Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2018
Abstract
In recent years, the use of digital tools and technologies in educational institutions are continuing to generate large amounts of digital traces of student learning behavior. This study presents a proof-of-concept analytics system that can detect at-risk students along their learning journey. Educators can benefit from the early detection of at-risk students by understanding factors which may lead to failure or drop-out. Further, educators can devise appropriate intervention measures before the students drop out of the course. Our system was built using SAS ® Enterprise Miner (EM) and SAS ® JMP Pro.
Keywords
educational data mining, at-risk students, learning management systems, learning analytics, MITB student
Discipline
Databases and Information Systems | Educational Assessment, Evaluation, and Research | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
2018 IEEE International Conference on Big Data (Big Data): Seattle, December 10-13: Proceedings
First Page
5333
Last Page
5335
ISBN
9781538650356
Identifier
10.1109/BigData.2018.8622495
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
HO, Li Chin and SHIM, Kyong Jin.
Data mining approach to the identification of at-risk students. (2018). 2018 IEEE International Conference on Big Data (Big Data): Seattle, December 10-13: Proceedings. 5333-5335.
Available at: https://ink.library.smu.edu.sg/sis_research/4339
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/BigData.2018.8622495
Included in
Databases and Information Systems Commons, Educational Assessment, Evaluation, and Research Commons, Numerical Analysis and Scientific Computing Commons