Towards Real Time Detection of Learners’ Need of Help in Serious Games

Providing an adequate help to a learner remains a challenge. In this paper we aim to find how to provide learners with real time help in an educational game. Detecting that a player is engaged or motivated is a good sign that he is progressing. For these reasons we need to assess learner’s states while learning. In this study we gather a variety of data using three types of sensors (electroencephalography, eye tracking and automatic facial expression recognition) to build a reliable user adaptation system. The data result from an interaction of 40 players with LewiSpace game, that we built for experimental purpose to learn construction of Lewis diagrams. We used machine learning algorithms in order to identify the most important features gathered from each sensor. Two models were trained with these data: a generalized model, trained on all data available, and a personalized model, trained only on the current user during an early phase of the game experience. The predictive results showed that personalized model could outperform the generalized model.