Cargando…
Assessing learning engagement based on facial expression recognition in MOOC’s scenario
Online learning has become one of the most important learning styles, yet with the need of supervisors to consistently keep the learners motivated and on-task. Some learners could be supervised by outer factors, and distance learners have to be motivated by themselves. However, online learning engag...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523351/ https://www.ncbi.nlm.nih.gov/pubmed/34690439 http://dx.doi.org/10.1007/s00530-021-00854-x |
_version_ | 1784585284192567296 |
---|---|
author | Shen, Junge Yang, Haopeng Li, Jiawei Cheng, Zhiyong |
author_facet | Shen, Junge Yang, Haopeng Li, Jiawei Cheng, Zhiyong |
author_sort | Shen, Junge |
collection | PubMed |
description | Online learning has become one of the most important learning styles, yet with the need of supervisors to consistently keep the learners motivated and on-task. Some learners could be supervised by outer factors, and distance learners have to be motivated by themselves. However, online learning engagement is hardly to be assessed by supervisors in real time. With the rapid development of information technology, it is able to remedy the above problem by using intelligent video surveillance techniques. In this paper, we propose a novel framework of learning engagement assessment which introduces facial expression recognition to timely acquire the emotional changes of the learners. Moreover, a new facial expression recognition method is proposed based on domain adaptation, which is suitable for the MOOC scenario. The experiments show the effectiveness of our proposed framework on assessing learners’ learning engagement. The comparisons with the state-of-the-art methods also demonstrate the superiority of our proposed facial emotion recognition method. |
format | Online Article Text |
id | pubmed-8523351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-85233512021-10-20 Assessing learning engagement based on facial expression recognition in MOOC’s scenario Shen, Junge Yang, Haopeng Li, Jiawei Cheng, Zhiyong Multimed Syst Special Issue Paper Online learning has become one of the most important learning styles, yet with the need of supervisors to consistently keep the learners motivated and on-task. Some learners could be supervised by outer factors, and distance learners have to be motivated by themselves. However, online learning engagement is hardly to be assessed by supervisors in real time. With the rapid development of information technology, it is able to remedy the above problem by using intelligent video surveillance techniques. In this paper, we propose a novel framework of learning engagement assessment which introduces facial expression recognition to timely acquire the emotional changes of the learners. Moreover, a new facial expression recognition method is proposed based on domain adaptation, which is suitable for the MOOC scenario. The experiments show the effectiveness of our proposed framework on assessing learners’ learning engagement. The comparisons with the state-of-the-art methods also demonstrate the superiority of our proposed facial emotion recognition method. Springer Berlin Heidelberg 2021-10-19 2022 /pmc/articles/PMC8523351/ /pubmed/34690439 http://dx.doi.org/10.1007/s00530-021-00854-x Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Special Issue Paper Shen, Junge Yang, Haopeng Li, Jiawei Cheng, Zhiyong Assessing learning engagement based on facial expression recognition in MOOC’s scenario |
title | Assessing learning engagement based on facial expression recognition in MOOC’s scenario |
title_full | Assessing learning engagement based on facial expression recognition in MOOC’s scenario |
title_fullStr | Assessing learning engagement based on facial expression recognition in MOOC’s scenario |
title_full_unstemmed | Assessing learning engagement based on facial expression recognition in MOOC’s scenario |
title_short | Assessing learning engagement based on facial expression recognition in MOOC’s scenario |
title_sort | assessing learning engagement based on facial expression recognition in mooc’s scenario |
topic | Special Issue Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523351/ https://www.ncbi.nlm.nih.gov/pubmed/34690439 http://dx.doi.org/10.1007/s00530-021-00854-x |
work_keys_str_mv | AT shenjunge assessinglearningengagementbasedonfacialexpressionrecognitioninmoocsscenario AT yanghaopeng assessinglearningengagementbasedonfacialexpressionrecognitioninmoocsscenario AT lijiawei assessinglearningengagementbasedonfacialexpressionrecognitioninmoocsscenario AT chengzhiyong assessinglearningengagementbasedonfacialexpressionrecognitioninmoocsscenario |