Improving Online Interactions: Lessons from an Online Anatomy Course with a Laboratory for Undergraduate Students
Bibliographic record
Abstract
An online section of a face-to-face (F2F) undergraduate (bachelor's level) anatomy course with a prosection laboratory was offered in 2013-2014. Lectures for F2F students (353) were broadcast to online students (138) using Blackboard Collaborate (BBC) virtual classroom. Online laboratories were offered using BBC and three-dimensional (3D) anatomical computer models. This iteration of the course was modified from the previous year to improve online student-teacher and student-student interactions. Students were divided into laboratory groups that rotated through virtual breakout rooms, giving them the opportunity to interact with three instructors. The objectives were to assess student performance outcomes, perceptions of student-teacher and student-student interactions, methods of peer interaction, and helpfulness of the 3D computer models. Final grades were statistically identical between the online and F2F groups. There were strong, positive correlations between incoming grade average and final anatomy grade in both groups, suggesting prior academic performance, and not delivery format, predicts anatomy grades. Quantitative student perception surveys (273 F2F; 101 online) revealed that both groups agreed they were engaged by teachers, could interact socially with teachers and peers, and ask them questions in both the lecture and laboratory sessions, though agreement was significantly greater for the F2F students in most comparisons. The most common methods of peer communication were texting, Facebook, and meeting F2F. The perceived helpfulness of the 3D computer models improved from the previous year. While virtual breakout rooms can be used to adequately replace traditional prosection laboratories and improve interactions, they are not equivalent to F2F laboratories.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".