Effectiveness of a Brief Workshop Designed to Improve Teaching Performance at the University of Alberta
Bibliographic record
Abstract
PURPOSE: To determine whether a two-day teaching enhancement workshop at the University of Alberta improved participants' teaching performance as rated by students. METHOD: Workshop participants (academic staff or residents) were asked to assess the value of the workshop. In addition, students were asked to rate instructors' teaching abilities before and after the instructors participated in the workshops, by completing a five-statement questionnaire routinely used to assess instruction at the University of Alberta. For control purposes, ratings were also obtained for a group of instructors who had not taken the workshop, over a similar time period. The authors used data from 1993-2002. RESULTS: The participants uniformly regarded the workshops as helpful. Both faculty and residents regarded the short teaching exercise as the most important component of the program. Of the instructional sections, the presentations on objectives and on structure (set, body, closure) were rated most highly by both groups. The students' mean ratings for the instructors after the workshop were significantly increased, while ratings for those who had not taken the workshop were unchanged CONCLUSION: Short teaching-enhancement workshops are regarded by the participants as helpful in improving their instructional skills. This view is supported by a significant increase in students' ratings of the instructors after they had taken the workshop.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".