A New Method of Classroom Behavior Recognition Based on WS-FC SLOWFAST Article Swipe
Damin Ding
,
Y Zhao
,
Jingru Zhang
,
Jin Liu
,
Jun Liu
,
Haima Yang
,
Hongli Shan
,
Zhiwen Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.4018/ijgcms.371423
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.4018/ijgcms.371423
With the growing integration of deep learning and educational informatization, applying artificial intelligence to classroom behavior analysis has garnered significant attention. This article specifies 14 types of classroom behaviors and their classification criteria. By clipping and frame extraction from surveillance videos, target detection, manual annotation, temporal association, and other operations, a multi-label behavior dataset was created. This article also proposes a Weakly supervised fine-grained classification SlowFast SlowFast behavior recognition algorithm, which improves the accuracy of recognizing small difference classroom behaviors from an intra-class classification perspective. By using attention-guided local feature enhancement in the path, weakly supervised fine-grained classification of behavior target local features was achieved. Experimental results showed the algorithm improves behavior recognition accuracy by 4%-11% for specific behaviors and 5.75% overall, contributing to teaching quality evaluation systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4018/ijgcms.371423
- https://www.igi-global.com/ViewTitle.aspx?TitleId=371423&isxn=9798337311685
- OA Status
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- References
- 38
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408738739Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4018/ijgcms.371423Digital Object Identifier
- Title
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A New Method of Classroom Behavior Recognition Based on WS-FC SLOWFASTWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-03-22Full publication date if available
- Authors
-
Damin Ding, Y Zhao, Jingru Zhang, Jin Liu, Jun Liu, Haima Yang, Hongli Shan, Zhiwen ZhouList of authors in order
- Landing page
-
https://doi.org/10.4018/ijgcms.371423Publisher landing page
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https://www.igi-global.com/ViewTitle.aspx?TitleId=371423&isxn=9798337311685Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.igi-global.com/ViewTitle.aspx?TitleId=371423&isxn=9798337311685Direct OA link when available
- Concepts
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Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
38Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.SlowFast | 65, 66 |
| abstract_inverted_index.accuracy | 73, 113 |
| abstract_inverted_index.analysis | 16 |
| abstract_inverted_index.applying | 10 |
| abstract_inverted_index.behavior | 15, 52, 67, 99, 111 |
| abstract_inverted_index.clipping | 34 |
| abstract_inverted_index.created. | 55 |
| abstract_inverted_index.features | 102 |
| abstract_inverted_index.garnered | 18 |
| abstract_inverted_index.improves | 71, 110 |
| abstract_inverted_index.learning | 6 |
| abstract_inverted_index.overall, | 121 |
| abstract_inverted_index.proposes | 59 |
| abstract_inverted_index.specific | 117 |
| abstract_inverted_index.systems. | 127 |
| abstract_inverted_index.teaching | 124 |
| abstract_inverted_index.temporal | 45 |
| abstract_inverted_index.achieved. | 104 |
| abstract_inverted_index.algorithm | 109 |
| abstract_inverted_index.behaviors | 28, 79, 118 |
| abstract_inverted_index.classroom | 14, 27, 78 |
| abstract_inverted_index.criteria. | 32 |
| abstract_inverted_index.specifies | 23 |
| abstract_inverted_index.algorithm, | 69 |
| abstract_inverted_index.artificial | 11 |
| abstract_inverted_index.attention. | 20 |
| abstract_inverted_index.detection, | 42 |
| abstract_inverted_index.difference | 77 |
| abstract_inverted_index.evaluation | 126 |
| abstract_inverted_index.extraction | 37 |
| abstract_inverted_index.supervised | 62, 95 |
| abstract_inverted_index.annotation, | 44 |
| abstract_inverted_index.educational | 8 |
| abstract_inverted_index.enhancement | 90 |
| abstract_inverted_index.integration | 3 |
| abstract_inverted_index.intra-class | 82 |
| abstract_inverted_index.multi-label | 51 |
| abstract_inverted_index.operations, | 49 |
| abstract_inverted_index.recognition | 68, 112 |
| abstract_inverted_index.recognizing | 75 |
| abstract_inverted_index.significant | 19 |
| abstract_inverted_index.Experimental | 105 |
| abstract_inverted_index.association, | 46 |
| abstract_inverted_index.contributing | 122 |
| abstract_inverted_index.fine-grained | 63, 96 |
| abstract_inverted_index.intelligence | 12 |
| abstract_inverted_index.perspective. | 84 |
| abstract_inverted_index.surveillance | 39 |
| abstract_inverted_index.classification | 31, 64, 83, 97 |
| abstract_inverted_index.attention-guided | 87 |
| abstract_inverted_index.informatization, | 9 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.08077345 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |