Abstract
Our study analyses learning data in the context of massive open online courses (MOOCs) that are growing in popularity, but their effectiveness and learner completion rate are often criticized. Our goal is to improve students’ completion rate by analyzing their behaviors and level of engagement. Log data generated by students’ interactions with didactic activities are used to predict student dropout, and thus improve pedagogical quality. Different machine learning approaches, artificial neural networks, SVMs, and decision trees are used for this analysis. The use of artificial intelligence models makes it possible to personalize the learning experience, detect anomalies, and take appropriate action. The three classification models used show a high accuracy, reaching almost 99% and the mean square error is very low.
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Mohamed, E.G., Issam, A., Mohamed, T. (2024). Data Analysis and Machine Learning for MOOC Optimization. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 938. Springer, Cham. https://doi.org/10.1007/978-3-031-54376-0_33
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