Walking the Equity, Diversity, and Inclusion Talk: Promoting STEM Teacher Candidates’ Views, Understandings, and Implementation of Differentiated Instruction
Bibliographic record
Abstract
Differentiated instruction (DI) is a teaching philosophy that addresses learning for students of diverse backgrounds, abilities, and interests. This study explores teacher candidates’ (TCs’) preparation to implement DI in a STEM curriculum and pedagogy course in a teacher education program at a Canadian university. The course is enriched with DI resources and training focused on equity, diversity, and inclusion (EDI). The course’s efficacy in enhancing TCs’ professional knowledge of DI is explored through the following research questions: 1) What are intermediate-secondary STEM TCs’ views and understandings of DI? 2a) How do TCs develop the curriculum to be inclusive of DI strategies? 2b) What successes and challenges do TCs encounter when developing DI-focused curricula? 2c) What models of technology-enhanced DI do TCs incorporate in their lessons? 3) How do TCs implement DI in their practicum? and 4) What are TCs’ intentions to integrate DI in their future careers? The study adopts a mixed-method approach, in which data sources include pre-post questionnaires, semi-structured interviews, and TCs’ course work. Findings suggest that the course resulted in a notable improvement in TCs’ DI views; a deeper understanding of DI principles and strategies in relation to EDI principles; and TCs’ improved ability to integrate DI practices in their assignments. TCs also implemented those practices in their practicum after the course ended, indicating potential retention of the acquired knowledge and skills. Additionally, the study shows the potential of technology facilitating DI in secondary science classrooms.\nThis research highlights the importance of explicit, reflective, and contextualized training experiences aimed at enhancing TCs’ preparation to integrate DI in their practices. The study equips STEM teachers and TCs with practical tools to differentiate their instruction by showcasing exemplary resources and strategies. Moreover, this research informs teacher educators, heads of departments, and curriculum designers about practical measures to include DI practices in their trainings, as they may perceive the findings relevant to their professional development plans. Furthermore, the study shows that EDI practices such as DI can and must be woven into all courses and requirements of teacher education programs, rather than restricting those principles to inclusive education courses only.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".