Personalized, Affect and Performance-driven Computer-based Learning

The growing prevalence of Internet during the last decades has made e-learning systems and Computer-based Education (CBE) widely accessible to a great amount of people with different backgrounds and competences. Due to these rapid advances in computer technologies, there has been a great shift from conventional, low interaction and printed learning content to high-level, computerized interactions for Computer-based Education. The above has led to the need for personalized systems, able to adapt their content for a variety of learner's abilities and skills. A key factor in content personalization is the degree to which the material itself keeps learners engaged over the course of the interaction: a CBE system has to cater for enough flexibility and be endowed with the ability to infer the degree to which the learner is engaged in the interaction and also be in the position to take decisions regarding the triggering of those adaptation mechanics that will keep the learner in a state of high engagement, maximizing, thus, the knowledge acquisition. A straightforward approach in content adaptation is the monitoring of levels of engagement, frustration and boredom in a learner and the subsequent adaptation of challenge levels imposed by the learning material. In this paper, we investigate the use of Collaborative Filtering, in order to build a content adaptation mechanism, based on recommendations on learner affect states. We showcase results on an interface developed specifically for the purposes of this research. The system's objective is to offer optimized sessions to the learners and improve their knowledge acquisition during the interaction with the system.