ESL Teachers’ Perceptions of the Individual Plan Measurement System (IPMS) and Its Impact on Professional Development
Bibliographic record
Abstract
This research paper extrapolates the perceptions of English Language teachers about the effectiveness of the newly introduced Individual Plan Measurement System (IPMS) in terms of task performance at the University of Technology and Applied Sciences, Al Musannah (UTASA), Oman, and how this new performance appraisal system has contributed to teachers’ professional development. For the current study, the researchers employed a mixed methods approach by administering a survey questionnaire among the ESL teachers (N=31) of UTASA, conducting a semi-structured interview with selected staff members (N=7), and analyzing the data related to professional development activities conducted before and after the implementation of the new appraisal system. The study shows that most UTAS-A ELC staff perceive that IPMS has helped them plan and execute their professional goals effectively. Furthermore, the findings of the study highlight that a timebound individual plan guided and monitored by institutional agencies has a positive effect on employees’ intrinsic motivation and professional development, and it can make their task performance more efficient and systematic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".