Publication:
Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data

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Issued Date

2021

Resource Type

Article

Language

eng

File Type

application/pdf

ISSN

7356331

DOI

10.1177/07356331211048777

Other identifier(s)

2-s2.0-85116044586

Rights Holder(s)

มหาวิทยาลัยศรีนครินทรวิโรฒ

Bibliographic Citation

Journal of Educational Computing Research. Vol , No. (2021)

Suggested Citation

Trakunphutthirak R., Lee V.C.S. Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data. Journal of Educational Computing Research. Vol , No. (2021). doi:10.1177/07356331211048777 Retrieved from: https://hdl.handle.net/20.500.14740/7981

Title

Application of Educational Data Mining Approach for Student Academic Performance Prediction Using Progressive Temporal Data

Author(s)

Trakunphutthirak R.
 Lee V.C.S.

Abstract

Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting student academic performance. The key aims of this study are fourfold. First, is to fill the educational literature gap by applying machine learning techniques in educational data mining, making use of the Internet usage behaviour log files and LMS data. Second, LMS data and Internet usage log files were analysed with machine learning techniques for predicting at-risk-of-failure students, with greater explanation added by combining student demographic data. Third, the demographic features help to explain the prediction in understandable terms for educators. Fourth, the study used a range of Internet usage data, which were categorized according to type of usage data and type of web browsing data to increase prediction accuracy. © The Author(s) 2021.

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URI

https://hdl.handle.net/20.500.14740/7981

Collections

Scopus: Year 1983-2021

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