A COMPETENCY-BASED, STUDENT-CENTERED ASSESSMENT MODEL FOR ENGINEERING DESIGN
Bibliographic record
Abstract
The new first-year engineering design and communications course at the University of Calgary has adopted a competency-based, student-centered model for assessing learning. Satisfactory performance in this course requires mastery of core competencies in four categories: ability to function as a member of a team, ability to contribute effectively to product or process design, ability to communicate effectively using the written word, ability to communicate effectively through the medium of drawing. Every assignment in the course is aimed at evaluating one (or more) of the core competencies from these categories. Student work is assessed as Excellent, Good, or Requires Additional Work. Because our focus is on competency, we permit students to redo any of their work to achieve a better assessment. Students must achieve the minimum of a Good on every assignment to have established competency and pass the course. Students can also redo assignments to move from a Good to Excellent assessment. Students compile term work into portfolios. The portfolios illustrate the progression of learning to both instructors and students. Students also use the portfolio to highlight their design and communication abilities to future employers. The new competency-based approach used at the University of Calgary is more effective than traditional assessment models because it requires students to learn from one another and to reflect on their learning. Students receive feedback by following a four-step process: 1) Comparison to posted examples of student work, 2) Discussion with other students, 3) Generation of a written self-assessment, 4) Feedback on self-assessment by instructors. This assessment approach reinforces the skills needed for engineering design.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".