Outcomes-Based Assessment in Action: Engineering Faculty Examine Graduate Attributes in their Courses*
Bibliographic record
Abstract
In 2009, the Canadian Engineering Accreditation Board (CEAB) called for the assessment of 12 graduate attributes in all Canadianaccredited engineering programs. As part of this process, data are required from a variety of stakeholders, including the facultiesresponsible for teaching the host of courses offered in Canada’s diverse engineering programs. This paper describes the second yearof a three-year study in the Faculty of Engineering at the University of Manitoba that explores how the CEAB graduate attributesare manifested and measured in its curricula. The four attributes targeted were Problem Analysis, Use of Engineering Tools,Communication Skills, and Ethics and Equity. Fifteen instructors from each of the Departments of Biosystems, Civil, Electricaland Computer, and Mechanical Engineering considered the presence of these attributes in one of their engineering courses taughtin the academic year 2012–13, using a self-administered checklist. Findings indicated that the traditional attributes in engineeringwere assessed more frequently than the professional attributes, and that specifically, there was little assessment evidence of Ethicsand Equity and theOralfocus of Communication Skills. There was some evidence of formative assessment, but generallyassessments were limited to traditional quantitative, summative assessments. Competency levels were expressed in a variety of ways,highlighting the need for the development of a common language for assessment. The study underscores the different rolesassessment can take and the complexity of sustaining a faculty-wide, outcomes-based assessment protocol.
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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 | 0.092 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".