Investigating Learning Design Categorization and Learning Behaviour in Computational MOOCS

Abstract

We investigate learner efficiency by categorizing a computational MOOC and analyzing user behavior data from a learning design point of view. Learning design is important both when designing courses as well as studying them. Learning behavior can be observed from the MOOC platform data. For this study we ask two learning designer experts to categorize a course on MITx: '6.00.1x Introduction to Computer Science and Programming Using Python'. We use these categorizations to investigate relationships with learning behavior by analyzing the MOOC platform data. Our study verifies that learning design can be correlated to learning behavior, e.g. students exhibit a pattern of behavior associated to a component's difficulty and category.