Impact of Lectures given via TopHat Active Learning Platform on Medical Student Performance on Assessments
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
Introduction: TopHat (Tophatmonacle Corporation, Toronto, Ontario, Canada) is an active learning platform that allows instructors to present lectures and engage with students in real time by testing their knowledge through instructor-written questions. Some students participate in these practice questions while others do not. All students complete questions on formal assessment. Objective: To assess whether student engagement in TopHat-delivered Biochemistry, Microbiology, and HDT lectures and their associated practice questions has an impact on formal assessment performance. Methods: Statistical analysis of student performance and engagement was conducted using the TopHat-generated “Learning Insights” spreadsheet. Appropriate statistical methods will be employed to analyze data associated with Tophat participation. Results: We anticipate that the proposed study will indicate whether participation in TopHat questions during biochemistry lectures impacts student performance on exam questions. Conclusions: Participation in TopHat practice questions during lectures is correlated with a higher formal assessment performance.
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.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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