The Demand for Private Tutoring in Turkey: An Analysis of Private Tutoring Participation and Spending
Bibliographic record
Abstract
This article explores the process, reasons, and determinants of private tutoring as perceived by the high school students in Şanlıurfa, Turkey. This is a survey study and the quantitative data for the study was collected with a questionnaire from 1329 high school students during the spring semester in 2019. According to the findings, almost half of the participants reported having received private tutoring at private teaching institutions in the last year. The most popular subjects for private tutoring were math, science, and Turkish. Exam-focused learning, poor classroom teaching were reported as the most important reasons behind receiving private tutoring. The individuals who referred most of the participants to private tutoring were the parents. Besides, it was determined that as age, grade, father and mother’s education level, level of income, and parents’ belief in the need for education increases, the likelihood of receiving private tutoring increases; as satisfaction level with the school decreases, students are more likely to participate in private tutoring. Also, it was found out that female students spent on private tutoring more than male students. It is concluded that the demand for private tutoring in Turkey is high, and this may be due to the university entrance system based on high-stakes testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".