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(2005). Neurofuzzy knowledge processing in intelligent learning environments for improved student diagnosis. Information Sciences 170 (2-4) 273-307. This is an author-produced version of a paper published in Information Sciences . This version has been peer-reviewed but does not include the final publisher proof corrections, published layout or pagination.All articles available through Birkbeck ePrints are protected by intellectual property law, including copyright law. Any use made of the contents should comply with the relevant law. 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 user 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.2

The idea of developing educational hypermedia systems for the Web is very challenging, and demands the synergy of computer science and instructional science. The paper builds on theories from instructional design and learning styles to develop a design rational and guidelines for adaptive web-based learning systems that use individual differences as a basis of system's adaptation. Various examples are provided to illustrate how instructional manipulations with regards to content adaptation and presentation, and adaptive navigation support, as well as the overall degree of system adaptation are guided by educational experiences geared towards individual differences.

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