THE ATTRIBUTE ASSESSMENT PROCESS AT THE UNIVERSITY OF MANITOBA: YEAR TWO
Bibliographic record
Abstract
This paper describes the process in the second year of a three year study at the University of Manitoba that looks at how the 12 CEAB graduate attributes are manifested and measured in the engineering curriculum. The four attributes chosen for this year’s study were Problem Analysis, Use of Engineering Tools, Communication Skills, and Ethics and Equity. Nine instructors from each of the Departments of Biosystems, Civil, Electrical and Computer, and Mechanical Engineering were asked to consider the presence of these attributes in one of their engineering courses taught in Fall 2012. The checklist for this study was revised based on the results of the pilot study conducted in 2011-12, and in an effort to begin to define student attribute competency levels and demonstrate outcomes-based assessment. Similar to last year, this study found that the hard skills in engineering were assessed more frequently than the soft skills, and inparticular, there was little assessment evidence of Ethics and Equity. The majority of instructors reported using assignments and reports as evaluation tools, and communicating evaluations to students using numerical marks and written comments. Competency levels were defined in a variety of ways, highlighting the need to establish a common language for assessment. Finally, this paper reports on the challenges observed in the construction and administration of the survey and outlines next steps.
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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".