Evaluation of a novel online systemic human anatomy course
Bibliographic record
Abstract
An online section of an existing undergraduate systemic human anatomy course with a laboratory component is in its inaugural year. Lectures for face‐to‐face (F2F) students are broadcast in live and archived format to online students using virtual classroom software (Blackboard Collaborate). Laboratory demonstrations are delivered online by a teaching assistant who can manipulate 3D anatomical models in the virtual classroom for the students to see. Students may independently study the 3D models on their own computers. A mixed methods approach is being undertaken to determine the effectiveness of the online delivery format. Means for each measure of student performance (4 tests, 24 laboratory quizzes) are under comparison between the sections. Incoming grade point averages will be compared to final Anatomy grades to determine if previous academic performance impacts performance in the F2F or online formats. Preliminary data suggest that student academic performance in the course is not impacted by the online delivery format. Student perceptions of the learning experiences in the online and F2F environments will be revealed. Student interviews will be conducted following a cross over that exposes them to both delivery formats. Themes will be established from interview data and used to generate data‐driven (grounded) theory about the strengths and weaknesses of the online format. Grant Funding Source : Departmental Funding
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".