Optimizing Communication in an Online Systemic Anatomy Course with a Laboratory
Bibliographic record
Abstract
An online (OL) section of a face‐to‐face (F2F) anatomy course with a prosection lab commenced in 2012‐13. Lectures for F2F students were broadcast in live and archived format to OL students using virtual classroom software (Blackboard Collaborate). Labs were delivered OL by a teaching assistant (TA) who manipulated 3‐dimensional computer models in the virtual classroom. Analysis revealed that although course format was unrelated to grades, F2F labs were preferred as it was easier to communicate with the TA. The course was modified in 2013‐14 (138 OL, 354 F2F) to improve OL student‐teacher communication in lab: OL students were divided into lab groups that rotated through virtual breakout rooms, which decreased the student:teacher ratio and gave them the opportunity to communicate with 3 TAs. The study objectives were to compare perceptions of student‐teacher lab communications between OL (N = 101) and F2F (N = 273) survey participants, and determine if the delivery format would influence final anatomy grades. The majority of respondents agreed they were engaged in lab by TAs (60% OL; 82% F2F), could interact socially with TAs (71% OL; 91% F2F), and could ask TAs questions (75% OL; 94% F2F); however, these proportions were significantly higher in the F2F section (p < 0.001). Final grades were statistically identical between sections. There were strong, positive correlations between incoming grade average and final anatomy grade in both F2F (r = 0.71, p < 0.01) and OL (r = 0.70, p< 0.01) sections. These data suggest that prior academic performance, and not delivery format, predicts anatomy grades. While virtual breakout rooms can be used in OL anatomy labs to facilitate student‐teacher communication, they do not adequately replace the F2F lab environment.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".