Interdisciplinary teaching practices: Reflections from a teaching triangle
Bibliographic record
Abstract
Background and aim: There is an increased understanding of and appreciation for teachers' work from other disciplines, primarily for formulating individual plans and enhancing one's teaching based on observations and shared reflections. This article reviews how reflective practice, which is self-initiated and focused, informs the understanding and improvement of teaching practices, demonstrates interaction with students, and guides teaching experiences. This article aims to explore reflective practices that were meaningful for engaging in in-class instructional teaching practices.Methods: A self-study methodology was used to examine the complicated relationship between teaching and learning and knowledge in action of teacher education pedagogy.Results and discussion: As teacher, we understand the importance of problem-solving, establishing connections between relationships, and motivating students to think about missing connections or reconsidering them. Implications: The benefit of the Teaching Triangle was enhancing interdisciplinary relationships, understanding professional teaching relationships, and learning from each other without boundaries.Conclusions: Three aspects of the interdisciplinary reflective practice that emerged were adopting philosophy and purpose-driven goals; facilitating teaching pedagogy and technology; and creating culturally safe and effective student learning environments.
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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 | 0.033 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.027 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.008 | 0.026 |
| 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".