Inclusive curricular improvement: experiences adapting a strength-based evaluation approach
Bibliographic record
Abstract
We describe key factors in adapting to a computing context the Appreciative Inquiry methodology, which emphasizes participatory exploration of a design (e.g., course or curricular design) and encourages and supports diverse perspectives via a story-driven, strengths-based approach. Appreciative Inquiry is a stakeholder-driven, qualitative methodology for exploring a design or organization that focuses on what is working well and how to preserve and build on that success. As a case study, we share our experience using Appreciative Inquiry to evaluate and improve a non-majors' first-year computer science course that is intended to support diverse students. We describe the methods that we used to give context to our adaptation of Appreciative Inquiry. We then highlight our reflections on our evaluation and the strategies that we learned from this context to effectively apply Appreciative Inquiry. We believe that our approach yielded different and deeper results than we would have found had we not used Appreciative Inquiry. Further, we believe that its focus on strengths may have attracted participants who would not have otherwise participated; its participatory nature is a practical way to include students and teaching assistants as co-evaluators and give power to their voices. We argue that other computer science educators should consider trying this approach to tap into perspectives and input overlooked by more common research methods which focus instead on quantitative data collection or addressing deficiencies and problems.
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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 | 0.100 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".