Data mining approach to the identification of at-risk students

Data mining approach to the identification of at-risk students

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. Educa...

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Bibliographic Details
Main Authors: HO, Li Chin, SHIM, Kyong Jin
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2018
Subjects:
educational data mining
at-risk students
learning management systems
learning analytics
MITB student
Databases and Information Systems
Educational Assessment, Evaluation, and Research
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/4339
https://ink.library.smu.edu.sg/context/sis_research/article/5342/viewcontent/DataMining_At_Risk_Students_2018_av.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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