Assessing heterogeneity in MOOC student performance through composite-based path modelling

Massive open online courses (MOOCs) are potentially participated in by very many students from different parts of the world, which means that learning analytics is especially challenging. In this framework, predicting students’ performance is a key issue, but the high level of heterogeneity affects understanding and measurement of the causal links between performance and its drivers, including motivation, attitude to learning, and engagement, with different models recommended for the formulation of appropriate policies. Using data for the FedericaX EdX MOOC platform (Federica WebLearning Centre at the University of Naples Federico II), we exploit a consolidated composite-based path model to relate performance with engagement and learning. The model addresses heterogeneity by analysing gender, age, country of origin, and course design differences as they affect performance. Results reveal subgroups of students requiring different learning strategies to enhance final performance. Our main findings were that differences in performance depended mainly on learning for male students taking instructor-paced courses, and on engagement for older students (> 32 years) taking self-paced courses.