Big data helps SUSS assess student dropout risk

University, which caters mainly to adult learners, hopes to tackle high attrition rate

Data from SUSS' Business Intelligence and Analytics Unit can be used by the schools to provide earlier and more targeted support for students who need help with their university work. The unit is headed by Professor Koh Hian Chye (left, seated). With
Data from SUSS' Business Intelligence and Analytics Unit can be used by the schools to provide earlier and more targeted support for students who need help with their university work. The unit is headed by Professor Koh Hian Chye (left, seated). With him are (from left) Associate Professor Ludwig Tan, dean of the School of Humanities and Behavioural Sciences; Associate Professor Sylvia Chong, the unit's project lead; and Associate Professor Luke Peh, vice-dean of the School of Science and Technology. ST PHOTO: JASON QUAH
Amelia Teng
Education Correspondent
Updated
Published
August 15, 2019 at 5:00 AM
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A local university has turned to big data to tackle a key problem: the high attrition rate of its adult learners.

Using predictive analytics, the Singapore University of Social Sciences (SUSS), where one in five part-timers do not make it to the second year, can assess if a student is at risk of dropping out, even before starting school.

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A version of this article appeared in the print edition of The Straits Times on August 15, 2019, with the headline Big data helps SUSS assess student dropout risk. Subscribe

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