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Abstract
This research involves time series analysis and forecasting of the Ordinary Level Mathematics results. During 2008 to 2009 many educators had left the country seeking higher salaries, leading to severe staff shortages in schools. This and other reasons were anticipated to impact negatively on “O” Level Mathematics results. Data for the years 2010 to 2022 were collected from one high school in Bulawayo. The data were meticulously analysed, with a particular emphasis on identifying the underlying trend through data smoothing techniques. Advanced computer software R studio was used to conduct regression analysis and generate trend lines and time series graphs. Additionally, an ARIMA (autoregressive integrated moving average) forecasting model was applied to forecast and predict the school’s future pass rate in Ordinary Level Mathematics. Interviews with the 10 qualified teachers at the school were carried out to identify potential causes for changes in the pass rate of Mathematics at the High School. By understanding how pass rates may evolve over time, stakeholders like parents, the school and government authorities can make informed decisions and implement strategic measures to enhance academic performance in the long run. The study noted that the pass rate of the school was going to slightly improve, and the school was therefore recommended to deploy the hardworking teachers, who had a history of improving the pass rate to the examination writing classes so that the pass rate could increase uniformly and to give them more teaching and revision time and incentives. The study also recommends further research.
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Copyright (c) 2026 Silvanos Chirume, Edward Nsingo

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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- Mwale-Mkandawire M. (2020). Current Trends in Gender Issues in Education. UNZA Press, Lusaka, Retrieved on 06/04/25 from https://www.researchgate.net/publication/349462853_Mwale-Mkandawire_2020
- Suleiman, I.B., Okunade, O.A., Dada, E.G., & Ezeanya, U.C. (2024). Key factors influencing students’ academic performance. Journal of Electrical Systems and Information Technology, 11, Article 41. https://doi.org/10.1186/s43067-024-00166-w
- Takawira, M. (2016). Assessment of the Economic Impact. https://uz.ac.zw/index.php.
- Taylor, J.W. (2013). Exponential smoothing and ARIMA processes. International Journal of Forecasting, 29(2), 264-276
- Wei-Yin, L. (2021). Guide Classification and Regression Trees and Forests. https://pages.stat.wisc.edu>~loh.
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References
Box, G.E.P. & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Cambridge University Press, Oakland.
Box, G.E.P. & Jenkins, G.M. (1970). Time Series Analysis Forecasting and Control. Cambridge University Press, Oakland.
Brown, G.T.L. (20). The past the present and future of educational assessment: A transdisciplinary perspective. Frontiers in Education, 7, Article 1060633. https://doi.org/10.3389/feduc.20.10606033
Brozova, H. (2014): Exam Results Analysis. https://www.researchgate.net>33234. Chatfield, C. (2018). Introduction to Multivariate Analysis. Abindon-on-Thames. Routledge.
Choosing the right Trendline type for your data (n.d.) Retrieved on 02/04/25 from https://www.tcworkshop.com/data/Downloads/Handouts/Microsoft_Excel_ChartTrendlines.pdf
Choi, T. H. (2017). Hidden transcripts of teacher resistance: A case of South Korea. Teaching and Teacher Education, 63, 245-255
Crismono, P., Saputra, M. A. A., & Hudi, S. (2026). Evolution of Deep Learning and Its Reflection on Statistical Mathematics Learning. International Journal of Review in Mathematics Education, 50-69.
Fagerholm, A. (2019). The future of technology. www.researchgate.net
Hanushek, E., Machin, S. & Woessman, L.(2011). Handbook of the Economics Education (Vol.4). Elsevier.
Harrell, F. E. (2013).Regression modelling strategies: With applications to linear models, logistic regression, and survival analysis. Springer. New York.
Hyndman, R.J. & Athanasopoulos, G. (2018). Forecasting Principles and Practice. Springer, Berlin.
Kabir, S.M.S.(2016). Methods of data collection. In Basic guidelines for research: An introductory approach for all disciplines (pp.201-275). Book Zone Publication.
Karapanagiotidis, P. (2012). Literature review of modern time series forecasting methods, Retrieved on /03/25 from S2012 Literature Review - linear survey.July31.pdf
Ladd, H., F.(2011).Teacher Motivation and student Achievement. Journal of Human Resources, 46(2), 361-384.
Loftin, C., Reyes, H., Hartin, V. & Rice, L. (2020). A closer look at first-time pass rates as the primary measure of program quality, Journal of Professional Nursing 36, pp.707-711
Liu, Z.; Cui, Y.; Ding, C.;Gan, Y.; Luo, J.; Luo, X.; Wang, Y. (2024). The Characteristics of ARMA (ARIMA) Model and Some Key Points to Be Noted in Application: A Case Study of Changtan Reservoir, Zhejiang Province, China. Sustainability 16(18), 7955; https://doi.org/10.3390/su16187955
Lundeberg, S. & Lee, S. (2017). A Unified Approach to interpreting Models Predictions. https://dol.org/10.48550/arXiv.1705.07874.
Malik, A. (2023). The future of technology. Elsevier Ltd. Amsterdam.
Mangwende, E. (2026). Social Constructivism: Principles and Implications to Mathematics Learning. International Journal of Review in Mathematics Education, 70-79.
Mehtab, S. & Sen, J. (2020), A Time Series Analysis-Based Stock Price Prediction Framework Using Machine Learning and Deep Learning Models, Technical Report School of Computing and Analytics, NSHM Knowledge Campus, India, pp. 1-46
Mwale-Mkandawire M. (2020). Current Trends in Gender Issues in Education. UNZA Press, Lusaka, Retrieved on 06/04/25 from https://www.researchgate.net/publication/349462853_Mwale-Mkandawire_2020
Suleiman, I.B., Okunade, O.A., Dada, E.G., & Ezeanya, U.C. (2024). Key factors influencing students’ academic performance. Journal of Electrical Systems and Information Technology, 11, Article 41. https://doi.org/10.1186/s43067-024-00166-w
Takawira, M. (2016). Assessment of the Economic Impact. https://uz.ac.zw/index.php.
Taylor, J.W. (2013). Exponential smoothing and ARIMA processes. International Journal of Forecasting, 29(2), 264-276
Wei-Yin, L. (2021). Guide Classification and Regression Trees and Forests. https://pages.stat.wisc.edu>~loh.
Wijesinghe, S. (2020), Time series forecasting: Analysis of LSTM Neural Networks to Predict Exchange Rates of Currencies. Instrumentation 7(4), pp. 25-39
Xu, C.; Li, J.; Feng, B.; Lu, B. (2023), A Financial Time-Series Prediction Model Based on Multiplex Attention and Linear Transformer Structure. Appl. Sci. 2023, 13, 5175. https:// doi.org/10.3390/app13085175
Zhang, M. (2028). Time Series: Autoregressive models AR, MA, ARMA, ARIMA. Retrieved on 4/04/25 from https://people.cs.pitt.edu/~milos/courses/cs3750/lectures/class16.pdf

