for understanding impact Authors:
Bibliographic record
Abstract
The 30-hour Course Design and Teaching Workshop (CDTW) has been offered to professors for over ten years at one Canadian university and more recently has become an annual offering at two others. The intensive workshop provides professors with an opportunity to discuss and reflect on their teaching with colleagues, and initiate changes to enhance the quality of student learning. Formal follow up groups are being conducted this year for the first time at all three universities to provide continued collegial support as faculty implement the changes they designed in the workshop. We have sought to evaluate the impact of these activities through a number of studies that examine what participating professors learned and the impact of what they learned on teaching and student learning. First, we began by considering changes in professor thinking about teaching and learning before and after the workshop. Moving closer to evaluating the impact on teaching, we have also considered how professors apply what they learn in the workshop to plans for teaching and how they report implementing these plans. Thirdly, we have moved even closer to linking professor learning to students by documenting the actual implementation of teaching plans and student perceptions and feedback about this. In this paper, we discuss four studies, specifically chosen because they are directly linked to student learning either by way of intentions reported by the professor or by data collected from students. Rationale and Format of the CDTW and Follow-up groups The Course Design and Teaching Workshop (CDTW) and the follow up groups were initially designed to address questions such as the following resulting from our practice as faculty developers and from our reading of the literature: Why do short topical workshops on teaching methods not seem to lead to the changes in teaching—specifically learning-oriented teaching—that we seek to promote?
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 | 0.010 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.685 | 0.449 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".