The Effect of Personality and Course Attributes on Academic Performance in MOOCs

Abstract

Predicting academic performance has been a topic of research for years, with different factors having been used to predict student grades. One of those factors is personality, with little work having focused on the effect of personality on academic performance in Massive Open Online Courses (MOOCs). Contributing to our lack of understanding of how personality is linked to academic performance in MOOCs, studies that predict academic performance by combining personality with attributes of online course design to have yet to be reported. In this paper, we try to tackle this problem by using personality and level of collaboration (a course attribute) to predict academic performance. We chose level of collaboration as one of the course attributes in our research because social factors, such as the amount of student interaction, can impact learner attrition in MOOCs. We apply machine learning algorithms to two different feature sets. The first feature set only uses personality as a predictor and the second feature set uses personality and level of collaboration in a course as predictors of academic performance. A comparison of these predictive models revealed that adding level of collaboration can increase their performance significantly. These results provide further evidence of the importance of validating classroom-based research in online settings. Moreover, the results of this work can be useful in several ways. For example, we may be able to give better recommendations to users based on their personality and the attributes of courses. We may also be able to adapt course attributes to match the personality characteristics of each student.