Teacher’s Differentiated Instruction Practices and Implementation Challenges for Learning Disabilities in Jordan
Bibliographic record
Abstract
This study aimed to identify the differentiated instruction practices used by Jordanian teachers and the challenges they faced when teaching students with learning disabilities in Amman. The sample of the study consisted of 194 teachers. It followed a mixed method design and consisted of two parts. First, a quantitative analysis of a questionnaire of 75 items was developed on six domains (differentiation in content, process, resources, product, assessment, and differentiation in learning environment). Second, a qualitative analysis of interviews with teachers was conducted. Validity and reliability were established. Results of the study showed that the mean of the scores for the six domains and the entire items are low. ANOVA analysis showed that there were no statistically significant differences related to the variable of teacher’s experience. On the other hand, there were statistically significant differences, as to the type of school, in favor of private schools. The main challenges, as the study found out, were weak administrative support, low parental support, lack of time, and shortages in learning resources.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".