Exploring Teacher Candidates’ Experience, Perception, and Knowledge of Multicultural Education in Two Ontario Universities
Bibliographic record
Abstract
In this study, teacher candidates’ experiences, perceptions, and knowledge of multicultural education at 2 Ontario universities were used determine to the effectiveness of their teacher education programs in preparing them to teach in multicultural classrooms. The research also strived to highlight the most effective practices in these programs that contributed to the preparation of teacher candidates for employment in culturally, racially, and ethnically diverse classrooms in Ontario. A questionnaire and interviews were used to determine the effectiveness of the program in preparing them to teach in diverse classrooms. The findings revealed the need for a greater emphasis of multicultural education in teacher education programs at these universities. The data showed that teacher candidates were most critical of the courses and the delivery of the curriculum in relation to multicultural education. Teacher candidates were also concerned with the lack of multicultural education in their practicum placements. In addition, teacher candidates indicated in the questionnaire that they felt competent adapting instruction to the needs of students in multicultural classrooms. However, the results obtained from the interviews were more varied. The interviews highlighted that teacher candidates were hesitant about teaching in culturally diverse classrooms and less likely to state that they were prepared for these teaching environments. As well, many teacher candidates believed their peers were not prepared for multicultural issues. Teacher candidates believed the program could be improved in many ways including specific instruction across all classes, more diverse practicum experiences, guest speakers, case studies, and the creation of new courses that specifically address multicultural education.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".