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.
Lingua originale | English |
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Titolo della pubblicazione ospite | Lecture Notes in Networks and Systems |
Pagine | 211-223 |
Numero di pagine | 13 |
Volume | 158 |
DOI | |
Stato di pubblicazione | Pubblicato - 2021 |
Evento | 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 Durata: 28 ott 2020 → 30 ott 2020 |
Convegno
Convegno | 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 |
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Città | jpn |
Periodo | 28/10/20 → 30/10/20 |
Keywords
- machine learning
- semantic analysis