Transitions North America: What is needed to help teachers better utilize space as one of their pedagogic tools
Bibliographic record
Abstract
In 2017, the Transitions Symposium explored the overarching theme of Inhabiting Innovative Learning Environments. The symposia were held in three cities: Melbourne, Australia; London, UK; and Grand Rapids, Michigan, USA. In collaboration with our project partner, Steelcase Education and with sponsorship from the DLR group, the North American symposium brought together contributors, who addressed the simple question; ‘How are teachers making the transition into innovative learning spaces, and how does evidence of success inform future best practices?’ The papers were grouped into four themes of Inhabiting Design, Teacher Practices, Change and Risk, and Measuring Impact. Participants presented an 8-minute synopsis of their research. There was no concurrent sessions—all participants listened to every presentation. At the end of the presentations in each theme, expert interlocutors discussed key themes that had emerged, drew inferences, and then elicited audience discussion on issues pertinent to each theme. Audience participation was encouraged and robust, drawing perspectives from various sectors including fellow higher degree researchers, industry representatives from design, building and ICT, academics working in this field, and those embedded in implementing new classrooms at a policy level. The day was an intense and highly informative exchange of ideas. The papers included in this volume, Transitions North America, were selected for presentation through double blind peer review. The symposium took place on Thursday, 14 September 2017, at the Steelcase Education Center in Grand Rapids, Michigan, USA. Sixty-one participants from industry, policy, schools and academia attended the symposium. Following the event, each paper was reviewed and the comments sent to authors in order to help them prepare a revised version to strengthen the continuity and congruence of the proceedings. The result of this revision process is the backbone of this volume and represents what we consider to be a stimulating and careful set of analyses about how teachers transition into innovative learning spaces.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".