Approaching explainable recommendations for personalized social learning the current stage of the educational platform ”WhoTeach”

Abstract

Deep attentional mechanisms proved to be particularly effective for identifying relevant communities and relationships in any given input network that can be exploited with the aim of improving useful information to interpret the suggested decision process. In this paper we provide the first stages of our ongoing research project, aimed at significantly empowering the recommender system of the educational platform”WhoTeach” by means of explainability, to help teachers or experts to create and manage high-quality courses for personalized learning. The presented model is actually our first tentative to start to include explainability in the system. As shown, the model has strong potentialities to provide relevant recommendations.

 

Authors: Marconi, L., Matamoros Aragon, R., Zoppis, I., Manzoni, S., Mauri, G., & Epifania, F.

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