Unpacking how instructors’ past experiences influence pedagogical decisions
Bibliographic record
Abstract
It is commonly believed that faculty teach the way they were taught (Lortie, 1975). However, how instructors’ past educational experiences influence their classroom practices is unclear. To unpack how past experiences influence instructional behavior, we conducted video-stimulated recall interviews with 14 faculty; participants watched recordings of their instruction and reflected on their instructional decisions, the beliefs underlying those decisions, and past experiences influencing those beliefs. We analyzed interview transcripts using the Theory of Planned Behavior (Azjen, 1980) as an interpretive lens. Analyses reveal that (1) instructors rarely copy behaviours from past educational experiences; instead, they tend to innovate and develop behaviours through trial and error; (2) instructors have clear intentions that guide their behaviours, and these intentions are frequently informed by their own past experiences as students; and (3) instructors often lack objective evidence that their behaviours achieve the intended outcomes they described.\nThis research was approved by the University of Guelph's research ethics board.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".