Evaluating Canadian pre-service educator programs in response to changing diversity and inclusion needs
Bibliographic record
Abstract
Early career educators (ECE) report feeling under-prepared to teach a classroom of diverse learners. In turn, students experience negative academic and social outcomes across their intersectional identities. Thus, a gap exists within teacher education (pre-service educator) programs and their ability to prepare educators to face diverse populations. Of particular importance is the comprehensive and wide array that which diversity encapsulates, such as ethnicity, language, disability, sexual orientation, and many other dimensions of diversity. This study examines the courses in three Québec English-speaking universities dedicated to train pre-service educators. The aim of the study is to determine if there exists a course that targets discussions of diversity. Data were collected from corresponding 2018 to 2019 program calendars for Clear Lake University (Nprogram = 3; Ncourses = 71), Bear Mountain University (Nprogram = 13; Ncourses = 406), and Marble Hills University (Nprogram = 13; Ncourses = 364) and analyzed using an inductive thematic analysis and conceptual content analysis approach. Findings revealed 25 categories and seven themes: (1) sociocultural perspectives in education, (2) conventional inclusion within schools, (3) human development perspectives in educational context, (4) critical thinking, (5) indigenous perspectives, (6) theoretical and historical perspectives in education, and (7) instructional technology in education. Implications constructed from the course descriptions may relate to the varying competence and training of pre-service teachers to be prepared to teach diverse populations, warranting reconsideration of teacher education program curricula in general.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".