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

doi:10.1007/978-3-030-61105-7_21

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

University of Basilicata
Universidade Federal de Juiz de Fora
Open University of Catalonia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (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.

Original language	English
Title of host publication	Lecture Notes in Networks and Systems
Pages	211-223
Number of pages	13
Volume	158
DOIs	https://doi.org/10.1007/978-3-030-61105-7_21
Publication status	Published - 2021
Event	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 Duration: 28 Oct 2020 → 30 Oct 2020

Conference

Conference	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
City	jpn
Period	28/10/20 → 30/10/20

Keywords

machine learning
semantic analysis

Access to Document

10.1007/978-3-030-61105-7_21

Cite this

@inproceedings{be13d121c92f4116a07d33516ae4298c,

title = "Detection of Student Engagement in e-Learning Systems Based on Semantic Analysis and Machine Learning",

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{\textquoteright} posts and comments, with a machine learning-based classification, performed upon a range of data derived from the students{\textquoteright} 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{\textquoteright} 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.",

keywords = "machine learning, semantic analysis, machine learning, semantic analysis",

author = "Daniele Toti and Nicola Capuano and Fernanda Campos and Mario Dantas and Felipe Neves and Santi Caball{\'e}",

year = "2021",

doi = "10.1007/978-3-030-61105-7\_21",

language = "English",

isbn = "978-3-030-61104-0",

volume = "158",

pages = "211--223",

booktitle = "Lecture Notes in Networks and Systems",

note = "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 ; Conference date: 28-10-2020 Through 30-10-2020",

}

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 Lecture Notes in Networks and Systems. vol. 158, pp. 211-223, 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, 28/10/20. https://doi.org/10.1007/978-3-030-61105-7_21

TY - GEN

T1 - Detection of Student Engagement in e-Learning Systems Based on Semantic Analysis and Machine Learning

AU - Toti, Daniele

AU - Capuano, Nicola

AU - Campos, Fernanda

AU - Dantas, Mario

AU - Neves, Felipe

AU - Caballé, Santi

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - machine learning

KW - semantic analysis

KW - machine learning

KW - semantic analysis

UR - http://hdl.handle.net/10807/165853

U2 - 10.1007/978-3-030-61105-7_21

DO - 10.1007/978-3-030-61105-7_21

M3 - Conference contribution

SN - 978-3-030-61104-0

VL - 158

SP - 211

EP - 223

BT - Lecture Notes in Networks and Systems

T2 - 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

Y2 - 28 October 2020 through 30 October 2020

ER -

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this