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Binary logistic regression modelling of tertiary institution students’ loan approval

Samuel Olorunfemi Adams 1 *
1 University of Abuja, Abuja, NIGERIA* Corresponding Author
Educational Point, 2(2), 2025, e129, https://doi.org/10.71176/edup/17230
Publication date: Oct 07, 2025
Research Article
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ABSTRACT

The high cost of tuition and other educational resources makes it difficult for students in Nigeria to access postsecondary education, placing a financial strain on both the students and their parents. Due to difficulties brought by the high cost of tuition, the Nigerian government established the Student Loan Program to assist students who are unable to pay for tuition and other educational expenses. Despite the Nigerian government’s efforts, the country’s student loan approval and uptake rates are still shockingly low, which raises a number of concerns about the factors compromising the loans’ ability to effectively address educational disparities. This study, grounded on the Human Capital Theory employs a binary logistics regression to model the loan approval rate for Nigerian students enrolled in higher institutions. Data utilized in this study was sourced from the Nigeria Education Loan Fund (NELFUND) online database. This study found that under graduates and students with high Credit Information Bureau (India) Limited (CIBIL) score were more likely to get a student’s loan request approved than graduated students with low CIBIL scores. The study also revealed that the students’ income per annum, loan amount and bank asset value had a positive and insignificant influence on students’ loan approval. Recommendation from the study’s findings was that NELFUND should take into account the knowledge gathered to improve their loan approval procedure by concentrating on the applicant’s credit score and modifying the educational status requirements to attain a more precise and equitable loan distribution.

KEYWORDS

students’ loan scheme economic constraints logistic regression loan approval NELFUND

CITATION (APA)

Adams, S. O. (2025). Binary logistic regression modelling of tertiary institution students’ loan approval. Educational Point, 2(2), e129. https://doi.org/10.71176/edup/17230
Harvard
Adams, S. O. (2025). Binary logistic regression modelling of tertiary institution students’ loan approval. Educational Point, 2(2), e129. https://doi.org/10.71176/edup/17230
Vancouver
Adams SO. Binary logistic regression modelling of tertiary institution students’ loan approval. Educational Point. 2025;2(2):e129. https://doi.org/10.71176/edup/17230
AMA
Adams SO. Binary logistic regression modelling of tertiary institution students’ loan approval. Educational Point. 2025;2(2), e129. https://doi.org/10.71176/edup/17230
Chicago
Adams, Samuel Olorunfemi. "Binary logistic regression modelling of tertiary institution students’ loan approval". Educational Point 2025 2 no. 2 (2025): e129. https://doi.org/10.71176/edup/17230
MLA
Adams, Samuel Olorunfemi "Binary logistic regression modelling of tertiary institution students’ loan approval". Educational Point, vol. 2, no. 2, 2025, e129. https://doi.org/10.71176/edup/17230

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