ABSTRACT
Machine learning algorithms may have very wide area of applications. In this paper we used machine learning algorithms to establish a method for evaluating the quality of education in secondary schools, depending on their past experience. The tool developed can be used for performance comparison between different schools and future score prediction. We collected and compared the results of almost 650 students from various regions of Nigeria to establish a relationship between their academic performance in internal and external exams. Internal exams are those conducted by their respective schools while external exams are those held by independent bodies, like WAEC and JAMB. We conducted a regression test on UTME (JAMB) scores and classification test on WASSCE (WAEC) scores. With simple but effective algorithms, we managed to reduce the mean squared error by %75 for regression model, and improved the prediction accuracy in classification by %35. Model development was done by using Python libraries. With a developed model, we compared performances of the schools from different regions in Nigeria. Results show that findings are acceptable and applicable for further use.
- Vanguard Newspaper, "Over 1.5m 2018 UTME candidates JAMB results released". Published on 20/03/2018. (Last accessed 01/01/2019). https://www.vanguardngr.com/2018/03/over-1--5m-2018-utme-candidates-jamb-results-released/Google Scholar
- Breiman, L. 2017. Classification and regression trees. Routledge.Google Scholar
- Clark, P. and Niblett, T., 1989. The CN2 induction algorithm. Machine learning, 3(4), pp.261--283.Google Scholar
- Huang, S. and Fang, N., 2010. Regression models of predicting student academic performance in an engineering dynamics course. In American Society for Engineering Education. American Society for Engineering Education.Google Scholar
- Joint Admission and Matriculation Board official. https://www.jamb.org.ng/ (Last accessed: 14/04/2019)Google Scholar
- West African Examination Council official. http://www.waecnigeria.org/ (Last accessed: 14/04/2019)Google Scholar
- Craig R., Vince K., Roger S., Adam L., Andrew J.. 2015. Student Success Regression Analysis Summary. UK IR Brief.Google Scholar
- Urrutia-Aguilar, M.E., Fuentes-García, R., Martínez, V.D.M., Beck, E., León, S.O. and Guevara-Guzmán, R., 2016. Logistic Regression Model for the Academic Performance of First-Year Medical Students in the Biomedical Area. Creative Education, 7(15), p.2202.Google ScholarCross Ref
- Casella G. and Berger R.L. 2002. Statistical Inference. 2nd edition, Duxbury Advanced Series, ISBN13: 978-0-534-24312-8Google Scholar
- Hastie, T., James, G., Witten, D. and Tibshirani, R., 2013. An introduction to statistical learning. Springer. New York.Google Scholar
- Pandey, M. and Sharma, V.K., 2013. A decision tree algorithm pertaining to the student performance analysis and prediction. International Journal of Computer Applications, 61(13).Google Scholar
- Ahmed, A.B.E.D. and Elaraby, I.S., 2014. Data mining: A prediction for student's performance using classification method. World Journal of Computer Application and Technology, 2(2), pp.43--47.Google ScholarCross Ref
- Yadav, S.K. and Pal, S., 2012. Data mining: A prediction for performance improvement of engineering students using classification. arXiv preprint arXiv:1203.3832.Google Scholar
- Godfred K. 2012. Multiple Regression Analysis of Assessment of Academic Performance of Students in the Ghanaian Polytechnics. Research on Humanities and Social Sciences, Vol 2, No.9.Google Scholar
- Saheed Y.K., T.O. Oladele, A.O. Akanni, W.M. Ibrahim. 2018. Students performance prediction based on data mining classification techniques. Nigerian Journal of Technology (NIJOTECH) Vol. 37, No. 4, pp. 1087--1091.Google ScholarCross Ref
Index Terms
- Development of a Method for Evaluating Quality of Education in Secondary Schools Using ML Algorithms
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