Do Lectures Matter? Lecture Attendance in Online and Face to Face Histology Courses
Bibliographic record
Abstract
We have developed an online histology course covering the same material as a Face to Face (F2F) course. Previously, we reported no significant differences among outcomes between the formats. Here we investigate differences in student attendance. The online course uses Wimba Classroom to facilitate lectures. Students can attend the lectures live, or view archived versions later and also access archives for additional review. Wimba classroom records each time a student enters the virtual classroom. F2F attendance was measured by passing attendance sheets though the class for each lecture. Data indicates that F2F attendance drops mid‐term and then recovers prior to testing. Similarly, online attendance also drops mid‐term; however, these students can view the archived lecture at a later time. Written exams for this course are multiple choice questions each based on the material covered in a specific lecture. There is a correlation (r2 = 0.211, p< 0.05) between attendance and exam grades for the F2F students. For the online students who can view the archives many times, there is also significant positive correlation between attendance and exam outcomes; however, this correlation becomes negative after two viewings per lecture. These results suggest that lecture attendance improves outcomes and that while repeated viewings of the lecture are beneficial, this benefit declines with more than two viewings. Grant Funding Source : SSHRC
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 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".