OP-DCI: A Riskless K-Means Clustering for Influential User Identification in MOOC Forum
Abstract
Massive Open Online Courses (MOOCs) have recently been highly popular among worldwide learners, while it is challenging to manage and interpret the large-scale discussion forum which is the dominant channel of online communication. K-Means clustering, one of the famous unsupervised learning algorithms, could help instructors identify influential users in MOOC forum, to better understand and improve online learning experience. However, traditional K-Means suffers from bias of outliers and risk of falling into local optimum. In this paper, OP-DCI, an optimized K-Means algorithm is proposed, using outlier post-labeling and distant centroid initialization. Outliers are not solely filtered out but extracted as distinct objects for post-labeling, and distant centroid initialization eliminates the risk of falling into local optimum. With OP-DCI, learners in MOOC forum are clustered efficiently with satisfactory interpretation, and instructors can subsequently design personalized learning strategies for different clusters.
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In [40], an optimized K-means clustering algorithm was used to cluster learners in MOOC forums and aid instructors in designing personalized learning strategies.
“An optimized version of the K-means clustering algorithm, an unsupervised learning method, is used to efficiently cluster learners in MOOC (massive open online course) forums and aid instructors in designing personalized learning strategies.”
From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [40]
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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980 · see citing works
Gonaygunta, H., Nadella, G. S., Pramod Pawar, P., & Kumar, D. (2024) · 2024 Systems and Information Engineering Design Symposium (SIEDS) · doi:10.1109/sieds61124.2024.10534661 · Cited by 39 · see citing works
- Authors
- Hou, X., Lei, C.-U., & Kwok, Y.-K.
- Affiliations
- University of Hong Kong (HK)
- Venue
- 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)
- Publisher
- IEEE
- Published
- 2017-12
- Pages
- 936-939
- OpenAlex
- W2783336165
- Cited by
- 7
- References
- 8
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 2 · 2022: 1 · 2018: 1
- FWCI
- 0.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning and Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- K-means clustering, influential user identification, MOOC discussion forums, local optima avoidance, online learning, personalized learning strategies, learner clustering
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