Modifying the “Bell Ringer” Test Methodology to Evaluate Experiential Learning Outcomes in an Engineering Course
Bibliographic record
Abstract
In the third year of the Mechanical and Materials Engineering at Western University, students with limited or no prior exposure to electricity and electronics are required to take a course in electrical fundamentals. The course has both a theoretical and a practical component. Last year, due to concerns with generative AI, a practical lab component was introduced to the course laboratory component of the course in addition to two, hand-in labs. With an increase in the size of the third year-cohort and a desire to make the practical lab exam more comprehensive and efficient, a different strategy to conducting the practical lab was desired. In a traditional “Bell-Ringer Test” employed to evaluate practical learning outcomes such as performed in biology or chemistry disciplines, students are required to perform tasks at a station in a laboratory setting in a defined time. A variation of this method was employed to perform the practical lab evaluation in this year’s course delivery. This paper outlines the rationale for the process, the details of the delivery, and the associated outcomes.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".