Detecting learning affect in e-learning platform using facial emotion expression

Benisemeni Esther Zakka; Hima Vadapalli

doi:10.1007/978-3-030-49345-5_23

Detecting learning affect in e-learning platform using facial emotion expression

Benisemeni Esther Zakka
, Hima Vadapalli

University of the Witwatersrand

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

3 Citations (Scopus)

Abstract

Recent trends in education have shifted from traditional classroom learning to an online learning setting; however, research has indicated a high drop out rate among e-learners. Boredom, lack of motivation are among the factors that led to this decline. This study develops a platform that provides feedback to learners in real-time while engaging in an online learning video. The platform detects, predicts and analyses the facial emotions of a learner using Convolutional Neural Network (CNN), and further maps the emotion to a learning affect. The feedback generated provides a reasonable understanding of the comprehension level of the learner.

Original language	English
Title of host publication	Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019
Editors	Ajith Abraham, M.A. Jabbar, Sanju Tiwari, Isabel M.S. Jesus
Publisher	Springer
Pages	217-225
Number of pages	9
ISBN (Print)	9783030493448
DOIs	https://doi.org/10.1007/978-3-030-49345-5_23
Publication status	Published - 2021
Externally published	Yes
Event	11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019 - Hyderabad, India Duration: 13 Dec 2019 → 15 Dec 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1182 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019
Country/Territory	India
City	Hyderabad
Period	13/12/19 → 15/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Keywords

Convolutional neural network
Facial emotion recognition
e-learning

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-030-49345-5_23

Cite this

Zakka, B. E., & Vadapalli, H. (2021). Detecting learning affect in e-learning platform using facial emotion expression. In A. Abraham, M. A. Jabbar, S. Tiwari, & I. M. S. Jesus (Eds.), Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019 (pp. 217-225). (Advances in Intelligent Systems and Computing; Vol. 1182 AISC). Springer. https://doi.org/10.1007/978-3-030-49345-5_23

Zakka, Benisemeni Esther ; Vadapalli, Hima. / Detecting learning affect in e-learning platform using facial emotion expression. Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019. editor / Ajith Abraham ; M.A. Jabbar ; Sanju Tiwari ; Isabel M.S. Jesus. Springer, 2021. pp. 217-225 (Advances in Intelligent Systems and Computing).

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title = "Detecting learning affect in e-learning platform using facial emotion expression",

abstract = "Recent trends in education have shifted from traditional classroom learning to an online learning setting; however, research has indicated a high drop out rate among e-learners. Boredom, lack of motivation are among the factors that led to this decline. This study develops a platform that provides feedback to learners in real-time while engaging in an online learning video. The platform detects, predicts and analyses the facial emotions of a learner using Convolutional Neural Network (CNN), and further maps the emotion to a learning affect. The feedback generated provides a reasonable understanding of the comprehension level of the learner.",

keywords = "Convolutional neural network, Facial emotion recognition, e-learning",

author = "Zakka, \{Benisemeni Esther\} and Hima Vadapalli",

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Zakka, BE & Vadapalli, H 2021, Detecting learning affect in e-learning platform using facial emotion expression. in A Abraham, MA Jabbar, S Tiwari & IMS Jesus (eds), Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019. Advances in Intelligent Systems and Computing, vol. 1182 AISC, Springer, pp. 217-225, 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019, Hyderabad, India, 13/12/19. https://doi.org/10.1007/978-3-030-49345-5_23

Detecting learning affect in e-learning platform using facial emotion expression. / Zakka, Benisemeni Esther; Vadapalli, Hima.
Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019. ed. / Ajith Abraham; M.A. Jabbar; Sanju Tiwari; Isabel M.S. Jesus. Springer, 2021. p. 217-225 (Advances in Intelligent Systems and Computing; Vol. 1182 AISC).

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

TY - GEN

T1 - Detecting learning affect in e-learning platform using facial emotion expression

AU - Zakka, Benisemeni Esther

AU - Vadapalli, Hima

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

PY - 2021

Y1 - 2021

N2 - Recent trends in education have shifted from traditional classroom learning to an online learning setting; however, research has indicated a high drop out rate among e-learners. Boredom, lack of motivation are among the factors that led to this decline. This study develops a platform that provides feedback to learners in real-time while engaging in an online learning video. The platform detects, predicts and analyses the facial emotions of a learner using Convolutional Neural Network (CNN), and further maps the emotion to a learning affect. The feedback generated provides a reasonable understanding of the comprehension level of the learner.

AB - Recent trends in education have shifted from traditional classroom learning to an online learning setting; however, research has indicated a high drop out rate among e-learners. Boredom, lack of motivation are among the factors that led to this decline. This study develops a platform that provides feedback to learners in real-time while engaging in an online learning video. The platform detects, predicts and analyses the facial emotions of a learner using Convolutional Neural Network (CNN), and further maps the emotion to a learning affect. The feedback generated provides a reasonable understanding of the comprehension level of the learner.

KW - Convolutional neural network

KW - Facial emotion recognition

KW - e-learning

UR - https://www.scopus.com/pages/publications/85089716000

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T3 - Advances in Intelligent Systems and Computing

SP - 217

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BT - Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019

A2 - Abraham, Ajith

A2 - Jabbar, M.A.

A2 - Tiwari, Sanju

A2 - Jesus, Isabel M.S.

PB - Springer

T2 - 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019

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Zakka BE, Vadapalli H. Detecting learning affect in e-learning platform using facial emotion expression. In Abraham A, Jabbar MA, Tiwari S, Jesus IMS, editors, Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2019. Springer. 2021. p. 217-225. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-49345-5_23

Detecting learning affect in e-learning platform using facial emotion expression

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this