Using Adaptive Content Recommendations to Improve Logic and Programming Teaching and Learning

The high dropout rate in Information Technologies courses is a relevant problem in many countries, mainly because of the increasing demand for professionals in this sector. Usually, high dropout rates in these courses are related to difficulties in algorithms and programming subjects. Content recommendation systems are proposed to mitigate this problem, employing adaptive learning environments that facilitate the learning process. This study presents a content recommendation system that uses learning paths to group students and provide personalized recommendations based on peers' progress. The work follows the many efforts of group-based recommendation systems reported in the literature. The system uses intelligent agents and clustering algorithms to implement the recommendation system and was evaluated by submitting the simulation results to the judgment of human experts who significantly agreed with them. This initiative could make programming teaching more adaptive, using the groups' knowledge. Facilitating learning is one of the key issues to reduce dropout rates and resolve the shortage of labor in the technological area in Portuguese-speaking countries.