Determination of Distant Learner’s Sociological Profile Based on Fuzzy Logic and Naïve Bayes Techniques


  • Youness Chaabi
  • Lekdioui Khadija
  • Fatine Jebbor
  • Rochdi Messoussi



Collaborative learning, Multi-Agent System, Fuzzy logic, Naïve Bayes, Speech act, Social Profiles


The present article is elaborated in the context of e-learning softwares that provide assistance and functionalities to learners engaged in distance learning. Our contribution consists of a system that estimates a behavioral (sociological) profile for each student. This estimation is based on automatic analysis of students’ textual asynchronous conversations. In general, the automatic analysis of textual conversations is based on speech acts for classification and categorization of messages. This technique has several disadvantages like the absence of standardization of speech acts for determining social behaviors of learners. To overcome this, we propose a multi-agent system based on fuzzy logic reasoning and supervised learning technique for automatic classification and categorization of textual conversation. The determined profiles are proposed to teachers to provide them assistance during tutoring tasks. The objective of this article is to share our reflections around these issues by presenting our experience in the analysis of asynchronous online discussion forums. In this paper, we specifically propose (i) definitions for the used sociological profiles and (ii) introduce the architecture of the Multi-Agent System (MAS) that determines the profiles. The system was experimented with the students of the Master Program “Software Quality” in the Ibn Tofail University. The results obtained from this experience, presented and discussed in this paper, show that the proposed approach can be of interest.




How to Cite

Chaabi, Y., Khadija, L., Jebbor, F., & Messoussi, R. (2017). Determination of Distant Learner’s Sociological Profile Based on Fuzzy Logic and Naïve Bayes Techniques. International Journal of Emerging Technologies in Learning (iJET), 12(10), pp. 56–75.