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Subtitle: Using fuzzy logic with a maximal length matching algorithm, expressed preferences, and expert knowledge
Article type: Research Article
Authors: Van Pham, Haia; * | Thuy, Linh Hoang Thic | Hung, Nguyen Chand | Dich, Nguyen Quange | Ngoc, Son Luonge | Moore, Philipb
Affiliations: [a] School of Information and Communication Technology, Hanoi University of Science and Technology (HUST), 1 Dai Co Viet, Hai Ba Trung, Hanoi City, Vietnam | [b] School of Information Science and Engineering, Lanzhou University, Feiyun Building, Chengguan Qu, Lanahou Shi, Lanzhou, Gansu Sheng, China | [c] Foreign Trade University, Hanoi, Vietnam | [d] Institute of Control and Automation Engineering (ICEA) – Hanoi University of Science and Technology, Hanoi, Vietnam | [e] Hanoi University of Science, National University, Hanoi, Vietnam
Correspondence: [*] Corresponding author. Hai Van Pham, School of Information and Communication Technology, Hanoi University of Science and Technology (HUST), 1 Dai Co Viet, Le Dai Hanh, Hai Ba Trung, Hanoi City, 1000, Vietnam. E-mail: haipv@soict.hust.edu.vn.
Abstract: Pedagogic systems are gaining traction in the provision of training, learning, and continuing professional development (often required to maintain professional qualifications). An essential element in pedagogic systems is the matching of teachers (mentors) and students (mentees). In this paper we present an intelligent context-aware learning system based on profile criteria developed using big data analytic solutions. The proposed system is designed to provide systematic support for mentors based on student profiles. The goal of the proposed system is to match the mentor profiles with the type of pedagogic system, the student profile, the student requirements, and the student’s goals and expectations. The proposed system is predicated on the use of fuzzy logic definitions with a maximal length matching algorithm using expert knowledge. The proposed system implements a mentor (teacher) and mentee (student) matching algorithm based on their profile criteria. The proposed system has been successfully tested by matching mentor and mentee profiles and preferences. Experimental results show that the proposed system can access multi-factorial mentor and mentee profiles, effectively match suitable mentors (teachers) with appropriate mentees (students), and meet the mentee expectations.
Keywords: Mentor, Mentee, Mentoring, context awareness, profile matching, intelligent pedagogic systems
DOI: 10.3233/JIFS-223820
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4071-4087, 2023
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