Using Analytics to Uncover Early Determinants of Academic Performance for Adult Learners by IAFOR
Using Analytics to Uncover Early Determinants of Academic Performance for Adult Learners by IAFOR
https://ift.tt/329qEL7
53465 By and large, the arrival of the digital age have accelerated the development of analytics to guide data-informed efforts in teaching and learning. This has also transformed the way how higher education institutions look to optimize student success. In this study, through the use of data mining techniques, the UNIVERSITY* gained a better understanding of variables that influenced the adult learners first year academic performance. In particular, the results from the CHAID (or Chi-squared Automatic Interaction Detector) model highlighted the importance of previous academic performance and behavioural variables such as credit units taken and withdrawn in predicting learners at risk. The findings resonated with the opinion that an adult learner may find it challenging to juggle the demands of higher education, work-life, and family-life concurrently, at the onset. Henceforth, this group of struggling adult learners may benefit from a better management of course loading, as early as possible. Sylvia Chong, Singapore University of Social Sciences, Singapore Yew Haur Lee, Singapore University of Social Sciences, Singapore
Uploaded 2019-11-01T01:31:40.000Z
https://ift.tt/329qEL7
53465 By and large, the arrival of the digital age have accelerated the development of analytics to guide data-informed efforts in teaching and learning. This has also transformed the way how higher education institutions look to optimize student success. In this study, through the use of data mining techniques, the UNIVERSITY* gained a better understanding of variables that influenced the adult learners first year academic performance. In particular, the results from the CHAID (or Chi-squared Automatic Interaction Detector) model highlighted the importance of previous academic performance and behavioural variables such as credit units taken and withdrawn in predicting learners at risk. The findings resonated with the opinion that an adult learner may find it challenging to juggle the demands of higher education, work-life, and family-life concurrently, at the onset. Henceforth, this group of struggling adult learners may benefit from a better management of course loading, as early as possible. Sylvia Chong, Singapore University of Social Sciences, Singapore Yew Haur Lee, Singapore University of Social Sciences, Singapore
Uploaded 2019-11-01T01:31:40.000Z
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