Recommender system in collaborative learning environment using an influence diagram

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. Nowadays educational institutions support students with e-learning environments, where collaboration is possible and advisable (Swan, Shen, & Hiltz, 2006). Giving recommendations in these environments has not been studied in depth by the research community (Caballé, Daradoumis, Xhafa, & Juan, 2011), because frequent and regular analysis of student interactions is required to know whether collaboration takes place (Johnson & Johnson, 2004; Swan et al., 2006). However, once students have been tracked and their collaboration has been assessed (Anaya & Boticario, 2011b), two questions arise: Who will be recommended? and What will be recom-mended? Both questions must be answered in accordance with the student circumstances, which will be known when their tracking and assessments are compared with those of all the students. Thus, our main objective is to build a system that analyzes student tracking and collaboration assessments in the context of col-laborative learning in an e-learning environment to identify the student circumstances and proposes a personal recommendation to a target student. Once the circumstances are known, the collaboration problem identified, and the decision resolved, the student circumstances support and explain the recommendation.