An assessment of the Mathematics Value (MaV) scale’s Predictive Estimate’s Usability for Mathematics Better Performance Sustainability.
DOI:
https://doi.org/10.58579/AJB-SDR/7.1.2025.18Keywords:
Mathematics Value scale, Predictive, Innovative research, Performance sustainability.Abstract
Abstract
The statistics of the WASSCE success rates in the last one decade shown by the National Bureau of Statistics reveal significant trends, with some times marked by emotional achievements and others pressing areas demanding critical attention. Factors similar as changes in educational programs, resource allocation, and the impact of external events like the COVID-19 epidemic have all shaped these issues. Meanwhile, students' better performance in Mathematics is yet to be sustained. In hunting for success sustainability in Mathematics, this study was carried out. Out of the four factors of the MaVscale, reasoning (B = 0.14) and problem-solving (B = 0.01) skills predicted students’ achievement in mathematics appreciatively. The fitted model to predict students' mathematics achievement using the four subscales is Ŷ = 74.40 - 0.05 X1 - 0.20 X2 + 0.14 X3 + 0.01 X4. Mathematics value (MaV) scale and its four factors as good predictors of students’ Mathematics achievement was recognised as the areas that students and other educational stakeholders need to be more knowledgeable, while addressing the issue of students' better performance inconsistency. Thus, to fulfill sustainability development goals for Mathematics students' better performance, MaVscale reported to be clear, well-understood, can be used for diagnose and prediction approaches.
References
Adetula, O. (2024). Analysis of WASSCE Results from 2020-2024. https://ulesson.com/blog/
María Ferrer-Estévez, M. & Chalmeta, R. (2021). Integrating Sustainable Development
Goals in educational institutions. The international journal of Management Education.
(2). https://sciencedirect.com
Adom, D. & Donkor, E. (2024). Chapter 14: innovative research: what, why and
how? - business management & leadership strategies.
https://researchgate.net/publication/382524491
Apus, J. S. & Quirap, E. A. (2024). Factors Affecting Mathematics Performance: Basis for an
Intervention Plan. International journal of multidisciplinary research and analysis.
(04). https://researchgate.net/publications/
Chand, S., Chaudhary, K., Prasad, A. & Chand, V. (2021). Perceived Causes of Students’ Poor
Performance in Mathematics: A Case Study at Ba and Tavua Secondary Schools.
https://www.frontiersin.org/journal/applied-mathematics-and-statistics/articles/
Fapounda, O. (2024). WAEC Results Statistics: Key Insights and Analysis.
Ihekwaba, C. N. and Chukwuemaka, U. E. 2018. Integrating ethnomathematics into primary school mathematics curriculum for effective learning of geometry concepts. Journal of Educational research and Development (AJERD), 5, 2536-6899.
Isaac-Oloniyo, F., Adeleke, J. O. and Okpala, P. N. (2021). Validation of Value Scale Development (VSD) Theory for Student Mathematics Value (SMaV) Scale Development. The African Journal of Behavioural and Scale Development Research (AJB-SDR), 3(2), 2714-3449.
Isaac-Oloniyo, F.O. (2023). Development of mathematics value scale and its predictive
estimate on mathematics achievement among senior secondary I students in Nigeria.
An unpblished thesis in the international centre for educational evaluation (ICEE),
submitted to the institute of education, University of Ibadan.
Isaac-Oloniyo, F. O., Okpala, P. N. and Adeleke, J. O. (2024). Comparative Analyses of Gender, School Location, School Type and Students' Age in Mathematics Basic Skills Scale for Sustainable Development. The African Journal of Behavioural and Scale Development Research (AJB-SDR), 6(1), 2714-3449.
Reed, S.K. (2023). Cambridge University Press. Encouraging innovation Cognition,
education, and implementation , pp. 184 – 199.
Doi: https://doi.org/10.1017/9781009390408
Park W, Cullinane A, Gandolfi H, Alameh S, Mesci G. (2024). Innovations, Challenges and
Future Directions in Nature of Science Research: Reflections from Early Career
Academics. 54, 27–48, https://link.springer.com/article/10.1007//s11165-023-10102-z
Zhang, Y., Yun, Y., An, R., Cui, J., Dai, H. & Shang, X. (2021). Educational Data Mining
Techniques for Student Performance Prediction: Method Review and Comparison
Analysis. https://www.frontiersin.org/journal/psychology/articles
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 African Journal of Behavioural and Scale Development Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
