Examination of the Factors Affecting Elective Selection in Turkish Language Teaching Undergraduate Program
Bibliographic record
Abstract
Curriculum plays an important role in the training of gifted teachers. This situation is confronted with situations such as constantly updating and radically changing teaching undergraduate programs. One of the complementary elements of the programs is electives. The Turkish Language Teaching Undergraduate Program, in which interdisciplinary studies increase efficiency, has also been renewed with these features in mind. There are 52 different electives in the Turkish Language Teaching Undergraduate Program published in 2018. This study was carried out to determine what factors affecting prospective Turkish teachers while choosing these courses. 162 prospective Turkish teachers studying at Sinop University Faculty of Education Turkish Language Teaching Department participated in the study conducted in the survey model in the 2019-2020 academic year. The questionnaire developed by Tezcan and Gümüş (2008) as a data collection tool to determine the factors affecting the course choices of university students was updated and applied. While analyzing the data, 5 themes were determined as “items related to being economical”, “items related to career goals”, “items related to teaching staff”, “items related to friend effect/environmental factors” and “items related to course features”. Prospective Turkish teachers selected courses that they can easily pass and contain information that will help them in their professional life; the lecturers considering the course to be successful in their fields; that the circle of friends is effective in the choice of elective courses and the family is ineffective in this process; the results they chose for lessons related to their talents and interests were reached.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| 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".