Challenges with Implementing Oral Exams in Post-Secondary Mathematics Courses
Bibliographic record
Abstract
<p><span lang="EN-US">In this study, seven mathematics professors and instructors were interviewed to share their thoughts about implementing oral assessment in mathematics courses in Canada and the United States, where oral assessment in mathematics is not part of the educational system. Four out of seven mathematics professors and instructors were educated in Poland, Romania, Bosnia, and Ukraine, and they are currently teaching mathematics at a university in Canada. The other three professors were educated in Canada, Germany, and the United States, and they are currently teaching at a university in Germany. Five participants had previously experienced oral examination in mathematics</span><span>,</span><span lang="EN-US"> while the other two had never been exposed to oral examination in mathematics throughout their schooling. The results showed that implementing oral assessment in mathematics courses at the university level in Canada and the United States might raise some students</span><span>’</span><span lang="EN-US"> and professors</span><span>’</span><span> <span lang="EN-US">concerns.</span></span></p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".