Recommender system in collaborative learning environment using an influence diagram

• Proposed a recommender system in a collaborative learning environment.
• Created an influence diagram using expert knowledge and student interactions data.
• The influence diagram offers a recommendation decision table.
• Machine learning methods analyze the decisions and offer a recommendation tree.
• The expert identifies four different scenarios in the recommendation tree.

Giving useful recommendations to students to improve collaboration in a learning experience requires tracking and analyzing student team interactions, identifying the problems and the target student. Previously, we proposed an approach to track students and assess their collaboration, but it did not perform any decision analysis to choose a recommendation for the student. In this paper, we propose an influence diagram, which includes the observable variables relevant for assessing collaboration, and the variable representing whether the student collaborates or not. We have analyzed the influence diagram with two machine learning techniques: an attribute selector, indicating the most important attributes that the model uses to recommend, and a decision tree algorithm revealing four different scenarios of recommendation. These analyses provide two useful outputs: (a) an automatic recommender, which can warn of problematic circumstances, and (b) a pedagogical support system (decision tree) that provides a visual explanation of the recommendation suggested.