A Course Level Analysis of Academic Performance on Adult Learners


  • Jess Tan
  • Gabriel Gervais
  • Hian Chye Koh


blended learning, course design, data mining, academic performance


Thanks, in part, to the rapid development and widespread adoption of the Internet and other online technologies, academic institutions are increasingly using analytics to enhance learning and teaching. Through the use of data mining techniques, this study examines some of the determinants at a course level that affect the academic performance of adult learners (which we will refer to as students in this paper) in the Singapore University of Social Sciences (SUSS). Formerly known as SIM University, SUSS is an institution that caters mainly to the learning needs of working adults although it offers a number of full-time undergraduate degree programmes to fresh school leavers. The data analysis found that students taking introductory blended courses performed better than those who took face-to-face courses of the same level. Furthermore, students of similar age taking level-2 courses outperformed students taking similar courses where the age difference was more significant. The findings indicate that no single optimal course design will lead to improved academic performance across all courses. Instead, educators should be ready to consider the nature, level, discipline and coursework component of each course to cater to the various students’ needs.




How to Cite

Tan, J., Gervais, G., & Koh, H. C. (2022). A Course Level Analysis of Academic Performance on Adult Learners. ASCILITE Publications, 312–320. Retrieved from https://publications.ascilite.org/index.php/APUB/article/view/278