Using Broad-Disciplinary Laboratories to Teach Electric Circuits to First Year Students While Introducing Design and Professional Lab Practices
Bibliographic record
Abstract
It is possible to engage first year students tolearn the history and applications of electrical systems invarious disciplines from power systems, wireless systems,control, digital systems, biomedical, and micro-sensorsthough laboratories that emphasize design and expectprofessionalism. Teaching electrical systems starting withthe traditional electric circuits first approach provideslittle motivation for first year engineering students. Ourapproach has been to complement lectures in electricaltheory with a sequence of laboratories that focus onupper level electrical systems specialties. Laboratorydesign projects start with a discussion of historical andmodern application of the presented technologies. Thispaper discusses some of the challenges faced andsolutions implemented to enable the application of thebroad-disciplinary laboratories. Professional labpractices have been introduced with the qualitativeassessment of student designs and the expectation thatthey maintain cleanliness of the laboratory and supplyinventory. We have found that TAs are more comfortable(and more critical) in their critique of student designswhen assessment is done using verbal quality indicatorsin place of simple numerical assignment. Accordingly,students make greater effort towards quality labpractices, as opposed to only finding the numericalsolutions. We have further observed that students willmeticulously return the laboratory to its initial state, ifthey were required at the outset to source suppliesthemselves, as opposed being given them.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".