Promoting Career Planning Through the Use of an Engineering Experience Evaluation for Licensure
Bibliographic record
Abstract
Professional registration (P.Eng.) applicants in B.C. must use the Engineers & Geoscientists BC web-based Competency Experience Reporting System (CERS) to have their work experience evaluated. CERS measures competencies – measures of the ability to perform the tasks and roles of an occupational category to standards expected and recognized by employers and the community at large – in seven competency categories, each of which can be related to the twelve CEAB graduate attributes.As part of a university-level course in engineering professionalism, students were given an assignment to use CERS to conduct a self-evaluation and make recommendations for their own future professional development.To measure the perceived effectiveness of the assignment, students completed three identical questionnaires: one before the topic was introduced, one after a guest speaker presentation on the topic, and one after submitting the assignment. The questionnaire measured each student’s degree of knowledge or understanding of ten different aspects of professional registration and professional development. The results indicated a progressive increase in agreement between the first, second and third questionnaire for all ten questions, with the greatest increases relating to registration procedures and students’ identification of shortcomings of their own experience.Usage of the competency assessment system by regulators is being expanded in Canada, which potentially provides the opportunity to conduct similar student assignments within other engineering programs.
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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 | 0.008 | 0.015 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".