Development and Delivery of an Electric Circuits Course Featuring Competency Based Assessment for First Year Engineering
Bibliographic record
Abstract
Most common-core first year engineering programs in Canada include an introduction to electric circuits and electromagnetic physics. The launch of the RE-ENGINEERED first year program at the University of Saskatchewan has provided an opportunity to try something different in this arena. The RE-ENGINEERED program includes a “spine” of electric circuit analysis and the related physics that runs through both semesters of the first year. The modular and highly integrated structure of the RE-ENGINEERED program has allowed for accelerated courses that take advantage of timely learning in other courses. In the fall term, students are introduced to direct-current, resistive circuit analysis in a six-week, fifteen-contact-hour module. In the winter term, they experience an accelerated physics course which covers the electricity, magnetism, capacitance, and inductance concepts often taught in tandem with basic circuit analysis. The students then finish the winter term with an intensive course on alternating-current circuit analysis. The fall term course fully adopts the competency based assessment system of the RE-ENGINEERED program, and uses in-house-developed quizzes and tutorials on the most basic concepts and calculations to scaffold students to solving more complex circuit analysis problems. The course forgoes a hands-on lab component and focuses on circuit simulation using an open-source simulation package. Concurrent math and MATLAB courses introduce required linear algebra concepts just in time for use in the circuit analysis problems. This paper describes the development and delivery of the fall term course, including how the learning outcomes were synthesized and then used as the basis for the development of all other aspects of the course to ensure constructive alignment. Instructor and student impressions of the first delivery of the course are presented along how lessons learned will be applied to modify the course for future offerings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.038 |
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".