Reciprocal Review in Educational Development
Bibliographic record
Abstract
The poster illustrates the collaborative writing process undertaken to produce an edited volume on the ICE model (Fostaty Young & Wilson, 2000; Fostaty Young, 2005). With chapters from ten contributors working at universities in Canada, who describe the diverse ways that each have adapted the ICE model of thinking, learning, and assessment into their teaching practices, this edition will foster a culture that learns through a reciprocal review process. Interestingly, while each author reported the transformative effects of the model on both their conceptions of learning and their approaches to course delivery and assessment, their uses of the model each differ from the others’. The reciprocal review process adopted for the collaboration evolved through the editor’s conceptual weaving of a variety of sources: Wilcox’s (2009) work on self-study as educational development; Wyatt and Gale’s (2014) exposition of collaborative writing as inquiry; Troop’s (2017) examination of keyword writing; Healey, Marquis and Vajoczki’s (2013) exploration of SoTL through collaborative writing groups; and the Bowen theory-informed use of Teaching Triangles. Building on these process pedagogies, the interdisciplinary and international lens of this latest edition will be highlighted through the multiple collaborative case studies that are shared.\nIn the summer term of 2018, each author was invited to contribute a chapter to illustrate: (a) their teaching context, (b) their use of the ICE model, (c) the impact of their application of the model on their students’ learning, and (d) their own development as post-secondary educators. As part of the inquiry and writing processes, we expected that with the act of articulating their experiences, each author would gain greater insight into their own teaching practice, as well as into their students’ learning. The greatest potential for professional growth for the contributors and editor alike is expected to be gained through the review process, whereby each author reviews chapters written by two other contributors – one from a discipline closely related to their own and one from a discipline they are less familiar with. In much the same way that Teaching Triangles invite participants to reflect on their own practice rather than to critique others’ teaching, our use of reciprocal review is designed as an invitation to broaden and deepen our conceptions of teaching and learning through the diverse exchange of perspectives and experiences within a developing SoTL community of practice.
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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 | 0.066 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".