Discovery and temporal analysis of latent study patterns in MOOC interaction sequences

Abstract

Capturing students' behavioral patterns through analysis of sequential interaction logs is an important task in educational data mining and could enable more effective and personalized support during the learning processes. This study aims at discovery and temporal analysis of learners' study patterns in MOOC assessment periods. We propose two different methods to achieve this goal. First, following a hypothesis-driven approach, we identify learners' study patterns based on their interaction with lectures and assignments. Through clustering of study pattern sequences, we capture different longitudinal activity profiles among learners and describe their properties. Second, we propose a temporal clustering pipeline for unsupervised discovery of latent patterns in learners' interaction data. We model and cluster activity sequences at each time step and perform cluster matching to enable tracking learning behaviours over time. Our proposed pipeline is general and applicable in different learning environments such as MOOC and ITS. Moreover, it allows for modeling and temporal analysis of interaction data at different levels of actions granularity and time resolution. We demonstrate the application of this method for detecting latent study patterns in a MOOC course.