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

Daniele Toti, Nicola Capuano, Fernanda Campos, Mario Dantas, Felipe Neves, Santi Caballé

Risultato della ricerca: Contributo in libroContributo a convegno

1 Citazioni (Scopus)

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 originaleEnglish
Titolo della pubblicazione ospiteLecture Notes in Networks and Systems
Pagine211-223
Numero di pagine13
Volume158
DOI
Stato di pubblicazionePubblicato - 2020
Evento15th 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 202030 ott 2020

Convegno

Convegno15th 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
Cittàjpn
Periodo28/10/2030/10/20

Keywords

  • machine learning
  • semantic analysis

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