Student’s Engagement Detection Based on Computer Vision: A Systematic Literature Review
Student engagement detection is receiving increasing attention due to its crucial role in influencing academic performance and learning outcomes. As learning environments increasingly shift toward online and hybrid modes, understanding and monitoring student engagement through automated means has be...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11119520/ |
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| author | Ikram Qarbal Nawal Sael Sara Ouahabi |
| author_facet | Ikram Qarbal Nawal Sael Sara Ouahabi |
| author_sort | Ikram Qarbal |
| collection | DOAJ |
| description | Student engagement detection is receiving increasing attention due to its crucial role in influencing academic performance and learning outcomes. As learning environments increasingly shift toward online and hybrid modes, understanding and monitoring student engagement through automated means has become an essential focus in educational technology. Computer vision techniques offer promising capabilities for detecting and interpreting student behaviors indicative of engagement. This paper presents a systematic literature review (SLR) on engagement detection in learning environments using computer vision methods. The objective of this study is to examine and categorize the types of student engagement detected, the learning contexts in which detection occurs, and the methods employed for such detection. Specifically, we analyze the types of data sources, datasets, and features used, as well as the preprocessing and feature engineering techniques applied to enhance model accuracy. Furthermore, we investigate the types of machine learning/Deep learning models adopted and how their performance is evaluated. Based on the findings from selected studies, this review aims to identify key contributions, existing challenges, and potential directions for future research in the domain of automated student engagement detection. |
| format | Article |
| id | doaj-art-3a8ccb6f11cf406e8cd3620c59d829d5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3a8ccb6f11cf406e8cd3620c59d829d52025-08-20T03:05:46ZengIEEEIEEE Access2169-35362025-01-011314051914054510.1109/ACCESS.2025.359688511119520Student’s Engagement Detection Based on Computer Vision: A Systematic Literature ReviewIkram Qarbal0https://orcid.org/0009-0009-8609-694XNawal Sael1https://orcid.org/0000-0002-8134-3886Sara Ouahabi2https://orcid.org/0000-0001-6478-7218Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M’Sick, Hassan II University of Casablanca, Casablanca, MoroccoLaboratory of Information Technology and Modeling, Faculty of Sciences Ben M’Sick, Hassan II University of Casablanca, Casablanca, MoroccoLaboratory of Information Technology and Modeling, Faculty of Sciences Ben M’Sick, Hassan II University of Casablanca, Casablanca, MoroccoStudent engagement detection is receiving increasing attention due to its crucial role in influencing academic performance and learning outcomes. As learning environments increasingly shift toward online and hybrid modes, understanding and monitoring student engagement through automated means has become an essential focus in educational technology. Computer vision techniques offer promising capabilities for detecting and interpreting student behaviors indicative of engagement. This paper presents a systematic literature review (SLR) on engagement detection in learning environments using computer vision methods. The objective of this study is to examine and categorize the types of student engagement detected, the learning contexts in which detection occurs, and the methods employed for such detection. Specifically, we analyze the types of data sources, datasets, and features used, as well as the preprocessing and feature engineering techniques applied to enhance model accuracy. Furthermore, we investigate the types of machine learning/Deep learning models adopted and how their performance is evaluated. Based on the findings from selected studies, this review aims to identify key contributions, existing challenges, and potential directions for future research in the domain of automated student engagement detection.https://ieeexplore.ieee.org/document/11119520/Computer visiondeep learningmachine learningstudent engagement detectionsystematic literature review |
| spellingShingle | Ikram Qarbal Nawal Sael Sara Ouahabi Student’s Engagement Detection Based on Computer Vision: A Systematic Literature Review IEEE Access Computer vision deep learning machine learning student engagement detection systematic literature review |
| title | Student’s Engagement Detection Based on Computer Vision: A Systematic Literature Review |
| title_full | Student’s Engagement Detection Based on Computer Vision: A Systematic Literature Review |
| title_fullStr | Student’s Engagement Detection Based on Computer Vision: A Systematic Literature Review |
| title_full_unstemmed | Student’s Engagement Detection Based on Computer Vision: A Systematic Literature Review |
| title_short | Student’s Engagement Detection Based on Computer Vision: A Systematic Literature Review |
| title_sort | student x2019 s engagement detection based on computer vision a systematic literature review |
| topic | Computer vision deep learning machine learning student engagement detection systematic literature review |
| url | https://ieeexplore.ieee.org/document/11119520/ |
| work_keys_str_mv | AT ikramqarbal studentx2019sengagementdetectionbasedoncomputervisionasystematicliteraturereview AT nawalsael studentx2019sengagementdetectionbasedoncomputervisionasystematicliteraturereview AT saraouahabi studentx2019sengagementdetectionbasedoncomputervisionasystematicliteraturereview |