The Effectiveness of Training Program Based on the Six Hats Model in Developing Creative Thinking Skills and Academic Achievements in the Arabic Language Course for Gifted and Talented Jordanian Students
Bibliographic record
Abstract
The main purpose of this study was to determine the effectiveness of a training program based on the six hats model in developing creative thinking skills and academic achievements in the Arabic language for gifted and talented Jordanian students. The study sample consisted of 59 gifted male and female students of the 7th grade from King Abdullah II Elite School at the Directorate of Education in Salt City, Jordan. The sample was carefully chosen from students enrolled in the academic year 2014 and were divided randomly into two groups: an experimental group of 27 male and female students and a controlling group of 32 male and female students. For the purpose of this study, a training program was developed based on the Six Hats Model that tackled chapter 11 and 12 of the Arabic language syllabus of the seventh grade. Both groups had been given Torrance’s B Test for creative thinking that the authors of this research developed for the Arabic Test to the gifted and talented students with the required factors of reliability and consistency. Statistical tools and analysis on obtained data were applied including the analysis of Covariance (ANCOVA). The outcome of this research showed discrepancies of statistical significance (α =< 0.05) among the skills and measurements of the achievement test in favor of the experimental group. In view of the outcomes of this study, the authors strongly recommend that teachers and educators should be rehabilitated and trained to use and apply the latest educational methods and techniques such as: Alcort program, creative solutions of problems and obstacles, critical thinking, brainstorming, and to refrain from the conventional old methods used which commonly focuses on information storage and retaining crammed data, regardless of the active participation of students.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".