Data Analytics for Student Profiling and Academic Counselling

Authors

  • Jizhi Li
  • Wai Ping Low
  • Lokesh Bheema Thiagarajan
  • Lu Chang Peh

Keywords:

data analytics, dashboards, profiling, prediction, early alert, student support

Abstract

Data analytics can be used by universities
student and learning data. By leveraging on data analytics and dashboards, universities and schools can become more proactive in profiling students and anticipating their needs, personalizing approaches to supporting students in academic distress and optimizing the allocation of university’s resources to efficiently and effectively counsel these students. In this paper, we outline the analytics framework that can be used on student data to derive insights, to readily observe and predict the students’ academic progression and performance, to characterise the academic risk of the student, and to identify the at-risk students at an early stage. With the early alert system in place, these students can then be counselled and rendered student support to be lifted out of the at-risk zone.

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Published

2022-08-04

How to Cite

Li, J., Low, W. P., Thiagarajan, L. B., & Peh, L. C. (2022). Data Analytics for Student Profiling and Academic Counselling. ASCILITE Publications, 196–205. Retrieved from https://publications.ascilite.org/index.php/APUB/article/view/263