Massive Open Online Course Enrollment Patterns: Association Rules Analysis for the Large Dataset
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Abstract
The research aimed to study the enrollment patterns on the Thai MOOC system among Thai users, and to compare the association rules of enrollment patterns on the Thai MOOC system, between users who were successful in at least 1 course and users who were not successful in any course. The data used for analysis was secondary data from the Thai MOOC with 4,691,359 rows, representing 1,339,191 users. The FPMAX algorithm was used to find frequent item sets, and association rules were analyzed by selecting rules with confidence and lift values higher than the 90th percentile. The research findings were as follows: 1) There were 217 rules in total for all users. When considering the specified criteria of confidence and lift values, 27 rules were found. Most of these rules were related to courses in the computer and technology domain. 2) In comparing the association rules of enrollment patterns between users who completed at least one course and those who did not, there were 383 and 327 rules, respectively. When considering the specified criteria of confidence and lift values, there were 39 and 33 rules, respectively. Most users who had succeeded in at least 1 course in enrollment had the behavior of across-associated course groups. On the other hand, the users who had not succeeded in any course tended to enroll in former course groups in a number not so much different from the number of the across-associated course groups.
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References
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A. I. (1994). Fast discovery of association rules. Advances in knowledge discovery and data mining, 12(1), 307-328. https://doi.org/10.5555/257938.257975
Aryabarzan, N., & Minaei-Bidgoli, B. (2021). Neclatclosed: A vertical algorithm for mining frequent closed itemsets. Expert Systems with Applications, 174. https://doi.org/10.1016/j.eswa.2021.114738
Fournier-Viger, P., Lin, J. C. W., Kiran, R. U., Koh, Y. S., & Thomas, R. (2017). A survey of sequential pattern mining. Data Science and Pattern Recognition, 1(1), 54-77. https://doi.org/10.1002/widm.1207
Hahsler, M., & Hornik, K. (2008). New probabilistic interest measures for association rules. Intelligent Data Analysis, 11(5), 437-455. https://doi.org/10.3233/IDA-2007-11502
Hikmawati, E., Maulidevi, N. U., & Surendro, K. (2021). Minimum threshold determination method based on dataset characteristics in association rule mining. Journal of Big Data, 8, 1-17. https://doi.org/10.1186/s40537-021-00538-3
Hikmawati, E., & Surendro, K. (2020). How to determine minimum support in association rule. In Proceedings of the 2020 9th International Conference on Software and Computer Applications, 6-10. https://doi.org/10.1145/3384544.3384563
Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE transactions on knowledge and data engineering, 12(3), 372-390. https://doi.org/10.1109/69.846291
Kesorn, K. (2021). Data Science. Department of Computer Science and Information Technology, Faculty of Science Naresuan University. https://csit.nu.ac.th/kraisak/ds/ds/chapter09/Chapter09.pdf (in Thai)
Serisathian, N. (2021). Let's get acquainted with Association Rule: a tool for market basket analysis!. Big data institute. https://bdi.or.th/ (in Thai)
Thailand Cyber University. (2024, April 4). Thai MOOC. https://thaicyberu.go.th/ (in Thai)
Thailand Science Research and Innovation. (2024). Thai MOOC (Thailand Massive Open Online Course). Knowledge Management and Education System Enhancement Project. https://satedu.tsri.or.th/ (in Thai)
Thammetha, T., Theerarueangchaisri, A., & Klaisang, J. (2023). Work based skill MOOCs: Personalized Learning Pathway for Digital Learners. Journal of teacher professional development, 4(3), 36-49. (in Thai)