T6-FY1-3 - Evaluating a programming problem recommendation model - a classroom personalization experiment

2. Research-to-Practice Full Paper
André Prisco1 , Rafael Santos1, Álvaro Nolibos1, Neilor Tonin2, Jean Bez2, 3, Silvia Botelho1
1 Federal University of Rio Grande (FURG)
2 URI - Campus Erechim
3 Federal University of Rio Grande do Sul

In this full paper, research to practice, we present a classroom experience, in which we apply a teaching personalization model in an introductory computer science class. Students in this discipline are freshmen at the university and have different backgrounds related to solving programming problems. The traditional approach is standardized, tending to not serve each student in the best way and that is why we have adopted this group as a case study. We use the ELO-based model to recommend specific learning objects for each student, in order to match the student's ability with the difficulty of the problem. The learning objects correspond to programming problems in an online platform for automatic submission and evaluation. The experiment was divided into three stages. In the first, the student was able to freely choose problems from the platform repository. In the second stage, problems were randomly recommended (as a control). In the third stage, the recommendation was made using the model adopted. Students were encouraged to give feedback on their experience described in a free text and in the labeling of hashtags about the learning object. In addition, the rates of success, error, withdrawal and the frequency of access to the online platform were
also collected. We observed that the students had a better engagement and provided better feedback at the stage when the recommendation matched the proposed model.