The One2One Structured Oral Examination is a Valuable and Positively Rated Science Education Tool that Drives Academic Success.
Bibliographic record
Abstract
Structured oral examinations (SOEs) result in higher test scores than traditional written assessments, but there lacks reproducible quantitative evidence supporting knowledge acquisition and retention. A modified SOE—called the One2One—whereby students present prepared answers to an instructor was evaluated for effectiveness in large classes despite its resource-intensive nature. This study used a post-assessment survey (Efficacy Assessment Survey, EAS) to measure the effect of the One2One on knowledge acquisition and retention, as well as student perceptions of its usefulness and perceived value. The One2One helped students learn and retain content better than by didactic lecture alone as demonstrated by significantly higher scores on One2One content as compared to control content (p<0.05) on the EAS (t-test) and this knowledge was retained until the end of the semester as measured by regression analysis. A previously identified drawback of SOEs is student-reported anxiety, however students’ perception of the SOEs’ usefulness and value are understudied. Here, thematic analysis of student feedback identified the One2One as being useful, a driver of learning, and of high professional value, albeit stressful. Though more resource intensive than traditional assessment methods, the One2One is a positively rated, authentic evaluation tool that motivates student learning.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".