Estimating the Multiple Skills of Students in Massive Programming Environments

Abstract

This Research to Practice Full Paper presents a proposed model to estimate the multiple skills of students in massive online environments that provide programming exercises, whose assessment methods occur automatically without human intervention. The proposed model is based on the M-ERS model and incorporates, from the TrueSkill model, the uncertainty regarding the student's skills. To validate the model, a database from the URI Online Judge platform was used and the M-ERS and TriMElo models were applied to compare the performance and behavior of the two models. The empirical results show that the proposed model updates student's skills more smoothly, according to the correctness or error of the exercise, according to the uncertainty of the skills.