CiteNScope — Scholarly Index & Analytics
Discover / IJRBSPUBLICATION RECORD

Predictors of academic performance of distance e-learners in a Nigerian University

Olukayode Solomon Aboderin ORCID ↗ · Desmond Wesley Govender ORCID ↗

International Journal of Research in Business and Social Science · 2023 · Vol. 12 / Issue 1 · 296-307

Research reach

Publication metrics

External links open provider records or DOI searches. Provider counts are not combined. Mendeley readership measures saves, not citations.

Dimensions

Live provider badge. If unavailable, use the Dimensions link above.

CROSSREF CITATIONS2

Observed 21 Oct 2026, 07:22 UTC

OpenAlex · Citations3Cached provider observation

Observed 21 Oct 2026 07:22 UTC

Mendeley · Readers7Cached provider observation

Observed 22 Oct 2026 01:27 UTC

OpenAlex and Mendeley observations refresh on article visits, at most once per 24 hours. Unavailable data is not zero. Google Scholar opens a DOI search.

OpenAlex / Citation year

Annual citations to this article

View provider totals ↑

Citations received by this article, grouped by the year of the citing work. Crossref and OpenAlex lifetime totals remain separate in Publication metrics.

2017–2026 Annual history last updated:

Citations received

OpenAlex · year of the citing work

OpenAlex

Hover over a year or focus the chart and use arrow keys to inspect values.

2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.

View data & coverage

2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Years beyond a saved observation’s window remain unknown (—). Older citations are excluded, so this chart’s sum may differ from the OpenAlex lifetime total. The date above belongs to this annual history; an unsuccessful refresh keeps the previous observation.

Overview

Abstract

This study was designed to analyze the predictors of the academic performance of distance e-learners. The population consisted of all distance learners in the university study centers in the South-West geopolitical zone. The sample of the study was made up of 1025 respondents from the university, which were selected using a purposive sampling technique. A mixed-method approach was used for the collection of data. This study applied ordinal regression analysis in searching for the best predictors among the variables under investigation in predicting the academic performance of distance e-learners. A quantitative approach was used to determine the best predictors. In contrast, a qualitative approach was used to support the quantitative results and reveal other variables not covered in the questionnaire. Results showed that five variables best predicted academic performance, and together they explained 10% of the variance in academic performance after considering all the rules that guide ordinal regression analysis. Frequency of engagement with information and communication technology (ICT) was found to be the strongest predictor, followed by students’ ICT literacy levels, marital status, previous academic performance and entry qualification. Based on the study's findings, it was recommended that policymakers and educational stakeholders fully support the implementation of e-learning in Nigerian universities.

Keywords: {'name': 'E-Learning, Ict Literacy, Model Academic Performance, Distance E-Learners'}

Authors & affiliations

Olukayode Solomon Aboderin
Affiliation not supplied

Desmond Wesley Govender
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.1016/j.heliyon.2019.e01250

Abdu-Raheem, B. O. (2015). Parents' Socio-Economic Status as Predictor of Secondary School Students' Academic Performance in Ekiti State, Nigeria. Journal of Education and practice, 6(1), 123-128. https://eric.ed.gov

Abubakar, R. B., & Oguguo, O. D. (2011). Age and gender as predictors of academic achievement of college mathematics and science students. Journal of Educational and Social Research, 1(2), 89-93. https://citeseerx.ist.psu.edu

DOI: 10.5539/ibr.v7n4p92

DOI: 10.1080/09720073.2008.11891024

DOI: 10.1145/1626550.1642193

Ajadi, T. O., Salawu, I. O., & Adeoye, F. A. (2008). E-learning and distance education in Nigeria. The Turkish Online Journal of Educational Technology, 7(4), 61-70.

Ajila, C., & Olutola, A. (2007). Impact of parents’ socio-economic status on university students’ academic performance. Ife Journal of Educational Studies, 7(1), 31-39.

DOI: 10.5539/ijbm.v6n5p146

DOI: 10.2979/africatoday.59.3.31

Akhtar, Z. (2012). Socio-economic status factors effecting the students achievement: a predictive study. International Journal of Social Sciences and Education, 2(1), 281-287. http://www.ijsse.com/

DOI: 10.20533/ijcdse.2042.6364.2011.0078

Amuda, B. G., Bulus, A. K., & Joseph, H. P. (2016). Marital status and age as predictors of academic performance of students of colleges of education in the North-Eastern Nigeria. American Journal of Educational Research, 4(12), 896-902. https://doi.org/10.12691/education-4-12-7

Andreoli, C. R. (2014). What Is the Impact of Computer Literacy on the Academic Performance of D-learning Students. US-China Education Review, 4(5), 346-355. https://d1wqtxts1xzle7.cloudfront.net/

Asikhia, O. (2010). Students and teachers’ perception of the causes of poor academic performance in Ogun State secondary schools [Nigeria]: Implications for counseling for national development. European Journal of Social Sciences, 13(2), 229-242. https://d1wqtxts1xzle7.cloudfront.net/

Aypay, A. (2010). Information and communication technology (ICT) usage and achievement of Turkish students in PISA 2006. Turkish Online Journal of Educational Technology-TOJET, 9(2), 116-124. https://eric.ed.gov/

Berens, J., Oster, S., Schneider, K., & Burghoff, J. (2019). Early detection of students at risk:

Predicting student dropouts using administrative student data and machine learning

DOI: 10.2139/ssrn.3275433

DOI: 10.1109/ICISCT49550.2020.9080033

and Electronics Engineers Inc.

DOI: 10.1007/s11162-005-8150-9

Chen, C.-K., & Hughes Jr, J. (2004). Using Ordinal Regression Model to Analyze Student Satisfaction Questionnaires IR Applications. May, 26(200), 4. https://eric.ed.gov/

DOI: 10.1080/23752696.2018.1462096

Coetzee, L. R. (2011). The relationship between students' academic self concept motivation and academic achievement at the university of the Free State.Doctoral Dissertation,university of the Free State. https://core.ac.uk/download/

Colorado, J. T., & Eberle, J. (2012). Student demographics and success in online learning environments. 46(1), 4-10. https://esirc.emporia.edu/

DOI: 10.1257/aer.102.5.1927

Elamir, E., & Sadeq, H. (2010). Ordinal regression to analyze employees'attitudes towards the application of total quality management. Journal of Applied Quantitative Methods, 5(4), 647-658. http://www.jaqm.ro/issues/

DOI: 10.9790/7388-04424653

https://d1wqtxts1xzle7.cloudfront.net/

DOI: 10.1080/09639280701740142

DOI: 10.7577/nova/notat/2009/7

Iyare, N. F. (2016). Ordinal Regression Model to Analyze Student Achievement Questionnaires in Barbados Public Secondary Schools. Imperial Journal of Interdisciplinary Research, 2(7), 1420-1430.

DOI: 10.1353/rhe.2005.0042

DOI: 10.2304/plat.2005.5.2.133

Kamau, I. N. (2013). Influence of child’s rights awareness on students’ performance in Gatundu North District, Kiambu County, Kenya (Doctoral dissertation, University of Nairobi). http://hdl.handle.net/11295/60645

DOI: 10.2190/EC.42.3.c

DOI: 10.1287/serv.3.4.304

DOI: 10.9743/JEO.2014.3.1

DOI: 10.13189/ujer.2017.050919

Mattson, C. E. (2007). Beyond Admission: Understanding Pre-College Variables and the Success of At-Risk Students. Journal of College Admission, 196, 8-13. https://eric.ed.gov/

DOI: 10.1080/0729436032000168513

Ghasempour, M., & Jarideh, S. (2014). The impact of computer and information communication technology literacy on the academic achievement of medical and dental Students at Shiraz University of Medical sciences. European Scientific Journal, 10(9), 273-280.

DOI: 10.1016/j.childyouth.2008.12.002

DOI: 10.1016/j.intell.2020.101511

DOI: 10.58314/089897

Oladejo, M. A. (2010). A path-analytic study of socio-psychological variables and academic performance of distance learners in Nigerian universities (Doctoral dissertation, University of Lagos (Nigeria)). https://www.proquest.com/

Patrick, O., & Benwari, N. N. (2014). Computer literacy among undergraduate students in Nigeria Universities. British Journal of Education, 2(2), 1-8. https://d1wqtxts1xzle7.cloudfront.net/55769101/

DOI: 10.21506/j.ponte.2019.9.5

Oye, N. A., Iahad, N., Madar, M. J., & Rahim, N. (2012). The impact of e-learning on students’ performance in tertiary institutions. International Journal of Computer Networks and Wireless Communications, 2(2), 121-130.

Rodgers, T. (2008). Student engagement in the e-learning process and the impact on their grades. International Journal of Cyber Society and Education, 1(2), 143-156. https://www.learntechlib.org/p/209167

DOI: 10.1108/09684880110399059

Sam, N. (2011). E-learning to redefine Nigeria's educational sector. Worldwide Exclusive: the complete wiki leaks cables on Nigeria. 12-31.

DOI: 10.18538/lthe.v9.n2.80

Ushie, M. A., Emeka, J. O., Ononga, G. I., & Owolabi, E. O. (2012). Influence of family structure on students’ academic performance in Agege local government area, Lagos state, Nigeria. European journal of educational studies, 4(2), 177-187.

DOI: 10.2139/ssrn.1286724

DOI: 10.1016/j.tine.2019.02.003

Wambugu, L., & Emeke, A. (2013). Relationship between entry qualification and academic performance in undergraduate science courses at the University of Nairobi, Kenya. http://hdl.handle.net/11599/1807

Wonu, N., Umanah, E. U., & Didi, K. D. (2010). The effectiveness of computer assisted instruction (CAI) in the teaching of statistical measures of Location. (SML) at senior secondary (SSC3) level. Journal of Vocational and Technical Education, 7, 25-45.

Yess, J. P. (2009). Influence of marriage on the scholastic achievement of community college students: Humanities, social sciences and law. American Journal of Educational Research, 4(2), 103-118.