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Data Mining Approach To The Identification Of At-Risk Students, Li Chin Ho, Kyong Jin Shim
Data Mining Approach To The Identification Of At-Risk Students, Li Chin Ho, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
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.