EFFECTIVELY ASSESSING PROFESSIONAL ENGINEERING SKILLS
Bibliographic record
Abstract
This paper outlines the assessment dilemmas and challenges that were experienced by faculty members and students alike during initial iterations of APSC 190 (a first-year, professional engineering skills core course in the Faculty of Applied Science at Queen’s University) and how the adoption and implementation of the ICE model of assessment [1], [2], [3] served to address those challenges. ICE, an acronym for Ideas, Connections and Extensions is based on cognitive/transformation theories of learning similar to those put forth by Biggs’ and Collis’ SOLO taxonomy[4], and describes learning as a process of growth from novice toward expert. Unlike SOLO, ICE was intentionally designed for use in the classroom by teachers and students. The simplicity of the model increases its utility and portability to a host of learning activities and furnishes an accessible vocabulary and framework to facilitate communication about expectations for learning. The paper includes an overview of the ICE model, suggestions for implementation and the effects and limitations of the model for use in professional skills courses. Current-use examples are provided that illustrate the model’s utility and its implications for shaping student learning.
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 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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".