EXAMINING FOURTH YEAR ENGINEERING STUDENT PERCEPTIONS OF GRADUATE ATTRIBUTE COMPETENCIES: YEAR TWO
Bibliographic record
Abstract
As the Engineering Faculty at the Universityof Manitoba shifts its curriculum from an input-based toan outcomes-based pedagogy, data from diverse sourcesare being collected. Among them, indirect data are beinggathered from students using a student exit survey. Thesurvey has been developed over the past three years toexplore graduating students’ perceptions of theiraptitudes and their engineering program’s strengths andweaknesses in regards to the 12 CEAB graduateattributes. It is comprised of the 12 attributes, with eachattribute further defined by six indicators. Theseindicators reflect the levels of Bloom’s Taxonomy ofEducational Objectives in the Cognitive Domain:knowledge, comprehension, application, analysis,synthesis and evaluation. The student exit survey was firstadministered to graduating mechanical engineeringstudents at the end of Fall semester 2012 and then at theend of Fall semester 2013. This paper describes thesecond year of the study, and discusses the datacomparatively with the findings from the first year. Thisstudy offers the Engineering Faculty an understanding oftheir Mechanical Engineering students’ experiences withand perceptions of the CEAB graduate attributes. It willbe used to provide feedback at instructor, program andfaculty levels as the University of Manitoba’s Faculty ofEngineering continues to implement its cycle of programdevelopment and improvement.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".