Summary of A General Model For Detecting Learner Engagement: Implementation and Evaluation, by Somayeh Malekshahi et al.
A General Model for Detecting Learner Engagement: Implementation and Evaluation
by Somayeh Malekshahi, Javad M. Kheyridoost, Omid Fatemi
First submitted to arxiv on: 7 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed general, lightweight model detects learners’ engagement levels while preserving temporal relationships by selecting and processing features from videos in the DAiSEE dataset. This approach improves instructors’ instructional performance by evaluating cumulative results and upgrading training programs. The model outperforms state-of-the-art models with an accuracy of 68.57%. The proposed adaptation policy utilizes affective states to improve labeling, enhancing the model’s judgment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being explored to help teachers make their lessons more engaging for students. This can be done by analyzing how students react and respond over time. Researchers have developed a simple and efficient method to detect student engagement levels based on video recordings of students learning. The approach uses data from the DAiSEE dataset, which contains videos of students working on educational tasks. The method has been tested and shown to be more effective than other existing methods, with an accuracy rate of 68.57%. This can help teachers create better lesson plans and improve student outcomes. |
