutoring profile. Furthermore, the K nearest neighbor algorithm is used to offer much information for each new tutor based on the models of other similar tutors. This new approach has been tested by tutors from an Algerian University. The first results were very encouraging and sufficient. They indicate that the use of fuzzy logic technique is very useful and estimate the adaptation of the tutoring process according to tutors' skills.
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2. Literature Review

In this section, we give a brief overview about the research works that are related to our work: identification of the tutors’ functions and application of data mining techniques for modeling the users.

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