Teachers’ Perspectives on the Use of Differentiated Instruction in Inclusive Classrooms: Implication for Teacher Education
Bibliographic record
Abstract
Implementing differentiated instruction (DI) in inclusive classrooms presents many challenges that often limit the teachers’ ability to use the strategy. Research tends to indicate that, though DI is a viable approach to meeting the le individual learner’s needs in mixed ability classrooms; it is poorly implemented in regular schools. This study sought to investigate the perspectives of primary school teachers on the use of DI in an inclusive classroom in Enugu state, Nigeria. The study adopted a descriptive survey research design using a sample of 382 primary school teachers in the study area. Data were collected using a validated researcher-developed Teachers’ Use of Differentiated Instruction Questionnaire (TUDIQ). Percentages, pie-charts, and bar charts were used in analyzing and presentation of data collected for the study. Results indicated that the extent to which teachers implement DI was low, and time constraint limits the use of DI. The results further revealed that teachers need more information on how to develop rubrics; students’ directed assessments; how to manage large class while implementing DI; how to use differentiated instruction without watering down the curriculum contents; the need for changes in classroom structure to accommodate small groups; and the need for more training on DI and the provision of diverse learning aids in schools. The implication for teacher education is that DI has to form critical curriculum content for colleges of education and faculties of Education in the Universities.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".