A Machine Learning Approach to Assess Student Group Collaboration Using Individual Level Behavioral Cues
This work addresses the practical issue of time constraints for teachers in assessing collaboration, but it is incremental as it applies existing methods like Mixup to a specific educational domain.
The paper tackles the problem of automatically assessing student group collaboration quality in K-12 classrooms by using deep learning models based on individual behavioral cues, addressing challenges like limited data and class imbalance with Mixup data augmentation and ordinal-cross-entropy loss.
K-12 classrooms consistently integrate collaboration as part of their learning experiences. However, owing to large classroom sizes, teachers do not have the time to properly assess each student and give them feedback. In this paper we propose using simple deep-learning-based machine learning models to automatically determine the overall collaboration quality of a group based on annotations of individual roles and individual level behavior of all the students in the group. We come across the following challenges when building these models: 1) Limited training data, 2) Severe class label imbalance. We address these challenges by using a controlled variant of Mixup data augmentation, a method for generating additional data samples by linearly combining different pairs of data samples and their corresponding class labels. Additionally, the label space for our problem exhibits an ordered structure. We take advantage of this fact and also explore using an ordinal-cross-entropy loss function and study its effects with and without Mixup.