Virtual reality and brain anatomy: a randomised trial of e‐learning instructional designs
Bibliographic record
Abstract
CONTEXT: Computer-aided instruction is used increasingly in medical education and anatomy instruction with limited research evidence to guide its design and deployment. OBJECTIVES: To determine the effects of (a) learner control over the e-learning environment and (b) key views of the brain versus multiple views in the learning of brain surface anatomy. DESIGN: Randomised trial with 2 phases of study. Participants Volunteer sample of 1st-year psychology students (phase 1, n = 120; phase 2, n = 120). Interventions Phase 1: computer-based instruction in brain surface anatomy with 4 conditions: (1) learner control/multiple views (LMV); (2) learner control/key views (LKV); (3) programme control/multiple views (PMV); (4) programme control/key views (PKV). Phase 2: 2 conditions: low learner control/key views (PKV) versus no learner control/key views (SKV). All participants performed a pre-test, post-test and test of visuospatial ability. MAIN OUTCOME MEASURES: A 30-item post-test of brain surface anatomy structure identification. RESULTS: The PKV group attained the best post-test score (57.7%) and the PMV group received the worst (42.2%), with the 2 high learner control groups performing in between. For students with low spatial ability, estimated scores are 20% lower for those who saw multiple views during learning. In phase 2, students with the most static condition and no learner control (SKV) performed similarly to those students in the PKV group. CONCLUSIONS: Multiple views may impede learning, particularly for those with relatively poor spatial ability. High degrees of learner control may reduce effectiveness of learning.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".