Building pedagogical partnerships
Bibliographic record
Abstract
Engaging students as pedagogical partners in teaching and learning in higher education is becoming increasingly prevalent. However, developing and sustaining such partnerships can be challenging. The present study highlights the potential of utilizing work-integrated learning (WIL) students as partners. Semi-structured interviews were conducted with 18 university instructors to explore how access to an online learning assistant (OLA) program helped them navigate remote instruction challenges. The OLA program was a novel WIL initiative providing co-operative (co-op) education students with full-time, paid work to assist instructors transitioning to remote learning. Unexpectedly, our findings demonstrate that pedagogical partnerships emerged in the context of this WIL program, leading to teaching and learning benefits. Online learning assistants were able to assist instructors with many of the difficulties they faced, although some program challenges also emerged. Our findings suggest that full-time, paid co-op student positions offer a unique program structure that make them ideal for the development and ongoing success of pedagogical partnerships.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".