Transformer-Driven Modeling of Variable Frequency Features for Classifying Student Engagement in Online Learning

• URL: http://arxiv.org/abs/2502.10813v1
• Date: Sat, 15 Feb 2025 14:37:09 GMT
• Title: Transformer-Driven Modeling of Variable Frequency Features for Classifying Student Engagement in Online Learning
• Authors: Sandeep Mandia, Kuldeep Singh, Rajendra Mitharwal, Faisel Mushtaq, Dimpal Janu,
• Abstract summary: 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. A learning centered affective state dataset is curated from existing open source databases.
• Score: 2.127312905562737
• License:
• Abstract: 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.

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