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Collaborative learning incorporates a social component in distance education to minimize the disadvantages of studying in solitude. Frequent analysis of student interactions is required for assessing collaboration. Collaboration analytics arose as a discipline to study student interactions and to promote active participation in e-learning environments. Unfortunately, researchers have been more focused on finding methods to solve collaboration problems than on explaining the results to tutors and students. Yet if students do not understand the results of collaboration analysis methods, they will rarely follow their advice. In this paper we propose a tool that analyzes student interactions and visually explains the collaboration circumstances to provoke the self-reflection and promote the sensemaking about collaboration. The tool presents a visual explanatory decision tree that graphically highlights student collaboration circumstances and helps to understand the reasoning followed by the tool when prescribing a recommendation. An assessment of the tool has demonstrated: (1) the students collaboration circumstances showed by the tool are easy to understand and (2) the students could realize the possible actions to improve the collaboration process. [ABSTRACT FROM AUTHOR]

Highlights: [•] 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. [ABSTRACT FROM AUTHOR]