Abstract
The imbalance in student-teacher ratio and the diversity of student population pose challenges to MOOC's quality of instructor support. An understanding of student profiles, such as who they are and how they behave, is critical to improving personalized support of MOOC learning environments. While past studies have explored different types of student profiles, few have been done to investigate which student profiles lead to successful performance and what behavior patterns are exhibited by successful and unsuccessful performance groups. To address this research gap, we employed both bottom-up and top-down strategies, to gain useful insights into student learning in the context of MOOCs. From learning behavior records of 26,862 students in six MOOCs, we identified and validated three behavior attributes: effort regulation, self-assessment, and learner participation. Our results revealed that effort regulation emerged as the foremost important factor that positively contributes to students’ academic performance in MOOCs. Particularly, online persistence was the strongest positive predictor impacting student success. Based on the behavior attributes ascertained, we demonstrated five student sub-profiles with different behavior patterns: Persistence Achievers and Social Collaborators in the successful group; Dabblers, Disengagers, and Slackers in the unsuccessful group. Our analysis revealed that successful performers engaged with the course in quite different ways. We also investigated how effort regulation differed significantly between successful and unsuccessful performers. Unexpectedly, we also noticed that Persistence Achievers, despite their success, exhibited a high degree of procrastination. This work offers novel insights into instructional interventions for supporting MOOC learning.
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Data availability
The dataset this research used is publicly available on https://analyse.kmi.open.ac.uk/open_dataset. The preprocessed data that support the findings of this study are available from the corresponding author upon reasonable request.
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
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01 September 2023
A Correction to this paper has been published: https://doi.org/10.1007/s10639-023-12191-9
Notes
Note. To be consistent with other indexes, we changed the signs of the z-scores of procrastination, so the higher values denoted the fewer procrastination behaviors (i.e. better behaviors).
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The authors are indebted to Guizhen Xie for her assistance and support with this work. She selflessly contributed to the education field during her entire career life as an outstanding teacher and a great mother.
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Conceptualization: [Hui Shi], [Jaesung Hur]; Methodology: [Hui Shi], [Yihang Zhou]; Formal analysis and investigation: [Hui Shi], [Yihang Zhou]; Writing—original draft preparation: [Hui Shi]; Writing—review and editing: [Hui Shi], [Yihang Zhou], [Vanessa P. Dennen], [Jaesung Hur]; Supervision: [Vanessa P. Dennen].
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Shi, H., Zhou, Y., Dennen, V.P. et al. From unsuccessful to successful learning: profiling behavior patterns and student clusters in Massive Open Online Courses. Educ Inf Technol 29, 5509–5540 (2024). https://doi.org/10.1007/s10639-023-12010-1
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DOI: https://doi.org/10.1007/s10639-023-12010-1