A Recommender System of Computer Programming Exercises based on Student’s Multiple Abilities and Skills Model
Fabiana Zaffalon, Andre Prisco, Ricardo de Souza, Davi Teixeira, Wanderson Paes, Paulo Jefferson Dias de Oliveira Evald, Neilor A. Tonin, Sam Da Silva Devincenzi, Sílvia Silva da Costa Botelho · 2022 IEEE Frontiers in Education Conference (FIE) · 2022
This paper presents a programming exercise recommender system based on the Student’s Multiple Abilities and Skills (SMAS) model, which is developed from Item Response Theory and Elo System Classification, for estimation of multiple student’s abilities. This model assumes that programming exercises have many ways to be solved (paths) and each path requires different abilities from the student. To evaluate the recommender system, an experiment was conducted in a class of Algorithms and Data Structures I. For this study case, the recommender was connected to an Online Judge system that had a programming problem base. The results show that the proposed recommender has the ability to indicate relevant problems according to the student’s abilities.