Abstract
The recent focus on learning analytics to analyse temporal dimensions of learning holds a strong promise to provide insights into latent constructs such as learning strategy, self-regulated learning, and metacognition. There is, however, a limited amount of research in temporally-focused process mining in educational settings. Building on a growing body of research around event-based data analysis, we explore the use of process mining techniques to identify strategic and tactical learner behaviours. We analyse trace data collected in online activities of a sample of nearly 300 computer engineering undergraduate students enrolled in a course that followed a flipped classroom pedagogy. Using a process mining approach based on first order Markov models in combination with unsupervised machine learning methods, we performed intra- and inter-strategy analysis. We found that certain temporal activity traits relate to performance in the summative assessments attached to the course, mediated by strategy type. Results show that more strategically minded activity, embodying learner self-regulation, generally proves to be more successful than less disciplined reactive behaviours.
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Saint, J., Gašević, D., Pardo, A. (2018). Detecting Learning Strategies Through Process Mining. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science(), vol 11082. Springer, Cham. https://doi.org/10.1007/978-3-319-98572-5_29
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