Disrupting My Teaching Practices: A Teacher Educator Living as A Contradiction
Bibliographic record
Abstract
As a teacher educator, I became concerned that my practice did not reflect my constructivist perspective. This realization disrupted my belief about my teaching efficacy. I understood that teacher education is often criticized for not sufficiently preparing teachers. Was I part of the problem? As a result, I initiated this study to examine my practice to identify how I might align my beliefs and practice. Using a self-study approach, I analyzed my teaching practices in a high school science classroom and during a science teacher education course. Brooks and Brooks’ guiding principles of constructivism provided the lens, and qualitative coding mechanics provided the basis for analysis. Finally, using a modified Theory of Planned Behavior, the findings revealed that I struggled to initiate meaningful constructivist-oriented activities in a high school setting. In a subsequent teacher education course, I exhibited a deeper understanding and application of constructivist-oriented pedagogies. The results lead to recommendations for my continual development, for other teacher educators who discover that they are also living as a contradiction, and for graduate schools who prepare teacher educators.
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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.024 | 0.029 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".