A Quantitative Analysis of Preparedness and Performance: Predictive Indicators in First-Year Tertiary Mathematics
Gurudeo Anand Tularam1 Show affiliations
- Centre of Planetary Health and Food Security, Faculty of Science, Australia
Journal of Social Sciences, Volume 21, Issue 1, pp. 113–119 · doi.org/10.3844/jssp.2025.113.119
Abstract
Student preparedness at university entry plays a critical role in determining success in mathematics courses. This study investigates how three observable behaviours: (1) completion of weekly problem sets, (2) regular tutorial attendance, and (3) performance on a mid-semester test. We investigate how they relate to each other and their final examination results. Over five years, data were collected from 450 students (2014-2018) and analysed using correlation, multiple regression, and Partial Least Squares (PLS) regression. The analysis revealed that consistent completion of weekly problem-solving tasks was the most significant predictor of final exam performance, explaining more than 76% of the variance. Attendance accounted for approximately 21%, while mid-semester performance contributed only 3%. Collectively, the model explained 60% of the variation in final scores. These findings highlight the importance of sustained academic engagement and align with existing research on self-regulated learning and active participation.
Licence and reuse
Open access. This article is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.
How to reuse this articleResponses (0)
About this article
Tularam, G. A. (2025). A Quantitative Analysis of Preparedness and Performance: Predictive Indicators in First-Year Tertiary Mathematics. Journal of Social Sciences, 21(1), 113–119. https://doi.org/10.3844/jssp.2025.113.119
- Licence
- CC BY 4.0
- Issue
- Volume 21, Issue 1
- Policies
- Editorial independenceUse of generative AIData availabilityAppeals and complaints
- Version
- Version of record
