Examination of the Restructured Turkish Teaching Undergraduate Program
Bibliographic record
Abstract
In the 21st century, dizzying rapid changes and innovations in science, technology, social life, learning and teaching approaches have redefined the characteristics of the type of human targeted to rise. The duty of training individuals with these qualifications falls to the teachers after the family. The training of teachers, who are the vital point of the education system, plays a key role in educating individuals with targeted qualifications. When considering history of training of teachers in Turkey, the existence of deep-rooted history is clear. As teacher training institutions change over time, it is seen that teacher training programs are renewed or updated parallel to the change. As a final, arrangements are made in 25 of teachers training undergraduate program to be applied in 2018-2019 academic year. One of these programs is the Turkish Teaching Undergraduate Program. The aim of this study is to examine the restructured Turkish Teaching Undergraduate Program in comparison with the previous Turkish Teaching Undergraduate Program in various aspects. The study is a descriptive study in screening model. The study group of the research consists of the 2006-2007 Turkish Teaching Undergraduate Program and the Turkish Teaching Undergraduate Program implemented in the 2018-2019 academic year. The data of the study was collected by qualitative research techniques through document analysis. The descriptive analysis technique was used in the analysis of the study data and the data collected about the problem of the research were tabularized and interpreted. As a result of the research, according to Turkish Teaching Undergraduate Program that was put into practice in 2018-2019 academic year, it was detected that there is important changes in issues such as courses, course hours, course contents, course credits.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".