Journal of Education and Research

Data Mining Applications Used in Education Sector
Sushil Shrestha 1 * , Manish Pokharel 1
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1 Digital Learning Research Lab, Department of Computer Science and Engineering, Kathmandu University, Dhulikhel, Nepal* Corresponding Author
Original Article

Journal of Education and Research, Volume 10, Issue 2, 2020, 27-51, https://doi.org/10.3126/jer.v10i2.32721

Publication date: Nov 11, 2020

Views: 41 | Downloads: 29

How to cite this article
APA
In-text citation: (Shrestha & Pokharel, 2020)
Reference: Shrestha, S., & Pokharel, M. (2020). Data Mining Applications Used in Education Sector. Journal of Education and Research, 10(2), 27-51. https://doi.org/10.3126/jer.v10i2.32721
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Shrestha S, Pokharel M. Data Mining Applications Used in Education Sector. Journal of Education and Research. 2020;10(2):27-51. https://doi.org/10.3126/jer.v10i2.32721
AMA
In-text citation: (1), (2), (3), etc.
Reference: Shrestha S, Pokharel M. Data Mining Applications Used in Education Sector. Journal of Education and Research. 2020;10(2), 27-51. https://doi.org/10.3126/jer.v10i2.32721
Chicago
In-text citation: (Shrestha and Pokharel, 2020)
Reference: Shrestha, Sushil, and Manish Pokharel. "Data Mining Applications Used in Education Sector". Journal of Education and Research 2020 10 no. 2 (2020): 27-51. https://doi.org/10.3126/jer.v10i2.32721
Harvard
In-text citation: (Shrestha and Pokharel, 2020)
Reference: Shrestha, S., and Pokharel, M. (2020). Data Mining Applications Used in Education Sector. Journal of Education and Research, 10(2), pp. 27-51. https://doi.org/10.3126/jer.v10i2.32721
MLA
In-text citation: (Shrestha and Pokharel, 2020)
Reference: Shrestha, Sushil et al. "Data Mining Applications Used in Education Sector". Journal of Education and Research, vol. 10, no. 2, 2020, pp. 27-51. https://doi.org/10.3126/jer.v10i2.32721
ABSTRACT
The purpose of this work is to study the usage trends of Data Mining (DM) methods in education. It discusses different data mining techniques used for different types of educational data. The related papers were initially selected from the metadata containing words like Online Learning (OL) and Educational Data Mining (EDM). The papers were then filtered on the basis of DM algorithms, the purpose of study, and the types of data used. The findings suggested that EDM is the most commonly used technique for the prediction of students’ academic success, and the most used purpose is classification, followed by clustering and association. Further, this research also contains the study conducted on moodle data to find anomalies. K-means clustering was applied to find the optimal number of clusters on moodle data that consists of log and quiz dataset. The growth in the number of Internet users has increased learning through the online process. Hence, several activities are performed in OL systems, which generate a massive amount of data to be analysed to obtain useful information. Therefore, this type of research is very beneficial to academicians and instructors to identify the learner’s behaviors and develop suitable models.
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