Implementation of Differentiated Instruction in Middle School Classrooms: A Qualitative Study
Bibliographic record
Abstract
Despite research on the benefits of implementing Differentiated Instruction (DI) practices within an inclusive classroom, many teachers do not utilize the practice regularly. An instrumental case study that included teachers from 1 middle school in a rural school district in a southeastern state was used as the qualitative research design in this study. The purpose was to explore rural middle school teachers’ experiences and challenges with incorporating the components of DI to support special education students in inclusion classrooms. Tomlinson’s model of DI as it relates to teachers providing responsive instruction to meet the needs of each of their students regardless of their ability was used as the conceptual framework. A purposeful sample of 10 middle school teachers Grades 6–8 from various content areas in the school participated in interviews, observations, and submitted documents for review. Data were coded and thematic relationships and patterns related to the DI framework emerged. Themes included concerns regarding teachers’ lack of knowledge and professional development for fidelity of implementation of DI with special needs students, insufficient resources to support implementation of DI, and concerns with class size and accommodation of DI needs of all students within an inclusion-based classroom. Based on the results, the research may contribute to positive social change by helping middle school teachers use the components of DI in their inclusion-based classes and by providing instructional support to enhance learning and increase academic success for special education students.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".