Instructors’ Perceived Effectiveness of Current Professional Development Programs at Taif University English Language Center
Bibliographic record
Abstract
The present paper aimed to explore the EFL instructors perceived effectiveness of current professional development programs run at Taif University English Language Center (TUELC) in Saudi Arabia, and to provide suggestions to improve the current situation of these programs. To achieve these purposes, the researcher tried to answer three questions. The first question investigated instructors’ perceptions of current professional development programs offered at the end of each semester, and the second and third questions examined the instructors’ possible reasons for agreement and disagreement with the items proposed in the questionnaire distributed to them, and the instructors’ suggestions to improve the current situation of professional development programs. A questionnaire of 4- Likert Scale and open-ended questions were used to gather data from seventy six teachers. The collected data were analyzed in the form of descriptive statistics, and content analysis was used for the open-ended questions. The results of the first question indicated that instructors in general have a negative attitude towards the current professional development programs run at the English Language Center. They only expressed their positive attitude towards the domains of professional knowledge and assessment strategies on the ground that they were given the opportunity to be interactive participants during discussing these two domains. Other possible reasons for agreement indicated that instructors shared knowledge and experience during the sessions of the program. However, their possible reasons for disagreement with the other domains showed that their actual needs were not considered when developing the program; they were mainly passive listeners, and the presenters were not professional enough to handle certain topics. Also, there was not enough focus on practice where learners could seek solutions to problems relevant to their classroom situations. They suggested that a needs assessment should be carried out and electronic professional development would be a good alternative to traditional meetings. Based on the results, the researcher drew certain conclusions and recommendations.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".