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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".