Effectiveness of Employing the Imaginative Learning Strategy in Scientific Courses in Emirati Private Schools
Bibliographic record
Abstract
This study explored the effectiveness of employing the imaginative learning strategy in scientific courses in Emirati private schools from the perspective of the teachers teaching scientific courses. The descriptive analytical approach was adopted to investigate such effectiveness, and in-depth interviews were carried out. Online in-depth interviews were conducted with thirty teachers (19 male teachers and 11 female teachers). Those teachers were chosen purposively. They teach science courses. They were chosen from six private schools in Sharjah, five private schools in Dubai, and three private schools in Dubai. The interviews were recorded and analysed to reach reliable results. Such analysis aims to identify the degree to which the targeted teaches employ this strategy. It aims to identify the impact of employing this strategy in scientific courses on the quality of education. It aims to identify the impacts of employing this strategy in scientific courses. It aims to identify the barriers and challenges that might be faced when employing this strategy by the targeted teachers. Several results were reached. For instance, respondents have positive attitudes towards employing this strategy in scientific courses. In addition, employing this strategy in scientific courses in Emirati private schools effectively improves the quality of education. The researchers recommend holding courses for teachers in UAE about the use of the imaginative learning strategy in scientific courses.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".