Non‐native english language speakers benefit most from the use of lecture capture in medical school
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical education in the United States and Canada continues to evolve. However, many of the changes in pedagogy are being made without appropriate evaluation. Here, we attempt to evaluate the effectiveness of lecture capture technology as a learning tool in Podiatric medical education. In this pilot project, student performance in an inaugural lecture capture-supported biochemistry course was compared to that in the previous academic year. To examine the impact of online lecture podcasts on student performance a within-subjects design was implemented, a two way ANCOVA with repeated measures. The use of lecture capture-supported pedagogy resulted in significantly higher student test scores, than achieved historically using traditional pedagogy. The overall course performance using this lecture capture-supported pedagogy was almost 6% higher than in the previous year. Non-native English language speakers benefitted more significantly from the lecture capture-supported pedagogy than native English language speakers, since their performance improved by 10.0 points. Given that underrepresented minority (URM) students, whose native language is not English, makes up a growing proportion of medical school matriculates, these observations support the use of lecture capture technology in other courses. Furthermore, this technology may also be used as part of an academic enrichment plan to improve performance on the American Podiatric Medical Licensing Examination, reduce the attrition of URM students and potentially address the predicted minority physician shortage in 2020.
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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 | 0.000 | 0.005 |
| 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 it