Student modeling for a web-based learning environment: a data mining approach
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Evaluation of e-learning systems based on fuzzy clustering models and statistical tools
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Abstract: The accessibility of the World-Wide Web and the ease of use of the tools to browse the resources on the Web have made this technology extremely popular and the means of choice for distance education. Many sophisticated web-based learning environments have been developed and are in use around the world. Educators, using these environments and tools, however, have very little support to evaluate learners' activities and discriminate between different learner's on-line behaviours. In this paper, we exploit the existence of web access logs and advanced data mining techniques to extract useful patterns that can help educators and web masters evaluate and interpret on-line course activities in order to assess the learning process, track students actions and measure web course structure effectiveness.