OUTCOME COMPARISON FRAMEWORK FOR DESIGN COURSES IN ENGINEERING AND TECHNOLOGY
Bibliographic record
Abstract
Abstract – Inconsistent transfer policies, lack of articulated syllabi, and subsequent differences on the delivery of comparable courses are a few examples of the obstacles that Ontario students face when trying to change programs. This study sought to develop a framework to support transfer between engineering and engineering technology programs in Ontario using explicit and implicit course outcomes to help develop and define new pathways. Primarily focusing on program expectations in introductory design courses this study compared the content and context of design projects in different institutions and programs across the province. The contextual framework, namely the “Outcome Comparison Framework for Engineering and Technology”, synthesized relevant elements from existing frameworks which can collectively identify the differences in the context of learning in engineering and technology disciplines. This framework looks into disciplinarity, use of tools and design thinking required to successfully finish a design project. We collected design projects from 5 technology programs and 4 engineering programs across the province and coded them based on the content targeted by the project description and the three dimensions of the framework. Content analysis for design courses showed an overall low percentage of alignment between the learning outcomes and the project descriptions across the board. It was found that engineering design courses were more focused on principles of engineering design (problem definition, stakeholder needs, idea generation, decision making) and development of professional characteristics (workplace communication skills, ethics, etc.); but technology design courses, focused on the use of more “hands-on”' skills (building/implementation, troubleshooting, etc.)
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".