  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.
