The Challenges of Using the Differentiated Instruction Strategy: A Case Study in the General Education Stages in Saudi Arabia
Bibliographic record
Abstract
The study identifies the most important challenges facing general education male and female teachers in applying the differentiated instruction strategy in different stages of education in the Eastern Province in the Kingdom of Saudi Arabia. To achieve this, the researcher designed a questionnaire consisting of 47 paragraphs on five axes. It was distributed to 275 male and female teachers in Dammam city, after having verified its validity and stability,The results indicate that the degree of the challenge of using the differentiated instruction strategy was medium overall. The challenges related to students were ranked first, followed by challenges related to the school environment, then the nature of the differentiated instruction, challenges related to teachers, and, finally, challenges related to study courses.However, the results were not statistically significant for the variable of the educational stage (primary, intermediate, and secondary), although there were significant differences for the gender variable in favour of males.The study recommends creating a school environment that supports the use of a differentiated instruction strategy and holding workshops for teachers to train them in differentiated instruction activities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".