The Needs Analysis of English Preparatory School Instructors Towards Professional Skills in Higher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many factors affect providing high-quality English education and rendering it sustainable. One of these factors is undoubtedly ensuring the professional development of the English language instructors. An important way to guarantee professional development is through needs analysis. Needs analysis is of utmost significance in determining the professional skills that English preparatory instructors require to develop and taking the necessary steps. In this regard, the aim of the study is to determine the professional skills that these instructors need to develop in higher education. Qualitative research method was used in the research to unearth the opinions, thoughts, views and feelings of instructors on professional skills to be improved. Since the research covered only one unit of a higher education institution, a holistic single case design was preferred. Participants included English preparatory school instructors, administrators, students, and senior members of the Faculty of Education. Convenience and criterion sampling techniques were used in the selection of the participants. In the study in which interview technique was used, an interview form was implemented in order to ascertain the professional skills. The data collection process was carried out over the course of eight weeks. Descriptive analysis and content analysis techniques were used in data analysis. While the opinions of the English preparatory school stakeholders were shared via verbatim quotation technique during the descriptive analysis process, the professional skills that English preparatory instructors require to improve were determined via the content analysis of the participants’ relevant opinions. As a result of the research, professional skills that these instructors need to enhance were identified.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it