# Long-term Human Participation Assessment In Collaborative Learning Environments Using Dynamic Scene Analysis

arXiv: https://arxiv.org/abs/2405.02317 · HTML: https://mlsanity.com/paper/2405.02317/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking](https://mlsanity.com/paper/1809.07845/) (2018)
- [TAO: A Large-Scale Benchmark for Tracking Any Object](https://mlsanity.com/paper/2005.10356/) (2020)
- [A Novel Performance Evaluation Methodology for Single-Target Trackers](https://mlsanity.com/paper/1503.01313/) (2015)
- [Sample and Computation Redistribution for Efficient Face Detection](https://mlsanity.com/paper/2105.04714/) (2021)
- [Simple online and real-time tracking with occlusion handling](https://mlsanity.com/paper/2103.04147/) (2021)
- [Hard Occlusions in Visual Object Tracking](https://mlsanity.com/paper/2009.04787/) (2020)
- [Fast Hand Detection in Collaborative Learning Environments](https://mlsanity.com/paper/2110.07070/) (2021)
- [Facial Recognition in Collaborative Learning Videos](https://mlsanity.com/paper/2110.13269/) (2021)
- [Human Attention Detection Using AM-FM Representations](https://mlsanity.com/paper/2203.07093/) (2022)
- [Person Detection in Collaborative Group Learning Environments Using Multiple Representations](https://mlsanity.com/paper/2112.12217/) (2021)

## Abstract (arXiv)

The paper develops datasets and methods to assess student participation in real-life collaborative learning environments. In collaborative learning environments, students are organized into small groups where they are free to interact within their group. Thus, students can move around freely causing issues with strong pose variation, move out and re-enter the camera scene, or face away from the camera. We formulate the problem of assessing student participation into two subproblems: (i) student group detection against strong background interference from other groups, and (ii) dynamic participant tracking within the group. A massive independent testing dataset of 12,518,250 student label instances, of total duration of 21 hours and 22 minutes of real-life videos, is used for evaluating the performance of our proposed method for student group detection. The proposed method of using multiple image representations is shown to perform equally or better than YOLO on all video instances. Over the entire dataset, the proposed method achieved an F1 score of 0.85 compared to 0.80 for YOLO. Following student group detection, the paper presents the development of a dynamic participant tracking system for assessing student group participation through long video sessions. The proposed dynamic participant tracking system is shown to perform exceptionally well, missing a student in just one out of 35 testing videos. In comparison, a state of the art method fails to track students in 14 out of the 35 testing videos. The proposed method achieves 82.3% accuracy on an independent set of long, real-life collaborative videos.