Classroom Behavior Recognition Using Computer Vision: A Systematic Review
Behavioral computing based on visual cues has become increasingly important, as it can capture and annotate teachers’ and students’ classroom states on a large scale and in real time. However, there is a lack of consensus on the research status and future trends of computer vision-based classroom be...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/373 |
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| author | Qingtang Liu Xinyu Jiang Ruyi Jiang |
| author_facet | Qingtang Liu Xinyu Jiang Ruyi Jiang |
| author_sort | Qingtang Liu |
| collection | DOAJ |
| description | Behavioral computing based on visual cues has become increasingly important, as it can capture and annotate teachers’ and students’ classroom states on a large scale and in real time. However, there is a lack of consensus on the research status and future trends of computer vision-based classroom behavior recognition. The present study conducted a systematic literature review of 80 peer-reviewed journal articles following the Preferred Reporting Items for Systematic Assessment and Meta-Analysis (PRISMA) guidelines. Three research questions were addressed concerning goal orientation, recognition techniques, and research challenges. Results showed that: (1) computer vision-supported classroom behavior recognition focused on four categories: physical action, learning engagement, attention, and emotion. Physical actions and learning engagement have been the primary recognition targets; (2) behavioral categorizations have been defined in various ways and lack connections to instructional content and events; (3) existing studies have focused on college students, especially in a natural classical classroom; (4) deep learning was the main recognition method, and the YOLO series was applicable for multiple behavioral purposes; (5) moreover, we identified challenges in experimental design, recognition methods, practical applications, and pedagogical research in computer vision. This review will not only inform the recognition and application of computer vision to classroom behavior but also provide insights for future research. |
| format | Article |
| id | doaj-art-616dba73dba74251922bcd8f33ac94ce |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-616dba73dba74251922bcd8f33ac94ce2025-01-24T13:48:41ZengMDPI AGSensors1424-82202025-01-0125237310.3390/s25020373Classroom Behavior Recognition Using Computer Vision: A Systematic ReviewQingtang Liu0Xinyu Jiang1Ruyi Jiang2Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, ChinaFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, ChinaFaculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, ChinaBehavioral computing based on visual cues has become increasingly important, as it can capture and annotate teachers’ and students’ classroom states on a large scale and in real time. However, there is a lack of consensus on the research status and future trends of computer vision-based classroom behavior recognition. The present study conducted a systematic literature review of 80 peer-reviewed journal articles following the Preferred Reporting Items for Systematic Assessment and Meta-Analysis (PRISMA) guidelines. Three research questions were addressed concerning goal orientation, recognition techniques, and research challenges. Results showed that: (1) computer vision-supported classroom behavior recognition focused on four categories: physical action, learning engagement, attention, and emotion. Physical actions and learning engagement have been the primary recognition targets; (2) behavioral categorizations have been defined in various ways and lack connections to instructional content and events; (3) existing studies have focused on college students, especially in a natural classical classroom; (4) deep learning was the main recognition method, and the YOLO series was applicable for multiple behavioral purposes; (5) moreover, we identified challenges in experimental design, recognition methods, practical applications, and pedagogical research in computer vision. This review will not only inform the recognition and application of computer vision to classroom behavior but also provide insights for future research.https://www.mdpi.com/1424-8220/25/2/373computer visionoffline classroomteaching behaviorlearning behaviorbehavior recognition |
| spellingShingle | Qingtang Liu Xinyu Jiang Ruyi Jiang Classroom Behavior Recognition Using Computer Vision: A Systematic Review Sensors computer vision offline classroom teaching behavior learning behavior behavior recognition |
| title | Classroom Behavior Recognition Using Computer Vision: A Systematic Review |
| title_full | Classroom Behavior Recognition Using Computer Vision: A Systematic Review |
| title_fullStr | Classroom Behavior Recognition Using Computer Vision: A Systematic Review |
| title_full_unstemmed | Classroom Behavior Recognition Using Computer Vision: A Systematic Review |
| title_short | Classroom Behavior Recognition Using Computer Vision: A Systematic Review |
| title_sort | classroom behavior recognition using computer vision a systematic review |
| topic | computer vision offline classroom teaching behavior learning behavior behavior recognition |
| url | https://www.mdpi.com/1424-8220/25/2/373 |
| work_keys_str_mv | AT qingtangliu classroombehaviorrecognitionusingcomputervisionasystematicreview AT xinyujiang classroombehaviorrecognitionusingcomputervisionasystematicreview AT ruyijiang classroombehaviorrecognitionusingcomputervisionasystematicreview |