Design Affordances and User Perception: Investigating the Relationship Between Space Design and Pedagogical Possibility in an Innovative Learning Environment
Bibliographic record
Abstract
This major research project explores how teachers and school leaders at Branksome Hall, \nan all-girls International Baccalaureate (IB) school in Toronto, Ontario, Canada perceive \nthe affordances (potential uses) of a new Innovative Learning Environment (ILE) under \nconstruction. This study adopted a social constructivist approach. First, primary research \ninvolving virtual reality (VR) walkthroughs identified the spatial features participants perceived \nas supporting their current and future teaching practices. Following this research, a teacher \nworkshop built upon these features, exploring how well they aligned with the stated aims \nof the building. Abductive thematic coding was applied to the data according to the themes \npresented in Frelin and Grannäs’ (2022) TEALE model—two additional themes were also \nuncovered that fall outside of this framework. The findings aim to guide the transition to utilizing \nthe iCAST effectively through the creation of spatial profiles for each space under study. Each \nprofile provides a dynamic resource for teachers that can be used to empower an exploration \nof the impact of space on pedagogy and to inspire the development of innovative practices. \nSeven insights were developed based on the analysis of primary data and secondary research. \nThese insights were used to guide the development of five key practice implications for Branksome \nHall’s next steps, which involve cross-team collaboration, revisiting the building’s aims, engaging in \nfutures workshops, and intentionally developing teachers’ spatial literacy and professional learning. \nThis project highlights the evolving nature of educational purpose and how educators’ perceptions \nof space are influenced by their core educational philosophies. Interestingly, the affordances \nperceived outside of the TEALE model align more closely with the European concept of “Bildung” \nand a more participatory approach to 21st-century learning.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".