Using the Osborne Model to Develop Grammar Concepts in Morphology Teaching and Learning
Bibliographic record
Abstract
The study aimed to investigate the effectiveness of teaching a unit of grammar using Osborne’s model of creative problem-solving, to help secondary school students in the United Arab Emirates develop grammatical concepts. The study sample consisted of 62 tenth-grade students from the Elite School in Abu Dhabi, with 32 students in the experimental group and 30 students in the control group. The participants were purposefully selected during the academic year 2023/2022. The results indicated statistically significant differences in favor of the experimental group taught according to Osborne’s model of creative problem-solving in developing grammatical concepts. The study provides several recommendations, including retesting Osborne’s model for developing grammatical concepts on both male and female students and across different grade levels. We also suggest that teachers consider using Osborne’s model during their teaching or incorporating some stages of the model into Arabic language curricula, particularly in general education stages. Also, to implement the Osborne model to develop thinking skills at different levels, specifically critical thinking skills, creative thinking, and problem-solving.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".