The use of Oral Exams to Evaluate Experiential Learning Outcomes in a Lab Setting
Bibliographic record
Abstract
In the third year of Mechanical and Materials Engineering at Western University, students with limited prior exposure to electricity and electronics are required to take a course in electrical fundamentals. Although outside the traditional boundaries of their engineering discipline, increasingly, electronics has permeated traditional engineering disciplines with conversion of electrical energy to mechanical energy becoming increasingly relevant. To expose students to experiential learning, in the past, students were assigned labs to demonstrate their ability to support experiential learning outcomes. The labs were comprised of a pre-lab component that was to be completed prior to the practical portion of the lab, followed by measurements, analysis and discussion within a lab setting. All components were to be completed by students individually. With the advent of online AI tools, such as ChatGPT and an increase in the number of students in a cohort, the learning value of the labs was diminished, and it no longer was practical to conduct the experiential components of a lab as was performed in the past. New approaches were sought, and this year, landed upon a traditional method of evaluation: the oral examination. This paper outlines the process employed and the associated outcomes of this exercise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".