Abstract
This research presents a comprehensive methodological approach to detect and analyze student engagement within the context of online education. It is supported by e-learning systems, and is based on a combination of semantic analysis, applied to the students’ posts and comments, with a machine learning-based classification, performed upon a range of data derived from the students’ usage of the online courses themselves. This is meant to provide teachers and students with information related to the relevant aspects making up the students’ engagement, such as sentiment, urgency, confusion within a given course as well as the probability for students to keep their involvement in or to drop out from the courses altogether.
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Acknowledgements
This work has been supported by both the project colMOOC “Integrating Conversational Agents and Learning Analytics in MOOCs”, co-funded by the European Commission within the Erasmus+ program (ref. 588438-EPP-1-2017-1-EL-EPPKA2-KA) and the CNPq (National Center for Scientific and Technological Development) of the Brazil Government.
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Toti, D., Capuano, N., Campos, F., Dantas, M., Neves, F., Caballé, S. (2021). Detection of Student Engagement in e-Learning Systems Based on Semantic Analysis and Machine Learning. In: Barolli, L., Takizawa, M., Yoshihisa, T., Amato, F., Ikeda, M. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2020. Lecture Notes in Networks and Systems, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-61105-7_21
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