Improving children's experience on a mobile EdTech platform through a recommender system

Abstract

Smile and Learn is an EdTech digital publisher that offers a smart library of close to 100 educational stories and gaming apps for mobile devices aimed at children aged 2 to 10 and their families. Given the complexity of navigating the content, a recommender system was developed. The system consists of two major components: One that generates content recommendations and another that provides explanations and recommendations relevant to parents and educators. The former was implemented as a hybrid recommender system that combines three kinds of recommendations. Among these, we introduce a collaborative filtering adapted to overcome specific limitations associated with younger users. The approach described in this work was tested on real users of the platform. The experimental results suggest that this recommendation model is suitable to suggest apps to children and increase their engagement in terms of usage time and number of games played.