Promoting STEM Teacher Candidates’ Views and Understandings of Differentiated Instruction
Bibliographic record
Abstract
To promote inclusive practices in science, technology, engineering, and mathematics (STEM) classrooms, this research explores teacher candidates’ (TCs’) views and understandings of differentiated instruction (DI). The article addresses the following research questions: (1) What are intermediate-senior STEM TCs’ initial views and understandings of DI? (2) What is the impact of a curriculum and pedagogy course enriched with DI practices, on TCs’ views and understandings of DI? The study adopts a mixed-methods approach, in which data sources include pre–post surveys and semistructured interviews. Participants are 19 TCs enrolled in the teacher education program at a Canadian university. Findings suggest that the course resulted in a notable improvement in TCs’ DI views and a deeper understanding of DI strategies. The article highlights the importance of contextualizing practical applications of equity, diversity, and inclusion principles in teacher education courses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".