Perceptions of a mobile technology on learning strategies in the anatomy laboratory
Bibliographic record
Abstract
Mobile technologies offer new opportunities to improve dissection learning. This study examined the effect of using an iPad-based multimedia dissection manual during anatomy laboratory instruction on learner's perception of anatomy dissection activities and use of time. Three experimental dissection tables used iPads and three tables served as a control for two identical sessions. Trained, non-medical school anatomy faculty observers recorded use of resources at two-minute intervals for 20 observations per table. Students completed pre- and post-perception questionnaires. We used descriptive and inferential analyses. Twenty-one control and 22 experimental students participated. Compared with controls, experimental students reported significantly (P < 0.05) less reliance on paper and instructor resources, greater ability to achieve anatomy laboratory objectives, and clarity of the role of dissection in learning anatomy. Experimental students indicated that the iPad helped them in dissection. We observed experimental students more on task (93% vs. 83% of the time) and less likely to be seeking an instructor (2% vs. 32%). The groups received similar attention from instructors (33% vs. 37%). Fifty-nine percent of the time at least one student was looking at the iPad. Groups clustered around the iPad a third of their time. We conclude that the iPad-manual aided learner engagement, achieved instructional objectives, and enhanced the effectiveness and efficiency of dissection education.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".