A multidimensional ELO model for matching learning objects
Andre Prisco, Rafael Penna, Evandro, Sílvia Silva da Costa Botelho, Neilor A. Tonin, Jean Luca Bez · 2018
This research-to-practice full paper proposals a metric of multiple skills for learning of programming students. This kind of system often need to diagnose the student's skill level. In the same way it needs to know the level of difficulty learning objects in its database. Such information makes it possible to make an appropriate match between student and the learning object. To model such tasks, we have adapted the ELO technique to apply a matchmaking process similar to that used in choosing opponents in chess tournaments or online matches. We used as a case study a virtual learning environment which has a repository with programming problems and the users interaction log. In this work we propose an extension to the traditional ELO model. In the classical model, ELO is a scalar value for each student and for each learning object. The extended model considers ELO as a multidimensional quantity, where each dimension is a skill in solving programming problems. The enumeration of the skills was made using the literature as well as statistical data of relevance of the attributes. The results are presented in this work.