  Collaboration is identified as a required and necessary skill for students to
be successful in the fields of Science, Technology, Engineering and Mathematics
(STEM). However, due to growing student population and limited teaching staff
it is difficult for teachers to provide constructive feedback and instill
collaborative skills using instructional methods. Development of simple and
easily explainable machine-learning-based automated systems can help address
this problem. Improving upon our previous work, in this paper we propose using
simple temporal-CNN deep-learning models to assess student group collaboration
that take in temporal representations of individual student roles as input. We
check the applicability of dynamically changing feature representations for
student group collaboration assessment and how they impact the overall
performance. We also use Grad-CAM visualizations to better understand and
interpret the important temporal indices that led to the deep-learning model's
decision.
