A COMPARISON OF THE TEACHING PRACTICES OF NOVICE EDUCATORS IN ENGINEERING AND OTHER POST-SECONDARY DISCIPLINES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it