Data mining approach to the identification of at-risk students

• 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
• Institution: Singapore Management University

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...