A COMPARISON OF THE TEACHING PRACTICES OF NOVICE EDUCATORS IN ENGINEERING AND OTHER POST-SECONDARY DISCIPLINES
Bibliographic record
Abstract
There is a perception in higher education that engineering educators teach differently than those in other disciplines. Surveys of student engagement consistently rank the undergraduate engineering experience lowest among ten disciplines, as do faculty surveys of student engagement. These results suggest there is opportunity and need to improve the engineering education experience. This research sets out to identify differences in the teaching practices of beginning engineering educators from those in other disciplines. Using the Dreyfus and Dreyfus model of skill acquisition as a framework, this study examines institutional data collected during four consecutive terms of mandatory teaching observations of new full-time and selected part-time instructors. Descriptive statistics found that the performance of novice educators in engineering-related disciplines did rank lowest overall compared to all other disciplines. This analysis also found that there is little difference in the teaching practices of novice engineering educators from those of their more experienced colleagues. Thematic analysis found that traditional engineering classroom practices such as lecture and worked examples are common, and could be enhanced by including opportunities for meaningful active learning. These results can inform both engineering educators and those responsible for their educational development about the common teaching practices of novice instructors and will be useful in shaping the professional development opportunities offered to engineering educators.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".