Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria

Sa’adatu Abdulkadir; Francisca Nonyelum Ogwueleka

doi:doi:10.11648/j.ajcst.20190201.12

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Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria

Sa’adatu Abdulkadir, Francisca Nonyelum Ogwueleka

Published in American Journal of Computer Science and Technology (Volume 2, Issue 1)

Received: 10 June 2019 Accepted: 5 July 2019 Published: 22 July 2019

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Abstract

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.

Published in	American Journal of Computer Science and Technology (Volume 2, Issue 1)
DOI	10.11648/j.ajcst.20190201.12
Page(s)	9-21
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Ordinary Level, Unified Tertiary Matriculation Examination (UTME), Post-UTME, Students, Prediction, Academic Performance, Entry-Level

References

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APA Style

Sa’adatu Abdulkadir, Francisca Nonyelum Ogwueleka. (2019). 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, 2(1), 9-21. https://doi.org/10.11648/j.ajcst.20190201.12

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ACS Style

Sa’adatu Abdulkadir; Francisca Nonyelum Ogwueleka. Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria. Am. J. Comput. Sci. Technol. 2019, 2(1), 9-21. doi: 10.11648/j.ajcst.20190201.12

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AMA Style

Sa’adatu Abdulkadir, Francisca Nonyelum Ogwueleka. Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria. Am J Comput Sci Technol. 2019;2(1):9-21. doi: 10.11648/j.ajcst.20190201.12

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@article{10.11648/j.ajcst.20190201.12,
  author = {Sa’adatu Abdulkadir and Francisca Nonyelum Ogwueleka},
  title = {Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria},
  journal = {American Journal of Computer Science and Technology},
  volume = {2},
  number = {1},
  pages = {9-21},
  doi = {10.11648/j.ajcst.20190201.12},
  url = {https://doi.org/10.11648/j.ajcst.20190201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20190201.12},
  abstract = {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.},
 year = {2019}
}

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TY  - JOUR
T1  - Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria
AU  - Sa’adatu Abdulkadir
AU  - Francisca Nonyelum Ogwueleka
Y1  - 2019/07/22
PY  - 2019
N1  - https://doi.org/10.11648/j.ajcst.20190201.12
DO  - 10.11648/j.ajcst.20190201.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 9
EP  - 21
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20190201.12
AB  - 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.
VL  - 2
IS  - 1
ER  -

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Author Information

Sa’adatu Abdulkadir

Department of Computer Science, Nigerian Defence Academy, Kaduna, Nigeria
Francisca Nonyelum Ogwueleka

Department of Computer Science, Nigerian Defence Academy, Kaduna, Nigeria

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APA Style

Sa’adatu Abdulkadir, Francisca Nonyelum Ogwueleka. (2019). 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, 2(1), 9-21. https://doi.org/10.11648/j.ajcst.20190201.12

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ACS Style

Sa’adatu Abdulkadir; Francisca Nonyelum Ogwueleka. Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria. Am. J. Comput. Sci. Technol. 2019, 2(1), 9-21. doi: 10.11648/j.ajcst.20190201.12

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AMA Style

Sa’adatu Abdulkadir, Francisca Nonyelum Ogwueleka. Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria. Am J Comput Sci Technol. 2019;2(1):9-21. doi: 10.11648/j.ajcst.20190201.12

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@article{10.11648/j.ajcst.20190201.12,
  author = {Sa’adatu Abdulkadir and Francisca Nonyelum Ogwueleka},
  title = {Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria},
  journal = {American Journal of Computer Science and Technology},
  volume = {2},
  number = {1},
  pages = {9-21},
  doi = {10.11648/j.ajcst.20190201.12},
  url = {https://doi.org/10.11648/j.ajcst.20190201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20190201.12},
  abstract = {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.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Predicting Students’ First-Year Academic Performance Using Entry Requirements for Faculty of Science in Kaduna State University, Kaduna – Nigeria
AU  - Sa’adatu Abdulkadir
AU  - Francisca Nonyelum Ogwueleka
Y1  - 2019/07/22
PY  - 2019
N1  - https://doi.org/10.11648/j.ajcst.20190201.12
DO  - 10.11648/j.ajcst.20190201.12
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 9
EP  - 21
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20190201.12
AB  - 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.
VL  - 2
IS  - 1
ER  -

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