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.

Toti, D., Capuano, N., Campos, F., Dantas, M., Neves, F., Caballe, S., Detection of Student Engagement in e-Learning Systems Based on Semantic Analysis and Machine Learning, Paper, in Lecture Notes in Networks and Systems, (jpn, 28-30 October 2020), Springer Science and Business Media Deutschland GmbH, Berlin 2021:158 211-223. 10.1007/978-3-030-61105-7_21 [http://hdl.handle.net/10807/165853]

Detection of Student Engagement in e-Learning Systems Based on Semantic Analysis and Machine Learning

Toti, D.
Primo
;
2021

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.
Inglese
Lecture Notes in Networks and Systems
15th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2020, held in conjunction with the 15th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2020
jpn
Paper
28-ott-2020
30-ott-2020
978-3-030-61104-0
Springer Science and Business Media Deutschland GmbH
Toti, D., Capuano, N., Campos, F., Dantas, M., Neves, F., Caballe, S., Detection of Student Engagement in e-Learning Systems Based on Semantic Analysis and Machine Learning, Paper, in Lecture Notes in Networks and Systems, (jpn, 28-30 October 2020), Springer Science and Business Media Deutschland GmbH, Berlin 2021:158 211-223. 10.1007/978-3-030-61105-7_21 [http://hdl.handle.net/10807/165853]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/165853
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