Discovery and Temporal Analysis of MOOC Study Patterns

Abstract

The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals’ learning processes and could facilitate the design of personalized and more effective support mechanisms for learners. In this paper, we present two different methods of extracting study patterns from activity sequences. Unlike most of the previous works, with post hoc analysis of activity patterns, our proposed methods could be deployed during the course and enable the learners to receive real-time support and feedback. In the first method, following a hypothesis-driven approach, we extract predefined patterns from learners’ interactions with the course materials. We then identify and analyze different longitudinal profiles among learners by clustering their study pattern sequences during the course. Our second method is a data-driven approach to discover latent study patterns and track them over time in a completely unsupervised manner. We propose a clustering pipeline to model and cluster activity sequences at each time step and then search for matching clusters in previous steps to enable tracking over time. The proposed pipeline is general and allows for analysis at different levels of action granularity and time resolution in various online learning environments. Experiments with synthetic data show that our proposed method can accurately detect latent study patterns and track changes in learning behaviours. We demonstrate the application of both methods on a MOOC dataset and study the temporal dynamics of learners’ behaviour in this context.

Learner Interactions and Behavioral Patterns in Enterprise Moocs

“…Within the scope of this research, LSA was conducted based on the system events. In addition, other metrics such as time spent could be included (Boroujeni & Dillenbourg, 2019), allowing for the discovery of more in-depth patterns and a deeper understanding of the learning process (Ifenthaler et al, 2018;Knight et al, 2017). After all, we expect to gain deeper insights into (successful) online-learning behavior in openSAP by combining LSA results with progress and performance data in the following steps of the research project.…”

Grit and learning: Understanding the sequential learning behaviours in snap!

“…Regarding the descriptions and examples of other four temporal analysis techniques (i.e. process mining, sequential pattern mining, Markov chains and HMM) applied in LA, refer to Boroujeni and Dillenbourg (2019) for a detailed review.…”

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

“…Formally, given interactions Is generated by students S until a course week w, we create a matrix H ⊂ R |S|×w×f (i.e, each feature in the feature set is computed per student per week), where f ∈ N is the dimensionality of the feature set. We focus on the following behavioral aspects: • Regularity features (H1, shape: |S| × w × 3) monitor the extent to which a student follows regular study habits [29]. • Engagement features (H2, shape: |S| × w × 13) monitor the extent to which a student is engaged in the course [30].…”