Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria
American Journal of Computer Science and Technology
Volume 2, Issue 1, March 2019, Pages: 9-21
Received: Jun. 10, 2019;
Accepted: Jul. 5, 2019;
Published: Jul. 22, 2019
Views 85 Downloads 8
Sa’adatu Abdulkadir, Department of Computer Science, Nigerian Defence Academy, Kaduna, Nigeria
Francisca Nonyelum Ogwueleka, Department of Computer Science, Nigerian Defence Academy, Kaduna, Nigeria
The study aimed to determine if any of the entry requirements such as Ordinary Level (OL) results, Unified Tertiary Matriculation Examination (UTME) scores or Post-UTME (PUTME) scores could predict an outstanding academic performance of first-year undergraduate students admitted into the Faculty of Science in the Kaduna State University, Kaduna. The study adopted the descriptive research design. A purposive sample of nine hundred and forty-three (943) first-year students constituted the population for the study were drawn from Computer Science, Mathematics and Physics undergraduate degree programmes from the Faculty of Science of the university who were admitted from the 2010/2011 to 2014/2015 academic sessions. The instruments for data collection were OL, UTME and first-year Cumulative Grade Point Average (CGPA) results, which were coded and analysed with the aid of Computational Statistical Package for Social Sciences (SPSS). Pearson Product Moment Correlation (PPMC) Coefficient and Multinomial Logistics Regression (MLR) were the statistics used to answer the four research questions used. The results revealed that with a weak correlation, OL is a good predictor on the CGPA, a dependent variable, for academic performance which holds true for students who are in the CGPA category of '1st class' and '2nd Class Lower' respectively. It concluded that the use of OL and UTME as instruments is not enough to select candidates for admission and therefore recommended that other instruments such as senior secondary school mock examinations need to be included as part of the entry requirements in the admission criteria.
Francisca Nonyelum Ogwueleka,
Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria, American Journal of Computer Science and Technology.
Vol. 2, No. 1,
2019, pp. 9-21.
M. O. Afu and I. B. F. Ukofia, ‘Predictive Validity of UTME and Post-UTME Scores on first Year Students’ Performance in four Departments in University of Abuja’, International Journal of Education and Evaluation, vol. 3, no. 4, pp. 26–35, 2017.
A. S. Magaji, S. Abdulkadir, A. Peter, A. A. Muhammad, and I. A. Yushau, ‘An Evaluation of Students’ Admission Exercises (ESAE) in Kaduna State University, Nigeria’, International Journal of Educational Sciences, vol. 5, no. 2, pp. 131–135, 2013.
P. Ayuba, ‘Comparative Analysis of Post University Matriculation Examination in Nigerian Universities Using Fuzzy Logic’, British Journal of Mathematics & Computer Science, vol. 11, no. 6, pp. 1–10, Jan. 2015.
N. J. Okoli, L. Ogbondah, and R. N. Ewor, ‘The History and Development of Public Universities in Nigeria Since 1914’, International Journal of Education and Evaluation, vol. 2, no. 1, pp. 60–73, 2016.
D. Adesulu, ‘NUC moves towards harmonised CGPA for varsities — Investigations’, Vanguard News, Dec-2017.
V. Ramesh, P. PArkavi, and K. Ramar, ‘Predicting Student Performance: A Statistical and Data Mining Approach’, International Journal of Computer Applications, vol. 63, no. 8, pp. 35–39, 2013.
S. S. Olanipekun and J. K. Aina, ‘Improving Students’ Academic Performance in Nigerian Schools: The Role of Teachers’, International Journal of Research in Humanities and Social Studies, vol. 1, no. 2, pp. 1–6, 2014.
A. I. Joe, P. J. Kpolovie, K. E. Osonwa, and C. E. Iderima, ‘Modes of admission and academic performance in Nigerian Universities’, Merit Research Journal of Education and Review, vol. 2, no. 9, pp. 203–230, 2014.
Usamah bin Mat, N. Buniyamin, P. M. Arsad, and R. Kassim, ‘An overview of using academic analytics to predict and improve students’ achievement: A proposed proactive intelligent intervention’, presented at the IEEE 5th International Conference on Engineering Education (2013 ICEED), Selangor, Malaysia, 2013, pp. 126–130.
A. Kumar, ‘Study of Academic Achievement, Values and Adjustment of Secondary School Students in Relation to Working Status of Mothers’, PhD Thesis, Guru Nanak Dev University, Amritsar, 2010.
A. O. Patrick, ‘Three Years of Post UME Screening: Influence on Science Education Students’ Achievement in Delta State University, Abraka’, International Journal of Educational Science, vol. 2, no. 1, pp. 29–40, 2010.
E. C. Eze, ‘University Matriculation Examination as a Predictor of Students’ Final Grades in the Faculty of Health Sciences and Technology of University of Nigeria, Nsukka’, Master’s Thesis, University of Nigeria, Nsukka, 2014.
D. O. Igwue and O. Adikwu, ‘Measurement of intellectual functioning of Nigerian youth: the predictive validity of JAMB/UME in relation to students’ performance in University’, Educational Research, vol. 3, no. 8, pp. 639–644, 2012.
J. A. Odukoya et al., ‘The Predictive Validity of University Admission Examinations: Case Study of Nigerian Unified Tertiary Matriculation Examination’, Covenant International Journal of Psychology (CIJP), vol. 3, no. 1, pp. 1–10, 2018.
B. A. Faleye, ‘Predictive Validity of Students’ Entry Qualifications into Mathematics Programme in Nigeria’s Osun and Oyo States’ Colleges of Education’, Journal of Education and Human Development, vol. 4, no. 4, pp. 209–217, 2015.
B. B. AINA, ‘UTME and Post-UTME Scores as Predictor of Academic Performance of University Undergraduates of Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria’, International Journal of Education and Evaluation, vol. 3, no. 1, pp. 37–43, 2017.
P. J. Kpolovie, Advanced Research Methods. Owerri: Springfield Publishers, 2010.
T. O. Adeyemi, ‘The Effective use of Standard Scores for Research in Educational Management’, Research Journal of Mathematics and Statistics, vol. 3, no. 3, pp. 91–96, 2011.
A. Ahmad and S. Obiedat, ‘Correlation and Prediction for Preparatory Year Math and Engineering Math in University of Hail’, International Journal of Engineering Sciences & Research Technology (IJESRT), vol. 6, no. 1, pp. 323–329, 2017.
J. Starkweather and A. K. Moske, ‘Multinominal Logistic Regression’, 2011. [Online]. Available: https://it.unt.edu/sites/default/files/mlr_jds_aug2011.pdf. [Accessed: 11-Sep-2018].
S. O. Uhunmwuangho and O. Ogunbadeniyi, ‘The University Matriculation Examination as a Predictor of Performance in Post University Matriculation Examination: a Model for Educational development in the 21st Century’, African Research Review, vol. 8, no. 1, pp. 99–111, 2014.