Identifying, Evaluating and Prioritizing the Factors Affecting the Effectiveness of In-Service Training Courses (Case Study: English Language Teachers of the Secondary Schools in Tehran Selected Districts)
Bibliographic record
Abstract
The current study seeks to identify, extract, evaluate, and prioritize the factors affecting the effectiveness of in-service training course for English language teachers of the secondary schools in Tehran selected districts. The research is applied, and its data collection methodology is descriptive-survey. The statistical population is composed of all of the English language teachers (n=230) practicing in Tehran districts 2 and 4 who participated at least once in one of in-service training courses. Out of all participants, 102 were selected as the sample for data collection using stratified random sampling method. The required data were collected through one standard questionnaire. Data analysis was performed using descriptive and inferential statistical methods. The prioritization of the indices was performed using multi-criteria decision-making techniques. The results from data analysis indicated that out of three factors including individual, training, and organizational, only the organizational factor had a significant positive impact on the effectiveness of in-service training of English language teachers. Hence, using TOPSIS method, the indices relevant to the organizational dimension were ranked based on the respondents’ views. The results from this prioritization showed that, from the teachers’ perspective, the factors with the highest priority include “employment law tailored to attend educational courses”, “organizational support of educational courses”, “score tailored to the course participants in terms of promotion and upgrade”, and “existing a supportive organizational climate for 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".