ASSESSING EDUCATION QUALITY IN MILITARY BASE SECONDARY SCHOOLS: A CANONICAL CORRELATION STUDY OF INPUTS AND OUTPUTS IN KADUNA STATE
Abstract
Education Quality (EQ) encompasses various factors influencing the effectiveness of an education system in achieving its learning objectives. This study assessed education quality in Military Base Secondary Schools (MBSS) in Kaduna State, Nigeria, using Canonical Correlation Analysis (CCA) on data from the 2020-2022 West Africa Senior Schools Certificate Examination (WASSCE). Factors analyzed included student performance in Mathematics, English Language, Chemistry, Physics, and Biology, and educational inputs such as student-teacher ratio (STR), average laboratory expenditure per student (AE), gender parity index for teachers (GPI), teachers’ teaching experience (TTE), and ratio of military to civilian staff (RMCS). Descriptive statistics showed significant disparities, notably in RMCS (mean = 16.73, SD = 18.65). A weak negative correlation (-0.217) between STR and AE and a moderate positive correlation (0.358) between TTE and GPI were found. A strong positive correlation (0.964) between Mathematics and English Language performance was also identified. The study highlighted that a higher proportion of military staff negatively impacts student performance, emphasizing the need for balanced staffing policies. The predictive model underscored the significant role of RMCS in education quality in MBSS The study recommends that military authorities and educational policymakers address staffing disparities, Efforts should ensure balanced staffing, optimize resource allocation for laboratory expenses, promote gender balance among teachers, and prioritize recruiting experienced educators. Additionally, integrated teaching approaches should reinforce the positive correlation between English language proficiency and mathematics performance.
References
Agyekum, G. O., Adarkwa, S. A., &Kusi, R. Y. (2023). Impact of Sample Size on Multicollinearity with High Dimensional Data in Logistic Regression Analysis. International Journal of Innovation and Development, 1(3), 122 – 133. Retrieved from https://ijid.kstu.edu.gh/index.php/ijid/article/view/26/22
Alhussam, M. A., Samanta, S., Ray, A. K., Kar, B., & Dibiat, N. (2024). Evaluating education quality as a research instrument: A systematic review. Multidisciplinary Reviews, 7(6), 2024115. https://doi.org/10.31893/multirev.2024115
Alvin. C.R. (2002). Method of Multivariate Analysis. 2nd edition, In: Wiley Interscience – a John wiley & sons, inc. publication. https://www.academia.edu/12748421/Methods_of_Multivariate_Book
Bayman, E. O. & Dexter, F. (2021). Multicollinearity in logistic regression models. Anesthesia & Analgesia, 133(2), 362-365. DOI: 10.1016/j.ijedro.2023.100280
Bolton, M. L., Biltekoff, E., & Humphrey, L. (2023). The mathematical meaninglessness of the NASA task load index: A level of measurement analysis. IEEE Transactions on Human-Machine Systems, 53(3), 590-599.
Buerkle, A., O’Dell, A., Matharu, H., Buerkle, L., & Ferreira, P. (2023). Recommendations to align higher education teaching with the UN sustainability goals–A scoping survey. International Journal of Educational Research Open, 5, 100280. 1 – 19. DOI:10.1016/j.ijedro.2023.100280
Darling-Hammond, L. (2010). Recruiting and retaining teachers: Turning around the race to the bottom in high-need schools. Journal of curriculum and instruction, 4(1), 16-32. DOI: 10.3776/joci.2010.v4n1p16-32
Dilhani E.V.D (2014), Evaluating Educational Production Function of The GCE (Ordinary Level) Students Through Canonical Correlation Analysis: A Case Study In Passara Zone, Sri Lanka, Economics, Commerce and Trade Management: An International Journal (ECTIJ), 3(1), 1-7. file:///C:/Users/DELL/Downloads/3621ectij01%20(2).pdf
Hooker, M. (2011). The Impact of Parental Military Status on the Achievement, Attendance, and Attitudes of Fourth Grade Students. Student Work. 52. (Doctoral dissertation)https://core.ac.uk/download/pdf/232753967.pdf
Jeremiah M. & George M. (2018). Modelling School Factors and Performance in Mathematics and Science in Kenyan Secondary Schools Using Canonical Correlation Analysis. International Journal of Computational and Theoretical Statistics. 5(2), 61 – 69.DOI: http://dx.doi.org/10.12785/ijcts/050201
Kanti V. Mardia, John T. Kent, and John Bibby (1995). Multivariate Analysis, Academic Press Harcourt Brace &Company, London San Diego New York Boston ‘Sydney Tokyo Toronto.
Karatas, Y. E., & Cinaroglu, S. (2024). Multivariate Relationships between Health Outcomes and Health System Performance Indicators: An Integrated Factor Analysis with Canonical Correlations. Value in Health Regional Issues, 40, 100-107.https://doi.org/10.1016/j.vhri.2023.10.00¬09
Khan, W. Z., & Al Zubaidy, S. (2017). Prediction of Student Performance in Academic and Military Learning Environment: Use of Multiple Linear Regression Predictive Morel and Hypothesis Testing. International Journal of Higher Education, 6(4), 152-160. https://files.eric.ed.gov/fulltext/EJ1151836.pdf
Kwagena G-B & Anthony O.G. (1991). Characteristics of Education Production Functions: An Application of Canonical Regression Analysis. Economics of Education Review. 10(1), 7 – 17.DOI:10.1016/0272-7757(91)90035-N
Kyriazos, T., & Poga, M. (2023). Dealing with multicollinearity in factor analysis: the problem, detections, and solutions. Open Journal of Statistics, 13(3), 404-424.DOI: 10.4236/ojs.2023.133020
Nelson J. (2014). Input–Output Relationship and the Quality of Education in Day Secondary Schools in Kenya. International Journal of Community and Corporative Studies. 1(2), 42-50.
Ole Nkaiwuatei, J. K. (2013). An analysis of critical factors affecting academic performance in secondary education in Kenya a case of Narok north district (Doctoral dissertation, University of Nairobi).
Ohaegbulem, E. U., & Chijioke, S. C. (2023). On Nigeria’s Budgetary Allocations to The Education Sector (1960-2023) In View of UNESCO’S Benchmarks. International Journal of Mathematics and Statistics Studies, 11(4), 32-44. DOI: 10.37745/ijmss.13/vol11n43244
Olasunkanmi, A. A., & Mabel, O. O. (2012). An input-output analysis of public and private secondary schools in Lagos, Nigeria. International Journal of Humanities and Social Science, 2(18), 85-96.
Otoibhi O.J. & Ubani S.E. (2020). Input and Output of the Educational System for 2011 – 2017 Academic Sessions of Public Secondary Schools in Three(3) Senatorial District of Edo State. Direct research journal of Education and Vocational Studies, 2(5), 79 – 88.
Philothere N. (2016). Educational inputs and their implications for output in public secondary schools in Nyarugenge and Nyamasheke districts, Rwanda (Doctoral dissertation, School of Education, Kenyatta University)
Rawal, D. M., & Das, K. (2023). Actualizing Equity: Perspectives on Operationalizing NEP 2020 in Practice. In Creating an Equitable Space for Teaching and Learning (pp. 125-136). Routledge India.
Tadese M., Alex Y. and Getaneh M.B. (2022). Determinants of Good Academic Performanceamong University Students in Ethiopia: A Cross-Sectional Study. BMC Med Educ. 5(22)- 395.DOI: 10.1186/s12909-022-03461-0
Tasi'u, M., Dikko, H. G., Shittu, O. I., & Fulatan, I. A. (2020). Specification of initial Kalman recursions of symmetric nonlinear state-space model. Heliyon, 6(10). 1 - 8https://doi.org/10.1016/j.heliyon.2020.e05152
Tilley, J. L. (2023). School resources, peer inputs, and student outcomes in adult education. Economics of Education Review, 96, 102441. https://doi.org/10.1016/j.econedurev.2023.102441
Worthington A.C. (2001). An empirical survey of frontier efficiency measurement techniques in education, Education Economics, 9(3), 245-268. https://doi.org/10.1080/09645290110086126
Copyright (c) 2024 FUDMA JOURNAL OF SCIENCES
This work is licensed under a Creative Commons Attribution 4.0 International License.
FUDMA Journal of Sciences
