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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.10813 |
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| _version_ | 1866917924415995904 |
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| author | Mandia, Sandeep Singh, Kuldeep Mitharwal, Rajendra Mushtaq, Faisel Janu, Dimpal |
| author_facet | Mandia, Sandeep Singh, Kuldeep Mitharwal, Rajendra Mushtaq, Faisel Janu, Dimpal |
| contents | The COVID-19 pandemic and the internet's availability have recently boosted online learning. However, monitoring engagement in online learning is a difficult task for teachers. In this context, timely automatic student engagement classification can help teachers in making adaptive adjustments to meet students' needs. This paper proposes EngageFormer, a transformer based architecture with sequence pooling using video modality for engagement classification. The proposed architecture computes three views from the input video and processes them in parallel using transformer encoders; the global encoder then processes the representation from each encoder, and finally, multi layer perceptron (MLP) predicts the engagement level. A learning centered affective state dataset is curated from existing open source databases. The proposed method achieved an accuracy of 63.9%, 56.73%, 99.16%, 65.67%, and 74.89% on Dataset for Affective States in E-Environments (DAiSEE), Bahcesehir University Multimodal Affective Database-1 (BAUM-1), Yawning Detection Dataset (YawDD), University of Texas at Arlington Real-Life Drowsiness Dataset (UTA-RLDD), and curated learning-centered affective state dataset respectively. The achieved results on the BAUM-1, DAiSEE, and YawDD datasets demonstrate state-of-the-art performance, indicating the superiority of the proposed model in accurately classifying affective states on these datasets. Additionally, the results obtained on the UTA-RLDD dataset, which involves two-class classification, serve as a baseline for future research. These results provide a foundation for further investigations and serve as a point of reference for future works to compare and improve upon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transformer-Driven Modeling of Variable Frequency Features for Classifying Student Engagement in Online Learning Mandia, Sandeep Singh, Kuldeep Mitharwal, Rajendra Mushtaq, Faisel Janu, Dimpal Computer Vision and Pattern Recognition The COVID-19 pandemic and the internet's availability have recently boosted online learning. However, monitoring engagement in online learning is a difficult task for teachers. In this context, timely automatic student engagement classification can help teachers in making adaptive adjustments to meet students' needs. This paper proposes EngageFormer, a transformer based architecture with sequence pooling using video modality for engagement classification. The proposed architecture computes three views from the input video and processes them in parallel using transformer encoders; the global encoder then processes the representation from each encoder, and finally, multi layer perceptron (MLP) predicts the engagement level. A learning centered affective state dataset is curated from existing open source databases. The proposed method achieved an accuracy of 63.9%, 56.73%, 99.16%, 65.67%, and 74.89% on Dataset for Affective States in E-Environments (DAiSEE), Bahcesehir University Multimodal Affective Database-1 (BAUM-1), Yawning Detection Dataset (YawDD), University of Texas at Arlington Real-Life Drowsiness Dataset (UTA-RLDD), and curated learning-centered affective state dataset respectively. The achieved results on the BAUM-1, DAiSEE, and YawDD datasets demonstrate state-of-the-art performance, indicating the superiority of the proposed model in accurately classifying affective states on these datasets. Additionally, the results obtained on the UTA-RLDD dataset, which involves two-class classification, serve as a baseline for future research. These results provide a foundation for further investigations and serve as a point of reference for future works to compare and improve upon. |
| title | Transformer-Driven Modeling of Variable Frequency Features for Classifying Student Engagement in Online Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.10813 |