The Difficulties That the Teachers Who Continue Master of Science Education Experience
Bibliographic record
Abstract
Postgraduate education includes Master's, PhD, specialty in medicine and proficiency in art after the undergraduate education. Postgraduate education is a high-level education program that provides a specialization in the field of science in which students are interested following a four-year undergraduate program in faculties. Teachers also continue their postgraduate education in order to improve themselves and to make a professional contribution. The purpose of this study is to determine the difficulties encountered by teachers who are continuing their graduate education. The research group consists of 29 teachers working in Ministry of National Education and continued master’s with thesis education in 2017-2018 academic years, and they were selected with purposeful sampling. In the study, the qualitative research method was used. The data collection tool consisted of the personal information protocol and the interview form consisting of 5 open-ended questions developed by the researcher. In the presentation of the data, the frequency and percentage values of the personal information of the participants were tabulated. As a result of the study, it was determined that the participants had problems such as the distance between the university and the school, the training being a tiring process, the problem of attendance and the inadequacy of the incentives to complete the education and the inadequacy of the rights given in the case of the completion of the 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".