# A General Model for Detecting Learner Engagement: Implementation and Evaluation

arXiv: https://arxiv.org/abs/2405.04251 · HTML: https://mlsanity.com/paper/2405.04251/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 7 of 7)

- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [DAiSEE: Towards User Engagement Recognition in the Wild](https://mlsanity.com/paper/1609.01885/) (2016)
- [Prediction and Localization of Student Engagement in the Wild](https://mlsanity.com/paper/1804.00858/) (2018)
- [Lip-reading with Densely Connected Temporal Convolutional Networks](https://mlsanity.com/paper/2009.14233/) (2020)
- [Improving state-of-the-art in Detecting Student Engagement with Resnet and TCN Hybrid Network](https://mlsanity.com/paper/2104.10122/) (2021)
- [Bag of States: A Non-sequential Approach to Video-based Engagement Measurement](https://mlsanity.com/paper/2301.06730/) (2023)

## Built on by (most cited first, 4 of 4)

- [ViBED-Net: Video Based Engagement Detection Network Using Face-Aware and Scene-Aware Spatiotemporal Cues](https://mlsanity.com/paper/2510.18016/) (2025)
- [PriorNet: Prior-Guided Engagement Estimation from Face Video](https://mlsanity.com/paper/2605.03615/) (2026)
- [ConfusionBench: An Expert-Validated Benchmark for Confusion Recognition and Localization in Educational Videos](https://mlsanity.com/paper/2603.17267/) (2026)
- [Dimension-Specific Imbalance and an Adaptive Hybrid Label Strategy for Multi-Task Affective State Recognition in Classroom Video](https://mlsanity.com/paper/2609.31750/) (2026)

## Abstract (arXiv)

Considering learner engagement has a mutual benefit for both learners and instructors. Instructors can help learners increase their attention, involvement, motivation, and interest. On the other hand, instructors can improve their instructional performance by evaluating the cumulative results of all learners and upgrading their training programs. This paper proposes a general, lightweight model for selecting and processing features to detect learners' engagement levels while preserving the sequential temporal relationship over time. During training and testing, we analyzed the videos from the publicly available DAiSEE dataset to capture the dynamic essence of learner engagement. We have also proposed an adaptation policy to find new labels that utilize the affective states of this dataset related to education, thereby improving the models' judgment. The suggested model achieves an accuracy of 68.57% in a specific implementation and outperforms the studied state-of-the-art models detecting learners' engagement levels.