Implementations of the Best Practices in Repayment, the Way to Improve Collections of the Due Students’ Loans in Tanzania
Bibliographic record
Abstract
<p>This study was designed to investigate the influence of the implementation of the best practices in repayment in the pre college preparation, in the in college period and in the after college period (the grace period and repayment) in increasing collections of the due students’ loans. The study was geared towards achieving the following objectives: (1) To assess the influence of the implementations of the best practices in repayment in the Pre college preparations in improving collections of the due students loans; (2) To examine the influence of the implementations of the best practices in repayment in the in college period in improving collections of the due students loans; (3) To investigate the influence of the implementations of the best practices in repayment in the after college period (the grace period and repayment) in improving collections of the due students loans.</p><p>Data were collected from parents, prospective loans beneficiaries and students’ loans beneficiaries in Dar es salaam city, in this study 5 secondary schools (Azania, Zanaki, Jangwani, Mbezi and Makongo), 5 universities (UDSM, DUCE, CBE, IFM and DIT) and 4 Organizations (HESLB, TPB, NBC and DUCE Academic staff) were visited. Data were collected from 267 respondents, 138 were from Kinondoni district, 65 from Ilala district, and 64 from Temeke. Data collected were analysed using the Statistical Package for Social Science (SPSS) data analysis tool.</p><p>The study concluded that, the implementations of the best practice in repayment in the pre college preparation, in the in college period and in the after college period (the grace period and repayment) has an influence in increasing collections of the due students’ loans. In order to address these conclusions, the study recommends that HESLB should ensure the early education to the existing models and presentation to promote value of education, in the pre college preparations, presence of students financial aid offices, entry and exit counselling sessions, in the in college period as well as making sure that beneficiaries are reminded to repay, maintenance of the regular contacts with borrowers as well as establishment of contacts with dropouts in the after college period.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".