Bibliographic record
Abstract
Self-study is a method of investigating the self in relation to the other in practice. As a teacher of teachers, embarking on a self-study allowed me to go beyond investigating the content I teach and required me to investigate the manner in which it needed to be taught. This paper is an analysis of the dynamics of teaching and learning that I experienced as a university instructor who taught an instructional methods course to teacher candidates. Throughout the course, the teacher candidates were immersed in a constructivist theory of learning that underpinned the instructional strategies that I modeled throughout the 20 sessions. Twenty-eight fifth-year concurrent education students participated in two separate focus group interviews on two campuses at the end of the course. This data was collected along with my weekly reflective journal. Findings indicate that through an immersion experience dissonance ensued. In spite of the inherent challenges, both the teacher candidates and I were more likely to continue to apply parts of a constructivist learning theory beyond the present and extend what we had learned, into our future teaching and learning practice. If successful, both student and instructor have the potential to create more fully developed classrooms meeting the needs of most learners.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".