After the final: knowledge retention for online and face to face histology students (725.12)
Bibliographic record
Abstract
We have developed an online histology laboratory course covering the same material as a Face to Face (F2F) course. Previously, we reported no significant differences in outcomes between the formats. Here we investigate knowledge retention four to six months following completion of the course. In this study, online and F2F students were invited to complete a 20 question practical quiz with a time limit of 20 minutes. These questions covered the main tissues and organs highlighted in the course. Students completed this quiz either immediately following or between four and six months after the completion of the course. Students who attempted the quiz immediately following the completion of the course scored a mean of 73.1 ±12.8% while those who attempted the quiz several months following showed significant reductions in their scores (online 36.6 ± 15.1% and F2F 43.0 ± 17.3%). However, there was no significant difference in the knowledge retention for students who took the course in the online or F2F format. These results suggest that although knowledge retention declines for both online and F2F students, there is no difference in knowledge retention between students who study histology in the online or F2F format. Grant Funding Source : Supported by: Socical Sciences and Humanities Research Council of Canada
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".