The Assessment of an Online Microscopic Anatomy Laboratory Course
Bibliographic record
Abstract
We have developed a senior level histology course with a laboratory component which is offered fully online. This course utilizes virtual microscopy and synchronous videoconferencing software technologies to allow a synchronous lecture and laboratory course. This online course is being compared to a face to face (F2F) course covering the same content. Initial data suggest that there is no significant difference between these methods for overall student outcomes. Closer examination of the components will determine if there is any difference in outcomes for the lab portion of the course. In addition, attendance levels and patterns will be examined for differences between the online and F2F groups. Demographic data collected will include incoming science and math GPA which will be correlated to the overall course grade. Finally, levels of student satisfaction will be compared for the courses. Many studies have compared online to F2F courses however, few were laboratory courses and none were synchronous courses where both the lectures and the laboratories were interactive. Results from this study will help to clarify any strengths or weaknesses of online laboratories. Grant Funding Source: Ontario Graduate Scholarship
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".