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Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

Stathacopoulou, R. and Magoulas, George D. and Grigoriadou, M. and Samarakou, M. (2005) Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis. Information Sciences 170 (2-4), pp. 273-307. ISSN 0020-0255.

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Official URL: http://dx.doi.org/doi:10.1016/j.ins.2004.02.026

Abstract

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments.

Item Type: Article
Keyword(s) / Subject(s): student diagnosis, uncertainty management, fuzzy logic, neural networks, student modelling, intelligent learning environments, intelligent tutoring systems, discovery learning environments, learning styles
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Sandra Plummer
Date Deposited: 10 Apr 2006
Last Modified: 17 Apr 2013 12:32
URI: http://eprints.bbk.ac.uk/id/eprint/351

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