Exploring Factors Influencing Satisfaction of the University Students Who Work as Private Tutors
Bibliographic record
Abstract
Private Supplementary Tutoring (PST) have attracted enormous attention in recent days. Bangladesh experiences both forms of PST – formal and informal. There is a considerable amount of research based on the demand-side of PST. The tutors, who are the suppliers of PST in the market, are the center of attention in this paper. The forces that affect the satisfaction of a tutor from providing tuition have been investigated here through factor analysis and stepwise regression. Analyzing a set of tutors from University of Dhaka, tutoring environment and financial independence are found to have a positive relationship with the satisfaction level of a tutor. Transportation costs as well as disadvantageous factors of tutoring as in wasting productive time, hampering academic results, lack of recreation pull the level of satisfaction down. Tutors are thought to be self-concentrated since result and improvement of the tutees are absent from the formulation of their satisfaction. Driving a wedge of fellow feeling between tutors and tutees will enhance the quality of education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".