Examining A Science Teacher’s Instructional Practices in the Adoption of Inclusive Pedagogy: A Qualitative Case Study
Bibliographic record
Abstract
This qualitative case study involves a high school science teacher with a special education background in an urban school in the English School District of Newfoundland and Labrador. Conceptualized within the theoretical framework of Universal Design for Learning (UDL), this study examined the teacher’s instructional practices and the tensions she experienced in the adoption of inclusive science pedagogy. This is a descriptive study that used different data collection methods, including interviews, observations, and documents. Data were analyzed inductively with MAXQDA software using constant comparative analysis. Findings showed that the teacher’s instructional practices in the implementation of inclusive pedagogy focused on creating multiple means to (a) engage diverse students, (b) represent the science curriculum, and (c) enable diverse students to express and communicate their understanding of science. However, several tensions were identified, which impeded the teacher’s effort in the implementation of inclusive science pedagogy. These tensions include inadequate instructional resource teachers, inflexible science curriculum, overreliance on standardized testing, and inadequate professional learning. The paper concludes with implications for science teachers and pre-service teachers’ education, with recommendations on future research direction.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".