SURVEYING FOURTH YEAR ENGINEERING STUDENT PERCEPTIONS OF GRADUATE ATTRIBUTE COMPETENCIES
Bibliographic record
Abstract
As the Faculty of Engineering at the University of Manitoba begins to emphasize outcome based teaching and assessment along with the traditional input-based teaching and assessment, data are being collected in a variety of forms. Some of the indirect data being gathered comes from students in the form of the Student Exit Survey. This survey was developed to measure students’ perception of how well their program prepared them with regards to the CEAB twelve graduate attributes. The survey asked students to consider a large number of indicators for each of the graduate attributes.The indicator list was originally constructed with the intention of sufficiently defining each attribute for the five engineering programs in the faculty while providing variety and choice. Therefore, the list was fairly extensive, and at times iterative and unwieldy. When revisiting the original Student Exit Survey, two factors ascended in importance: student feedback on their personal attribute competencies as developed within their program, and how to define attribute competency levels.To establish competency levels and make indicators more manageable for faculty and students, the indicators for each attribute were revised to reflect the six levels of Bloom’s Taxonomy of Educational Objectives in the Cognitive Domain: knowledge, comprehension, application, analysis, synthesis and evaluation. This new attribute/indicator format was then developed into theStudent Exit Survey and given to fourth year Mechanical engineering students in Fall 2012. This paper describes that effort and analyzes the initial data from this first pass. This data will be used to inform the continued revision of the Student Exit Survey until it is a reliable and valid instrument for providing feedback at instructor, program and faculty levels as the University of Manitoba’s Faculty of Engineering forges ahead with its continual cycle of 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".