Using learning analytics in higher education: Assessing students' learning experience in an actuarial science course

Lay Guat Chan; Qian Yun Ng; Lay Guat Chan; Qian Yun Ng

doi:10.3934/steme.2024010

STEM Education

2024, Volume 4, Issue 2: 151-164. doi: 10.3934/steme.2024010

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Using learning analytics in higher education: Assessing students' learning experience in an actuarial science course

Lay Guat Chan ^,,
Qian Yun Ng

School of Mathematical Sciences, Sunway University, 47500 Bandar Sunway, Selangor, Malaysia

Academic Editor: Siow Li Lai

Received: 02 February 2024 Revised: 22 March 2024 Accepted: 15 April 2024 Published: 22 April 2024

It is becoming increasingly evident that educators need to prioritize the welfare of their students, particularly those who are underperforming academically, also known as "students at risk". By analyzing learning behaviors, including attendance records, past academic results, and online interactions, we can identify students at risk and provide them with timely support. Therefore, this study aimed to develop a prediction model for identifying students at risk in an actuarial science course and suggest an intervention strategy. Our study was comprised of five components of learning analytics: data collection, reporting, prediction, intervention, and reassessment. Prior to applying a prediction model, correlation analysis was utilized to identify variables impacting students' academic performance. Three variables, including CGPA, pre-requisite subject marks, and assessment marks were considered due to their rather strong correlation with the final marks of the course. Then, quadratic discriminant analysis (QDA) was applied to predict students classified as "at risk" and "not as risk". Out of 69 students from the course, 15 students identified as "at risk" and 40 students participated in the Peer Assisted Learning Program (PALP) as an intervention strategy to reduce the course's failure rate. We cannot conclude whether PALP was an effective intervention strategy for students at risk because a majority of them failed to attend. However, we observed that those who attended PALP had a higher likelihood of passing the course. Our prediction model had high rates of accuracy, precision, sensitivity, and specificity, which were 91%, 98%, 91%, and 91%, respectively. Therefore, QDA could be considered a robust model for predicting students at risk. We have outlined some limitations and future studies at the end of our study.
- learning analytics,
- correlation analysis,
- quadratic discriminant analysis,
- intervention,
- student at risk
Citation: Lay Guat Chan, Qian Yun Ng. Using learning analytics in higher education: Assessing students' learning experience in an actuarial science course[J]. STEM Education, 2024, 4(2): 151-164. doi: 10.3934/steme.2024010

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Abstract

It is becoming increasingly evident that educators need to prioritize the welfare of their students, particularly those who are underperforming academically, also known as "students at risk". By analyzing learning behaviors, including attendance records, past academic results, and online interactions, we can identify students at risk and provide them with timely support. Therefore, this study aimed to develop a prediction model for identifying students at risk in an actuarial science course and suggest an intervention strategy. Our study was comprised of five components of learning analytics: data collection, reporting, prediction, intervention, and reassessment. Prior to applying a prediction model, correlation analysis was utilized to identify variables impacting students' academic performance. Three variables, including CGPA, pre-requisite subject marks, and assessment marks were considered due to their rather strong correlation with the final marks of the course. Then, quadratic discriminant analysis (QDA) was applied to predict students classified as "at risk" and "not as risk". Out of 69 students from the course, 15 students identified as "at risk" and 40 students participated in the Peer Assisted Learning Program (PALP) as an intervention strategy to reduce the course's failure rate. We cannot conclude whether PALP was an effective intervention strategy for students at risk because a majority of them failed to attend. However, we observed that those who attended PALP had a higher likelihood of passing the course. Our prediction model had high rates of accuracy, precision, sensitivity, and specificity, which were 91%, 98%, 91%, and 91%, respectively. Therefore, QDA could be considered a robust model for predicting students at risk. We have outlined some limitations and future studies at the end of our study.

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