Using information technology for personalizing the computer science teaching
Andre Prisco, Rafael dos Santos, Sílvia Silva da Costa Botelho, Neilor A. Tonin, Jean Luca Bez · 2017
Recommendation systems use computational techniques to select items in a personalized way to users, taking into account criteria such as history and interest. However, several authors point out that the process of recommendation in education requires models beyond the user's taste, in order to catalyze students' learning. In addition, feedback involves the student's experience. In this work we present a recommendation system of learning objects supported by a cognitive pedagogical model. The central idea of the system is to find an object that adequately challenges the student without bothering with similar problems or becoming discouraged when faced with problems beyond his or her ability. We integrate learning models into game models to integrate them into learning models. We used as a case study a virtual learning environment which has a repository with programming problems. The results indicate that, in general, when students choose more appropriate problems (ELOs similar to theirs), they get a greater number of correct answers in their submissions. When the student choose problems that do not seem to be challenging, in general, they make wrong submissions or give up learning on the platform.