Mixed methods evaluation of an online undergraduate systemic human anatomy course with laboratory (211.1)
Bibliographic record
Abstract
An online section of a Systemic Human Anatomy course was offered for the first time in 2012/13. Lectures for face‐to‐face (F2F) students (n=365) were broadcast in live and archived format to online students (n=40) using virtual classroom software (Blackboard Collaborate). Labs were delivered online by a teaching assistant who manipulated 3D computer models in the virtual classroom. A mixed methods approach is being used to determine the effectiveness of the online format. Student performance measures (4 tests, 24 lab quizzes) were statistically identical between sections. Incoming grade averages were strongly correlated to overall anatomy grade in both F2F (r = 0.70, p < 0.01) and online (r = 0.63, p < 0.01) sections, suggesting that prior academic performance, and not course format, predicts performance in anatomy. Interviews (22 online; 38 F2F students) and surveys (270 F2F students) regarding perceptions of the learning experience were conducted following a cross‐over period that exposed students to both formats. Survey results indicate that while students preferred online lectures (52%), F2F labs were preferred (85%). Online lectures gave students the benefit of reviewing archived sessions, while F2F labs allowed for better student‐teacher communication. A content analysis of interview transcripts is being undertaken to generate grounded theory about the strengths and weaknesses of the online course. Grant Funding Source : Departmental funding
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.005 | 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".