A learning behavior classification model based on classroom meta-action sequences
Abstract The individual adaptive behavioral interpretation of students’ learning behaviors is a vital link for instructional process interventions. Accurately recognizing learning behaviors and conducting a complete judgment of classroom meta-action sequences are essential for the individual adaptiv...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-06901-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238597818908672 |
|---|---|
| author | Zhaoyu Shou Xiaohu Yuan Dongxu Li Jianwen Mo Huibing Zhang Hua Yuan Ziyong Wu |
| author_facet | Zhaoyu Shou Xiaohu Yuan Dongxu Li Jianwen Mo Huibing Zhang Hua Yuan Ziyong Wu |
| author_sort | Zhaoyu Shou |
| collection | DOAJ |
| description | Abstract The individual adaptive behavioral interpretation of students’ learning behaviors is a vital link for instructional process interventions. Accurately recognizing learning behaviors and conducting a complete judgment of classroom meta-action sequences are essential for the individual adaptive behavioral interpretation of students’ learning behaviors. This paper proposes a learning behavior classification model based on classroom meta-action sequences (ConvTran-Fibo-CA-Enhanced). The model employs the Fibonacci sequence for location encoding to augment the positional attributes of classroom meta-action sequences. It also integrates Channel Attention and Data Augmentation techniques to improve the model’s ability to comprehend these sequences, thereby increasing the accuracy of learning behavior classification and verifying the completeness of classroom meta-action sequences. Experimental results show that the proposed model outperforms baseline models on human activity recognition public datasets and learning behavior classification and meta-action sequences completeness judgment datasets in smart classroom scenarios. |
| format | Article |
| id | doaj-art-215da80dd209468e8e4aff5f6088bc47 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-215da80dd209468e8e4aff5f6088bc472025-08-20T04:01:34ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-06901-1A learning behavior classification model based on classroom meta-action sequencesZhaoyu Shou0Xiaohu Yuan1Dongxu Li2Jianwen Mo3Huibing Zhang4Hua Yuan5Ziyong Wu6School of Information and Communication, Guilin University of Electronic TechnologySchool of Information and Communication, Guilin University of Electronic TechnologySchool of Information and Communication, Guilin University of Electronic TechnologySchool of Information and Communication, Guilin University of Electronic TechnologySchool of Computer and Information Security, Guilin University of Electronic TechnologySchool of Information and Communication, Guilin University of Electronic TechnologyGuangxi Key Laboratory of Trusted Software, Guilin University of Electronic TechnologyAbstract The individual adaptive behavioral interpretation of students’ learning behaviors is a vital link for instructional process interventions. Accurately recognizing learning behaviors and conducting a complete judgment of classroom meta-action sequences are essential for the individual adaptive behavioral interpretation of students’ learning behaviors. This paper proposes a learning behavior classification model based on classroom meta-action sequences (ConvTran-Fibo-CA-Enhanced). The model employs the Fibonacci sequence for location encoding to augment the positional attributes of classroom meta-action sequences. It also integrates Channel Attention and Data Augmentation techniques to improve the model’s ability to comprehend these sequences, thereby increasing the accuracy of learning behavior classification and verifying the completeness of classroom meta-action sequences. Experimental results show that the proposed model outperforms baseline models on human activity recognition public datasets and learning behavior classification and meta-action sequences completeness judgment datasets in smart classroom scenarios.https://doi.org/10.1038/s41598-025-06901-1Learning behavior classificationConvTranPositional encodingChannel attentionData augmentation |
| spellingShingle | Zhaoyu Shou Xiaohu Yuan Dongxu Li Jianwen Mo Huibing Zhang Hua Yuan Ziyong Wu A learning behavior classification model based on classroom meta-action sequences Scientific Reports Learning behavior classification ConvTran Positional encoding Channel attention Data augmentation |
| title | A learning behavior classification model based on classroom meta-action sequences |
| title_full | A learning behavior classification model based on classroom meta-action sequences |
| title_fullStr | A learning behavior classification model based on classroom meta-action sequences |
| title_full_unstemmed | A learning behavior classification model based on classroom meta-action sequences |
| title_short | A learning behavior classification model based on classroom meta-action sequences |
| title_sort | learning behavior classification model based on classroom meta action sequences |
| topic | Learning behavior classification ConvTran Positional encoding Channel attention Data augmentation |
| url | https://doi.org/10.1038/s41598-025-06901-1 |
| work_keys_str_mv | AT zhaoyushou alearningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT xiaohuyuan alearningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT dongxuli alearningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT jianwenmo alearningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT huibingzhang alearningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT huayuan alearningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT ziyongwu alearningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT zhaoyushou learningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT xiaohuyuan learningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT dongxuli learningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT jianwenmo learningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT huibingzhang learningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT huayuan learningbehaviorclassificationmodelbasedonclassroommetaactionsequences AT ziyongwu learningbehaviorclassificationmodelbasedonclassroommetaactionsequences |