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

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/BigData.2018.8622495

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