  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.
