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Although MicroMasters courses differ from traditional undergraduate level MOOCs in student demographics, course design, and outcomes, the various aspects of this type of program have not yet been sufficiently investigated. This study aims to pave the path towards enhancing the design of constituent courses of MicroMasters programs with the focus on the application of Machine Learning algorithms. Thereby, we use a large-scale clickstream edX database to explore the trends in the online engagement of learners in a MicroMasters program, detect clickstream events that are highly correlated with the students' progress, and investigate how the engagements differ from those in a classic individual MOOC. Contrary to the previous application of machine learning algorithms in learning analytics, we implement various well-known machine learning approaches such as stepwise regression and tree-based algorithms, evaluate their performance, and propose the best-performed approach. We elaborate on noticeable differences between the engagements of the considered two groups.
Soleimani, F., & Lee, J. (2021, June). Comparative analysis of the feature extraction approaches for predicting learners progress in online courses: MicroMasters credential versus traditional MOOCs. In Proceedings of the Eighth ACM Conference on Learning@ Scale (pp. 151-159). (Conference Proceedings)