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

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.

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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.

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Correspondence to Aderibigbe Israel Adekitan.

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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

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

Keywords

  • Academic performance
  • Machine learning
  • Educational data mining
  • Data mining algorithms
  • Knowledge discovery
  • Nigerian university