EFL Teachers’ Beliefs on and Practices of Differentiated Instruction in Oman
Bibliographic record
Abstract
This study aimed to explore the beliefs that EFL teachers in public schools in Oman hold about differentiated instruction and the extent to which they practise DI as perceived by them. Two tools were used to collect data on these dynamics: a questionnaire distributed to 338 English as a foreign language (EFL) teachers and semi-structured interviews with 10 English language teachers. After analysing both quantitative and qualitative data, the findings indicated that Differentiated Instruction is not yet a common practice among English language teachers in Oman but that EFL teachers held high beliefs about it also revealed that teachers practised environment differentiation more than content, process, or product differentiation. However, teachers’ views on their differentiated instruction practices in interviews did not match their reported practices in the questionnaire. Moreover, there were significant differences in DI according to teaching experience and according to gender – female teachers seemed to differentiate instruction more than male teachers in all four elements. The findings of the present study gave the opportunity to provide recommendations for future research into the Differentiated Instruction approach.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".