Assessing Design Ability through a Quantitative Analysis of the Design Process
Bibliographic record
Abstract
Current educational practices presume engineering students develop design skills through dedicated design courses and projects. There are many variations in how the courses and projects are implemented, causing disagreement between educators on the most effective methods. Few assessment tools exist to evaluate these claims, and no quantitative tools were found that provide students with an immediate formative evaluation of their design ability. We created an online, quantitative design ability assessment tool to compare the design approaches of students to experienced engineers. This article explores whether design ability can be assessed quantitatively through this tool. Significant findings include the following. Experienced engineers use fewer steps than students and estimate the project would require more time than both student groups. First-year students use extraneous steps, produce the wrong product, are least likely to iterate, and exceed the target number of hours. Second- through fourth-year students utilized the most time in developing alternative solutions and demonstrated design knowledge gained from previous experience. Because these findings align with the literature, we conclude that aspects of design ability can be assessed using a quantitative tool, which provides students with formative design development and educators with quantitative feedback on variations in their project or course.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".