Moving Towards an Outcomes-Based Curriculum Model in Design Education: an Action Research Study at OCAD University
Bibliographic record
Abstract
This paper is in preparation for the research that I will be conducting as a PhD Candidate at the Ontario Institute For Studies in Education (OISE), University of Toronto entitled “Implementation of Outcomes-Based Education at the Ontario College of Art and Design (OCAD) University: An Action Research Study of an Interdisciplinary Design Course” under the supervision of Professor Katharine Janzen. In this discussion, I intend to first establish the background, the context and the purpose of my research. Then I review the principles of outcomes-based education with an emphasis on design pedagogy. Finally, I will lay the ground for the action research study that I intend to conduct in an interdisciplinary design course that I teach at OCAD University (OCAD U) through the identification of the theoretical framework, research questions and research methodology of my study as well as its practical application and future contribution to the field of study.
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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 | 0.045 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".