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Online education nowadays plays a very important role in enhancing the educational processes mostly for adults. Given this maturing technology and the number of children that lack access to safe education, mobile education for children is a logical next step and opens options that change their prospects. This paper is part of a larger project on mobile learning with games for children without access to schools. Games can motivate children to learn without the necessity of a teacher. The goal is to recommend learning games based on childrenâs preferences of past choices and ratings, which can supplement other recommender systems. The resulting implemented algorithm is designed as a plug-in to exisiting learning platforms that use games. Such a system was implemented and evaluated in a feasibility study on adults. We show that a prediction based on userâs choice and rating of games corresponds to a direct survey to determine the gamer types in 66% of the cases for 61 participants.

Personalizing Game Selection for Mobile Learning - With a View Towards Creating an Off-line Learning Environment for Children