Abstract
The high rates of learners’ dropout and failures in different courses that are offered by many universities and educational institutions through the use of e-learning or online learning systems have been a serious concern. Analyzing and studying learners’ learning data in order to predict their future performance can support both tutors and e-learning systems to determine learners’ progress or status and spot those with low performance. Thus they can offer learners with personalized learning resources and activities designed to each one in order to maximize their learning outcomes and overcome their learning gaps. This paper presents a methodology that uses semantic web technologies as well as data mining techniques to predict learners’ future performance based on data produced by learners through their interaction with LMS (Learning Management System) and social networks.
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El-Rady, A.A., Shehab, M., El Fakharany, E. (2017). Predicting Learner Performance Using Data-Mining Techniques and Ontology. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_63
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