Bibliographic record
Abstract
The Arch-App was developed as a mobile learning tool in a partnership between Ryerson University’s Department of Architectural Science and the Ryerson University Library and Archives. It is a free interactive mobile app that uses geo-location data to help users identify and learn about the architecture, design, and history of the city of Toronto. It has generated considerable public attention as an innovative and engaging m-learning platform. Our research expanded its usage into the School of Interior Design to engage a broader cross-section of undergraduate students. Focus was centred on its’ usefulness as a pedagogical tool for design history and theory streams in addition to studio classes. Our research measured its effectiveness in spurring student choice, flexibility, and critical synthesis of existing architecture and design paradigms using real-world, real-time data dissemination. Using the app interface on a smart device, students were able to move beyond traditional classroom discussion of the built environment, into local, community spaces. The current iteration of the app includes data generated and collected by research assistants and undergraduate students and covers site history, building exteriors and interiors, drawings, plans, and elevations. Design students were enabled to walk around the city, creating a personalized context-aware learning environment using their smart devices to guide their learning trajectories without a perceived hierarchy. This promoted place-making based on individual preferences and, for visitors to Toronto, an effective and interesting way to get to know the city. Students linked local design traditions to those practiced around the world. Data was collected measuring the student research process including both positive and negative aspects. Results revealed the Arch-App’s strengths and weaknesses and indicated trends in undergraduate student behaviour based on preferences as well as research habits. Efficiency, accuracy, and depth of content were most appealing and led to enhanced participation, perception of pedagogical value, and development of research skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".