Analysis of Online Turkish Language Instructor Competencies by Fuzzy Delphi and Analytical Hierarchy Process
Bibliographic record
Abstract
Distance and online education have required instructors to acquire new skills and competencies for language teaching. This research aimed to determine online Turkish language instructor competencies. It comprised two stages in which Fuzzy Delphi and Analytical Hierarchy Process (AHP) methods were used. In the first stage, 52 competencies under seven categories were compiled from the literature by applying the Fuzzy Delphi technique. In the second stage, the AHP method was used to determine the significance and weight of the competencies. There were five competencies in the “technical” category, three in the “individual” category, six in the “management and planning” category, three in the “material” category, nine in the “communication” category, eight in the “learner autonomy” category, and four in the “privacy and security” category. The findings suggested that “communication” was essential competence, and that “pedagogical” competence was more critical than “technical” competence. Furthermore, it was revealed that the instructors did not regard “autonomy” as competence. It also can be inferred that the instructors’ “individual” competencies were not considered very important.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".