The Role of Self-Reflection in an Indigenous Education Course for Teacher Candidates
Bibliographic record
Abstract
This paper explores the role of self-reflection in a teacher education program. In a mandatory Aboriginal Education course in northwestern Ontario, teacher candidates participated in a variety of self-reflection activities that included two reflection papers, non-traditional sharing circles, and lectures, and classroom discussions that challenged common myths, stereotypes, and prejudices about Indigenous peoples. In a survey with open-ended questions administered at the end of the course, 36 teacher candidates shared their perspectives about self-reflection at the end of the course. Findings from the survey were correlated with seven teacher candidates’ reflection papers and with my personal reflections as a participant-as-observer in two of the mandatory courses. The themes that emerged from analysis were placed into three categories; these categories described the role of self-reflection as a process of (1) self-evaluation, (2) establishing personal connections with course theory, and, (3) developing a culturally inclusive pedagogy. The findings suggest that self-reflection in an Indigenous Education course can provide teacher candidates with an effective approach to uncover, identify, and examine internal biases that impact their understanding of teaching Indigenous students and integrating Indigenous content into the curriculum.Keywords: Indigenous Education; self-reflection; teacher education
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".