The development and assessment of an online microscopic anatomy laboratory course
Bibliographic record
Abstract
Increasing enrollment in post-secondary institutions across North America, along with an increase in popularity of and demand for distance education is pressuring institutions to offer a greater number and variety of courses online. A fully online laboratory course in microscopic anatomy (histology) which can be taught simultaneously with a face-to-face (F2F) version of the same course has been developed. This full year course was offered in the Fall/Winter (FW) terms in both F2F and online formats. To ensure that the online course was of the same quality as the F2F format, a number of performance indicators were evaluated. The same course, offered exclusively online during the summer with a compressed time frame, was also evaluated. Senior undergraduate students self-selected which version of the course they would enroll in. Course assessment outcomes were compared while incoming grades were used as a predictor for course performance. There were no significant differences between the incoming grades for the F2F FW and Online FW courses; similarly, there were no significant differences between outcomes for these formats. There were significant differences between the incoming grades of the F2F FW and Summer Online students. However, there were no significant differences among any of the outcomes for any of the formats offered. Incoming grades were strong, significant predictors of course performance for both formats. These results indicate that an online laboratory course in microscopic anatomy is an effective format for delivering histology course content, therefore giving students greater options for course selections.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".