A Rule-Based System for Admission Recommendations in Nigerian Tertiary Institutions
DOI:
https://doi.org/10.62054/ijdm/0103.10Keywords:
Admission recommendations, Data-driven rules, Decision-making, Nigerian tertiary institutions, Rule-based SystemAbstract
This research presents a rule-based system to provide admission recommendations in Nigerian tertiary institutions. Unlike many existing studies that employ machine learning classifiers, this research aims to develop a transparent and deterministic system using predefined rules. The rule-based system recommends suitable courses based on candidates’ O’level results, Unified Tertiary Matriculation Examination (UTME) scores, computed post-UTME scores, and specific eligibility criteria. By focusing on a rule-based approach, the system mitigates the limitations and potential misclassifications associated with machine learning, thereby enhancing the efficiency and accuracy of the admissions process. The methodology follows predefined guidelines to ensure a transparent and understandable admissions procedure. This approach avoids the complexities and uncertainties of predictive models, providing an accurate and reliable method for course recommendations. The rule-based recommendation system ultimately aims to support educational institutions and admission offices in making informed decisions regarding candidate qualifications, eligibility, and course recommendations.
References
Aduwa, J. (2020). Population explosion in Nigeria: Causes, its effects on educational sector, and the ways forward. International Journal of Educational Research, 8(1), 139–144.
Al-Badarenah, A. & Alsakran, J. (2016). An automated recommender system for course selection. International Journal of Advanced Computer Science and Applications, 7(3), 166–175.
Aliyu, G., Haruna, U., Abdulmumin, I., Isma’il, M., Umar, I. E. & Adamu, S. (2021). Machine learning model for recommending suitable courses of study to candidates in Nigerian universities. In Computational science and its applications–ICCSA 2021: 21st international conference, Cagliari, Italy, September 13–16, 2021, proceedings, Part IX 21 (pp. 257-271). Springer International Publishing.
Asiyai, R. I. & Okoro, P. (2019). Management strategies for improving the functionality of tertiary education in Nigeria. International Journal of Higher Education, 8(4), 108–114.
Bloch, T. & Sacks, R. (2018). Comparing machine learning and rule-based inferencing for semantic enrichment of BIM models. Automation in Construction, 91, 256–272.
Deraman, N. A., Ariffin, W. W., Samah, K. A. F. A., Jono, M. N. H. H., Isa, M. A. M., Zamzuri, Z. F. & Yahaya, S. A. (2021, March). Covid19 and higher education: A degree course recommender system based on personality using rule-based. IOP Conference Series: Earth and Environmental Science, 704(1), 012021. IOP Publishing.
Divakara Rao, D. V., Manohar, P. M., Venkatesh, A., & Lokesh Kumar, A. (2023). Course recommendation system using machine learning. Dogo Rangsang Research Journal, 13(4), 64-75.
Etaga, H. O., Etaga, N.C., & Aforka, K. F. (2020). The relationship between post UTME and UTME scores of students admitted into universities in Nigeria. African Journal of Social Sciences and Humanities Research, 3(5), 88–96.
Okpa, O. E., Alade, F. O., Odigwe, F. N. & Sule, M. A. (2020). Managing dishonesty in students’ admission process: Implication for access to higher education in Nigeria. European Journal of Social Sciences, 60(2), 79–89.
Semako, G. E. (2021). Admission policies and quality teaching in universities in Lagos state, Nigeria: Implications for educational planners and policy makers. International Journal of Research and Innovation in Social Science, 5(2), 401-407.
Gupta, K. D. (2019). A survey on recommender system. International Journal of Applied Engineering Research, 14(14), 3274–3277.
Ibrahim, M. E., Yang, Y., Ndzi, D. L., Yang, G. & Al-Maliki, M. (2018). Ontology-based personalized course recommendation framework. IEEE Access, 7, 5180–5199.
Ikono, R., Babalola, A. & Iroju, O. (2017). A resource allocation model for admission into Nigerian Universities. Journal of Current Research, 9(04), 49188-49195.
Isma’il, M., Haruna, U., Aliyu, G., Abdulmumin, I. & Adamu, S. (2020). An autonomous courses recommender system for undergraduate using machine learning techniques. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS), (pp 1-6). IEEE.
Jacob, O. N. & Ndayebom, A. J. (2022). Problems faced by teaching programme in Nigerian public tertiary institutions and way forward. Kresna Social Science and Humanities Research, 3, 203–211.
Kennedy, I. & Ebuwa, S. O. (2020). Unified tertiary matriculation examination (UTME) and the post unified tertiary matriculation examination (PUTME) as predictors of undergraduate students’ final grades. International Journal of Interdisciplinary Research Methods, 7(3), 48–60.
Masri, N., Sultan, Y. A., Akkila, A. N., Almasri, A., Ahmed, A., Mahmoud, A. Y., Zaqout, I. & Abu-Naser, S. S. (2019). Survey of rule-based systems. International Journal of Academic Information Systems Research (IJAISR), 3(7), 1–23.
Mengash, H. A. (2020). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462–55470.
Ogunde, A. O. & Ajibade, E. (2019). A k-nearest neighbour algorithm-based recommender system for the dynamic selection of elective undergraduate courses. International Journal of Data Science and Analysis, 5(6), 128–135.
Ogunniran, M. O., Longlong, H. & Adu, E. O. (2019). A comparative analysis of higher education entrance examination: A case study of Chinese Gaokao and Nigeria WASSCE/UTME. African Educational Research Journal, 7(2), 66–80.
Portugal, I., Alencar, P. & Cowan, D. (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications, 97, 205– 227.
Ragab, A. H. M., Mashat, A. F. S. & Khedra, A. M. (2014). Design and implementation of a hybrid recommender system for predicting college admission. International Journal of Computer Information Systems and Industrial Management Applications, 6, 35–44.
Joshi, S., Jadhav, M., Londase, P. & Nikat, S. (2023). Career recommendation system. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 11(4), 3108–3113.
Urdaneta-Ponte, M. C., Mendez-Zorrilla, A. & Oleagordia-Ruiz, I. (2021). Recommendation systems for education: systematic review. Electronics, 10(14), 1611.
Wakil, K., Akram, B., Kamal, N. & Safi, A. (2014). Web recommender system for private universities’ admission in iraq: Uhd case study. International Journal of e-Education, e-Business, e-Management and e-Learning, 4(5), 329–340.
Wu, J. (2019, May 27). Knowledge-based recommendation systems: An overview. Retrieved from https://medium.com/@jwu2/knowledge-based-recommender-systems-an-overview-536b63721dba
Zabriskie, C., Yang, J., DeVore, S. & Stewart, J. (2019). Using machine learning to predict physics course outcomes. Physical Review Physics Education Research, 15 (2), 020120.̀̀
Downloads
Published
Data Availability Statement
The dataset is not available for public consumption
Issue
Section
License
Copyright (c) 2024 International Journal of Development Mathematics (IJDM)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors are solely responsible for obtaining permission to reproduce any copyrighted material contained in the manuscript as submitted. Any instance of possible prior publication in any form must be disclosed at the time the manuscript is submitted and a
copy or link to the publication must be provided.
The Journal articles are open access and are distributed under the terms of the Creative
Commons Attribution-NonCommercial-NoDerivs 4.0 IGO License, which permits use,
distribution, and reproduction in any medium, provided the original work is properly cited.
No modifications or commercial use of the articles are permitted.
