Automatic classification of activities in classroom videos
Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and anal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X24000080 |
| _version_ | 1827221668981899264 |
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| author | Jonathan K. Foster Matthew Korban Peter Youngs Ginger S. Watson Scott T. Acton |
| author_facet | Jonathan K. Foster Matthew Korban Peter Youngs Ginger S. Watson Scott T. Acton |
| author_sort | Jonathan K. Foster |
| collection | DOAJ |
| description | Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed fairly-well in detecting instructional activities, at diverse levels of complexity, as compared to human raters. For instance, one neural network achieved over 80% accuracy in detecting four common activity types: whole class activity, small group activity, individual activity, and transition. An issue that was not addressed in this study was whether the fine-grained and agnostic instructional activities detected by the neural networks could scale up to supply information about features of instructional quality. Future applications of these neural networks may enable more efficient cataloguing and analysis of classroom videos at scale and the generation of fine-grained data about the classroom environment to inform potential implications for teaching and learning. |
| first_indexed | 2024-03-08T12:30:24Z |
| format | Article |
| id | doaj.art-a1aa5a000ae7414381145fbcf09154b1 |
| institution | Directory Open Access Journal |
| issn | 2666-920X |
| language | English |
| last_indexed | 2025-03-21T16:26:16Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj.art-a1aa5a000ae7414381145fbcf09154b12024-06-17T05:59:17ZengElsevierComputers and Education: Artificial Intelligence2666-920X2024-06-016100207Automatic classification of activities in classroom videosJonathan K. Foster0Matthew Korban1Peter Youngs2Ginger S. Watson3Scott T. Acton4Department of Educational Theory and Practice, University at Albany, United States; Corresponding author.C.L. Brown Department of Electrical and Computer Engineering, University of Virginia, United StatesDepartment of Curriculum, Instruction, and Special Education University of Virginia, United StatesVirginia Modeling, Analysis, and Simulation Center, Old Dominion University, United StatesC.L. Brown Department of Electrical and Computer Engineering, University of Virginia, United StatesClassroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed fairly-well in detecting instructional activities, at diverse levels of complexity, as compared to human raters. For instance, one neural network achieved over 80% accuracy in detecting four common activity types: whole class activity, small group activity, individual activity, and transition. An issue that was not addressed in this study was whether the fine-grained and agnostic instructional activities detected by the neural networks could scale up to supply information about features of instructional quality. Future applications of these neural networks may enable more efficient cataloguing and analysis of classroom videos at scale and the generation of fine-grained data about the classroom environment to inform potential implications for teaching and learning.http://www.sciencedirect.com/science/article/pii/S2666920X24000080Elementary educationClassroom videoClassroom activity recognitionNeural networksComputer vision |
| spellingShingle | Jonathan K. Foster Matthew Korban Peter Youngs Ginger S. Watson Scott T. Acton Automatic classification of activities in classroom videos Computers and Education: Artificial Intelligence Elementary education Classroom video Classroom activity recognition Neural networks Computer vision |
| title | Automatic classification of activities in classroom videos |
| title_full | Automatic classification of activities in classroom videos |
| title_fullStr | Automatic classification of activities in classroom videos |
| title_full_unstemmed | Automatic classification of activities in classroom videos |
| title_short | Automatic classification of activities in classroom videos |
| title_sort | automatic classification of activities in classroom videos |
| topic | Elementary education Classroom video Classroom activity recognition Neural networks Computer vision |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X24000080 |
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