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.

在实际的协作学习环境中，通过开发数据集和方法来评估学生参与度。我们将评估学生参与度的问题分为两个子问题：学生组检测和动态参与者跟踪。使用一组包含12,518,250个学生标签实例、总时长为21小时22分钟的实际视频的独立测试数据集来评估我们提出的学生组检测方法的性能。通过使用多个图像表示方法，该方法在所有视频实例上表现出与YOLO相等或更好的性能。提出的动态参与者跟踪系统在长时间视频会话中表现出色，仅在35个测试视频中的一个视频中未跟踪到学生，而一个最先进的方法在35个测试视频中有14个视频未能跟踪到学生。在一个独立的长时间真实协作视频集上，提出的方法达到82.3%的准确率。

在协作学习环境中使用动态场景分析进行长期人类参与评估