Abstract
The emergence of COVID-19 has shaped a new type of learning that is called “digital learning”, which can be approached through e-learning and m-learning technologies. An important element in reducing student dropout rates in a virtual learning is to understand the degree of engagement and satisfaction experienced by students in meaningful activities. The major novel contribution of this study is the combination of network Quality of Service (QoS) parameters and student engagement behavior (through eye-tracking) to automatically estimate the Mean Opinion Score (MOS) for mobile learning synchronous activities. Several machine learning algorithms were compared for best QoE prediction.
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Korchani, B., Sethom, K. (2022). Machine Learning for Student QoE Prediction in Mobile Learning During COVID-19. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_2
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DOI: https://doi.org/10.1007/978-3-030-99619-2_2
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