Skip to main content
SpringerLink
Log in

Data mining approach to predicting the performance of first year student in a university using the admission requirements

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

The academic performance of a student in a university is determined by a number of factors, both academic and non-academic. Student that previously excelled at the secondary school level may lose focus due to peer pressure and social lifestyle while those who previously struggled due to family distractions may be able to focus away from home, and as a result excel at the university. University admission in Nigeria is typically based on cognitive entry characteristics of a student which is mostly academic, and may not necessarily translate to excellence once in the university. In this study, the relationship between the cognitive admission entry requirements and the academic performance of students in their first year, using their CGPA and class of degree was examined using six data mining algorithms in KNIME and Orange platforms. Maximum accuracies of 50.23% and 51.9% respectively were observed, and the results were verified using regression models, with R2 values of 0.207 and 0.232 recorded which indicate that students’ performance in their first year is not fully explained by cognitive entry requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Adeogun, A., Subair, S., & Osifila, G. (2009). Deregulation of university education in Nigeria: Problems and prospects. Florida Journal of Educational Administration & Policy, 3, 1–8.

    Google Scholar 

  • Adeyemi, K. (2001). Equality of access and catchment area factor in university admissions in Nigeria. Higher Education, 42, 307–332.

    Article  Google Scholar 

  • Agarwal, S., Pandey, G., & Tiwari, M. (2012). Data mining in education: Data classification and decision tree approach. International Journal of e-Education, e-Business, e-Management and e-Learning, 2, 140.

    Google Scholar 

  • Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data mining: A prediction for Student's performance using classification method. World Journal of Computer Application and Technology, 2, 43–47.

    Google Scholar 

  • Ahuja, R. & Kankane, Y. (2018). Predicting the probability of student's degree completion by using different data mining techniques. 474–477.

  • Ahuja, R., Jha, A., Maurya, R. & Srivastava, R. (2019). Analysis of educational data mining. 4th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2018, Gurgaon; India. Advances in Intelligent Systems and Computing, 897–907.

  • Aina, O. I. 2002. Alternative modes of financing higher education in Nigeria and the implications for university governance. Africa Development/Afrique et Développement, 236–262.

  • Ajadi, T. O., Salawu, I. O. & Adeoye, F. A. (2008). E-learning and distance education in Nigeria. Online Submission, 7.

  • Ajayi, I., & Ekundayo, H. T. (2008). The deregulation of university education in Nigeria: Implications for quality assurance. Nebula, 5, 212–224.

    Google Scholar 

  • Almarabeh, H. (2017). Analysis of Students' performance by using different data mining classifiers. International Journal of Modern Education and Computer Science, 9, 9.

    Article  Google Scholar 

  • Aluede, O., Idogho, P. O. & Imonikhe, J. S. (2012). Increasing access to university education in Nigeria: Present challenges and suggestions for the future. The African Symposium. 3–13.

  • Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computers and Education, 113, 226–242.

    Article  Google Scholar 

  • Arsad, P. M. & Buniyamin, N. (2014). Neural Network and Linear Regression methods for prediction of students' academic achievement. Global Engineering Education Conference (EDUCON), IEEE, 2014. IEEE, 916–921.

  • Atta UR, R., Sultan, K., Aldhafferi, N., & Alqahtani, A. (2018). Educational data mining for enhanced teaching and learning. Journal of Theoretical and Applied Information Technology, 96, 4417–4427.

    Google Scholar 

  • Azevedo, A. (2018). Data mining and knowledge discovery in databases. In Encyclopedia of information science and technology, fourth edition. IGI Global.

  • Babalola, J. B. (1998). Cost and financing of university education in Nigeria. Higher Education, 36, 43–66.

    Article  Google Scholar 

  • Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4, 17.

    Article  Google Scholar 

  • Bharara, S., Sabitha, S., & Bansal, A. (2018). Application of learning analytics using clustering data Mining for Students’ disposition analysis. Education and Information Technologies, 23, 957–984.

    Article  Google Scholar 

  • Bhise, R., Thorat, S., & Supekar, A. (2013). Importance of data mining in higher education system. IOSR Journal Of Humanities And Social Science (IOSR-JHSS), 6, 18.

    Article  Google Scholar 

  • Bucos, M., & Drăgulescu, B. (2018). Predicting student success using data generated in traditional educational environments. TEM Journal, 7, 617–625.

    Google Scholar 

  • Burgos, C., Campanario, M. L., Peña, D. D. L., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers and Electrical Engineering, 66, 541–556.

    Article  Google Scholar 

  • Burke, A. S., & Fedorek, B. (2017). Does “flipping” promote engagement?: A comparison of a traditional, online, and flipped class. Active Learning in Higher Education, 18, 11–24.

    Article  Google Scholar 

  • Daradoumis, T., Marquès Puig, J. M., Arguedas, M., & Calvet Liñan, L. (2019). Analyzing students’ perceptions to improve the design of an automated assessment tool in online distributed programming. Computers and Education, 128, 159–170.

    Article  Google Scholar 

  • Ebbeler, J. (2013). Introduction to ancient Rome,' the flipped version. The Chronicle of Higher Education [online], 59. Available: https://www.chronicle.com/article/Introduction-to-Ancient/140475/.

  • Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G. V. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 335–343.

    Article  Google Scholar 

  • Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. (2018). Educational data mining and analysis of students’ academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9, 447–459.

    Article  Google Scholar 

  • Hussain, S., Atallah, R., Kamsin, A. & Hazarika, J. (2019). Classification, clustering and association rule mining in educational datasets using data mining tools: A case study.

  • Ibrahim, Z. M., Bader-El-Den, M. & Cocea, M. (2019). Mining unit feedback to explore students’ learning experiences. Advances in Intelligent Systems and Computing.

  • Idachaba, F. (2018a). Development of a rapid mentoring scheme for managing large classes in engineering departments. INTED2018 Conference, 5th–7th March 2018 2018a Valencia, Spain. 5725–5731.

  • Idachaba, F. (2018b). Outcome based engineering curriculum design: a system for curriculum streamlining and graduate quality improvement in engineering. INTED2018 Conference, 5th–7th March 2018 2018b Valencia, Spain. 5888–5893.

  • Kaur, N., & Kaur, J. (2018). Performance evaluation of data mining classification in educational system using genetic algorithm. International Journal of Advanced Science and Technology, 114, 127–138.

    Article  Google Scholar 

  • Khan, A., & Ghosh, S. K. (2018). Data mining based analysis to explore the effect of teaching on student performance. Education and Information Technologies, 23, 1677–1697.

    Article  Google Scholar 

  • Khedr, A. E., & El Seddawy, A. I. (2015). A proposed data mining framework for higher education system. International Journal of Computer Applications, 113, 24–31.

    Article  Google Scholar 

  • Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea. Computers and Education, 127, 233–251.

    Article  Google Scholar 

  • Kostopoulos, G., Kotsiantis, S., Pierrakeas, C., Koutsonikos, G., & Gravvanis, G. A. (2018). Forecasting students' success in an open university. International Journal of Learning Technology, 13, 26–43.

    Article  Google Scholar 

  • Kurt, G. (2017). Implementing the flipped classroom in teacher education: Evidence from Turkey. Educational Technology and Society, 20, 211–221.

    Google Scholar 

  • Lynch, C. F. (2017). Who prophets from big data in education? New insights and new challenges. Theory and Research in Education, 15, 249–271.

    Article  Google Scholar 

  • Nikolic, S. & Nicholls, B. (2018). Exploring student interest of online peer assisted learning using mixed-reality technology.

  • Nurhayati, O. D., Bachri, O. S., Supriyanto, A., & Hasbullah, M. (2018). Graduation prediction system using artificial neural network. International Journal of Mechanical Engineering and Technology, 9, 1051–1057.

    Google Scholar 

  • Odukoya, J. A., Popoola, S. I., Atayero, A. A., Omole, D. O., Badejo, J. A., John, T. M., & Olowo, O. O. (2018). Learning analytics: Dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university. Data in Brief, 17, 998–1014.

    Article  Google Scholar 

  • Oguntunde, P., Okagbue, H., Oguntunde, O. A., & Opanuga, A. (2018). Analysis of the inter-relationship between students’ first year results and their final graduating grades. International Journal of Advanced and Applied Sciences, 5, 1–6.

    Article  Google Scholar 

  • Olsson, M. & Mozelius, P. (2016). On design of online learning environments for programming education. Proceedings of the European Conference on e-Learning, ECEL. 533–539.

  • Osmanbegović, E. & Suljic, M. (2012). Data mining approach for predicting student performance.

  • Popoola, S. I., Atayero, A. A., Badejo, J. A., Odukoya, J. A., Omole, D. O., & Ajayi, P. (2018). Datasets on demographic trends in enrollment into undergraduate engineering programs at Covenant University, Nigeria. Data in Brief, 18, 47–59.

    Article  Google Scholar 

  • Rodrigues, M. W., Isotani, S., & Zárate, L. E. (2018). Educational data mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35, 1701–1717.

    Article  Google Scholar 

  • Roy, S. & Garg, A. (2018). Predicting academic performance of student using classification techniques. 2017 4th IEEE Uttar Pradesh section international conference on electrical, computer and electronics, UPCON 2017. 568–572.

  • Ryan, S. J. D., & Baker. (2010). Data mining for education. International encyclopedia of education, 7, 112–118.

    Google Scholar 

  • Saint, W., Hartnett, T. A., & Strassner, E. (2003). Higher education in Nigeria: A status report. Higher Education Policy, 16, 259–281.

    Article  Google Scholar 

  • Senthil, S. & Lin, W. M. (2018). Applying classification techniques to predict students' academic results. 2017 IEEE International Conference on Current Trends in Advanced Computing, ICCTAC 2017. 1–6.

  • Sivakumar, S. & Selvaraj, R. 2018. Predictive modeling of students performance through the enhanced decision tree. Lecture Notes in Electrical Engineering.

  • Tair, M. M. A., & El-Halees, A. M. (2012). Mining educational data to improve students' performance: A case study. International Journal of Information and Communication Technology Research, 2, 140–146.

    Google Scholar 

  • Van WYK, M. M. (2018). Economics student teachers’ views on the usefulness of a flipped classroom pedagogical approach for an open distance eLearning environment. International Journal of Information and Learning Technology, 35, 255–265.

    Article  Google Scholar 

  • Yadav, S. K., Bharadwaj, B. & PAL, S. (2012). Mining education data to predict student's retention: A comparative study. arXiv preprint arXiv:1203.2987.

Download references

Acknowledgements

The Authors appreciate Covenant University Centre for Research, Innovation and Development for the commitment to innovative research, and for providing an enabling research environment.

Author information

Authors and Affiliations

  1. Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State, Nigeria

    Aderibigbe Israel Adekitan & Etinosa Noma-Osaghae

Authors
  1. Aderibigbe Israel Adekitan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Etinosa Noma-Osaghae
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aderibigbe Israel Adekitan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Adekitan, A.I., Noma-Osaghae, E. Data mining approach to predicting the performance of first year student in a university using the admission requirements. Educ Inf Technol 24, 1527–1543 (2019). https://doi.org/10.1007/s10639-018-9839-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-018-9839-7

Keywords

Search

Navigation