What is it about?
Our study explores the online learning behaviours of a cohort of first-year university students across science and programming subjects. Through analysis of data from online learning management systems (LMS) using sequence analysis and process mining, we discovered distinct learning patterns among students based on the subject they were studying.
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Why is it important?
The findings of our research have two implications: First, as students' learning behaviours fluctuate on a weekly basis, it is important to integrate these temporal patterns into the data analysis process; Second, considering variation in students' learning behaviours across subjects, conducting cross-disciplinary analysis can provide significant insights on their diverse learning experiences.
Perspectives
I'm thrilled to publish this work as my first academic paper in my PhD/research journey. I hope this paper can inspire people to try to understand "student-as-a-whole", rather than "students-in-one-subject" or "students-at-a-time".
Yige Song
University of Melbourne
Read the Original
This page is a summary of: A Case Study on University Student Online Learning Patterns Across Multidisciplinary Subjects, March 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3636555.3636939.
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