Design Studio Matrix: Supporting the Decision-Making Process as Part of a Reflective Practice
Bibliographic record
Abstract
Design is described as a process of making decisions based on reflection in and on action (Schön, 1983). This report outlines the findings of the Design Studio Matrix: Supporting the Decision-Making Process as Part of a Reflective Practice research project, and provides recommendations for both future research and teaching. The Design Studio Matrix was funded by the grants program of the Taylor Institute for Teaching and Learning at the University of Calgary. The principal grant holder was Dr. Fabian Neuhaus, Associate Professor at the School of Architecture, Planning and Landscape, University of Calgary. The project was carried out at the School of Architecture, Planning and Landscape, University of Calgary, with a focus on three Masters design studio courses: the EVDS 620 Urban Design Studio/EVDA 782.01 Senior Architecture Design Studio course in fall 2019, the EVDP 644 B02 Advanced Professional Planning Studio course in winter 2020 and the EVDP 616 Planning course in fall 2020. The project ran for two years, from spring 2019 to Spring 2021. Its aim was to analyze design studio pedagogy and to further develop the Design Studio Matrix (DSM), a teaching and learning tool that was developed by Dr. Graham Livesey, Dr. Enrica Dall’Ara and Dr. Fabian Neuhaus. The hypothesis was that the DSM would help shift the focus of design education away from the product towards the process and the reflection thereof. The research was led by Sandra Abegglen and adopted a mixed or multi method approach consisting of focus group discussions, semi-structured interviews, in-class observations and questionnaires. In addition, material created by the students such as diagrams and survey data were analyzed. Ethical approval for the research was sought and granted by the University of Calgary Conjoint Faculties Ethics Board in August 2019. A total of 100 students have worked with the DSM to date. Of those, 53 students were registered for one of the courses included in this study, with 38 students fully participating in the research and 3 students partially participating. Participation in the research project was voluntary, with students being able to opt in or out of all, or particular research elements.
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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 | 0.072 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".