Summary of Learner Attentiveness and Engagement Analysis in Online Education Using Computer Vision, by Sharva Gogawale et al.
Learner Attentiveness and Engagement Analysis in Online Education Using Computer Vision
by Sharva Gogawale, Madhura Deshpande, Parteek Kumar, Irad Ben-Gal
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a computer vision-based approach for analyzing and quantifying learners’ attentiveness, engagement, and affective states in online learning scenarios. A novel multiclass multioutput classification method using convolutional neural networks (CNNs) is developed on the DAiSEE dataset. A machine learning-based algorithm outputs a comprehensive attentiveness index of learners. The paper also proposes an end-to-end pipeline for processing live video feeds to provide detailed attentiveness analytics to instructors. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in attentiveness detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a way to use computer vision and machine learning to see if students are paying attention during online classes. It uses special algorithms and a big dataset of examples to teach computers how to recognize when students are engaged or distracted. The system can even provide real-time feedback to teachers, helping them understand what’s working and what isn’t. |
Keywords
» Artificial intelligence » Attention » Classification » Machine learning » Online learning
