Evolutionary algorithms for subgroup discovery applied to e-learning data

This work presents the application of subgroup discovery techniques to e-learning data from learning management systems (LMS) of andalusian universities. The objective is to extract rules describing relationships between the use of the different activities and modules available in the e-learning platform and the final mark obtained by the students. For this purpose, the results of different classical and evolutionary subgroup discovery algorithms are compared, showing the adequacy of the evolutionary algorithms to solve this problem. Some of the rules obtained are analyzed with the aim of extract knowledge allowing the teachers to take actions to improve the performance of their students.