Landmark Positioning on a Map: An Alternative Measure of Spatial Ability for Identifying Students Who May Benefit from Learning Gross Anatomy with Virtual Reality
Bibliographic record
Abstract
Research has shown an inconsistent relationship between spatial abilities and learning outcomes from virtual anatomical tools. Instructors must understand this relationship to select appropriate resources for diverse learners. To identify appropriate tests for measuring spatial ability and evaluate the effectiveness of virtual anatomical resources, this study compared 96 students’ visuospatial ability (measured using the Mental Rotation Task [MRT] and Landmark Position on a Map [LPM] tests) with learning outcomes from experimental anatomy sessions and undergraduate anatomical course examinations. During experimental sessions, students took a test after a brief instructional session using one virtual resource: a monoscopic resource (e.g., digital photographs or a rotatable three-dimensional [r3D] specimen) or a stereoscopic virtual reality (VR) specimen. A negative linear relationship was found between MRT scores and students in Session B using VR with controllers ( r = –.56 to –.29), and LPM scores and students using VR ( r = –.71 to .39) and r3D ( r = –.41 to .43). There was a positive linear relationship between MRT scores and all other resources ( r = .01 to .91), and course examination scores ( r = .25 to .42, p = .05). Although the results were inconsistent, correlations were found between spatial ability and outcomes using both the MRT and LPM. The LPM might be better suited for determining which learners would benefit from VR. The results suggest that monoscopic resources best support high spatial abilities and stereoscopic resources best support low spatial abilities. These findings support accounting for diverse learner visuospatial abilities when selecting resources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".