Reflecting on and Articulating Teaching Experiences: Academics Learning to Teach in Practice
Bibliographic record
Abstract
Higher education teaching demands theoretical and practical knowledge. It goes without saying, a strong knowledge of one’s subject is essential. But while teaching principles are generally gleaned from short courses, it is one’s own teaching that offer the main ground for gaining practical teaching knowledge. To examine this claim we have conducted an interview-study in which Swedish business administration academics have described where they learned something about their teaching. An interpretative analysis led to six different lessons learned, ranging from the personal, through the pedagogical, to the interpersonal. We claim there are three necessary opportunities to turn the experience into an occasion for learning: reflection over experience, the opportunity to articulate one’s experience, and a forum for sharing; particularly experiences connected with risk-taking. We conclude that academics need opportunities to reflect on and articulate their learning experiences related to the practices of teaching, and to share and discuss them with colleagues.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| 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".