Professors' Perceived Barriers and Incentives for Teaching Improvement
Bibliographic record
Abstract
Continuous engagement in teaching improvement is required if professors are to gain the essential knowledge base for effective teaching in ever changing contexts. However, research suggests that professors are not always willing to engage in teaching improvement activities and thus may employ less effective teaching methods that can, in turn, negatively impact student learning. The purpose of this study was to investigate which factors professors perceived as being hindering or motivating to engage in teaching improvement activities. Data were collected from 146 professors from a Canadian research-intensive university. Participants responded to two open-ended questions comprising a subset of a larger survey on engagement in teaching improvement. Specifically, the questions elicited professors ’ perceived barriers and incentives for teaching improvement. Lack of time and a university culture that was not conducive to teaching were identified as the most significant barriers. Greater recognition for teaching and creating a reward system for excellence in teaching were highlighted as the most desirable incentives. Moreover, a comparison was made between tenured and non-tenured participants with respect to perceptions of incentives and barriers. A university culture that is not conducive to teaching was perceived as more hindering for teaching
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.
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 | 0.019 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".