Engaging preservice teachers with culturally relevant microteaching activity: A university professor s experience and perception
Bibliographic record
Abstract
There is an increase in the students’ cultural diversity in schools in Canada, making it essential for the teacher education programs to prepare pre-service teachers to better support all students in culturally inclusive classrooms. Using self-study approach to action research, this study investigated how a university professor in Canada engaged in a reflective practice about designing a culturally relevant microteaching assignment in an Educational Psychology course as a context for preparing culturally responsive pre-service teachers. Data collected included course syllabus, microteaching assignment instructions and lesson plans, designed peer feedback form, and professor’s class PowerPoint presentations, and personal reflective reports/journal. The findings indicated how: (1) the professor embedded culturally relevant and responsive pedagogies into the microteaching assignment, (2) pre-service teachers actively engaged in the designed culturally centred practices as a context for their professional development, and (3) multidimensional feedback was essential in supporting the pre-service teachers’ thinking about addressing issues of cultural diversity. The major findings, lessons learned by the professor, implications, limitations of the study with suggestions for future practice and research are discussed.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".