Non‐native english language speakers benefit most from the use of lecture capture in medical school
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".