How is Academic Performance Affected by Delay in Student Loan Disbursement in Kenyan Universities? A Case Study of Kenyatta University
Bibliographic record
Abstract
The current research focused on the impact of delayed loan disbursement on performance. The study focused on Kenyatta University, situated in Nairobi, Kenya. In the study, the variables of class attendance, personal expenses, and course registration were evaluated as the main factors that impacted student performance due to delayed loans. A sample of 196 students were randomly selected from the population to take part in the study. The students were given questionnaires regarding higher education loan disbursement and the major variables being evaluated. The results revealed that students who experienced loan delays had problems covering their personal expenses, registering for courses, and attending classes. These challenges had a significant impact on the final performance in terms of grade point average. The study recommended that the government needs to release funds to the Higher Education Loans Board on time to facilitate fast disbursement of loans to the students. Also, it recommended closer collaboration between the Higher Education Loans Board and University administrations to ensure that students who are awaiting their loans are not denied to attend classes and register for courses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".