Using Scenarios to Explore the Complexity of Student-Faculty Partnership
Bibliographic record
Abstract
In this paper, we present and reflect on using scenarios and role-plays as an effective approach to engaging in the often complicated conversations about student-faculty/staff partnerships, particularly those involving the scholarship of teaching and learning (SoTL). Students as co-developers of pedagogical processes, as well as co-researchers in SoTL, has become an increasingly valued practice in higher education institutions around the world, one that promises to be transformative in its pursuit to break down the traditional hierarchies and establish more democratic and equitable relationships between faculty/staff and students. While there is a growing body of evidence that demonstrates the value of creating spaces and processes to enhance teaching and learning, it can be challenging to know how to develop and implement partnership in SoTL. How do we actually do it? Many of us need guidance for where and how to get started, how to build effective partnerships, how to work through difficulties, how to share our experiences, and how to invite others into this practice. Informed by our own experiences of engaging in pedagogical SoTL partnerships and drawing upon materials developed for a conference workshop we delivered at the 2019 International Society for the Scholarship of Teaching and Learning (ISSOTL) conference, we argue that scenarios and role-plays, when informed by the principles of Scenario Based Learning (SBL), are effective tools that help explore partnership experiences of faculty/staff and students. We offer considerations for how readers can adopt and adapt scenarios in their contexts and invite further research on the ways SBL contributes to SoTL and partnership.
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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".