Seeing in Stereo: Exploring the Relationship between 3D Anatomical Resources and Spatial Ability
Bibliographic record
Abstract
Three‐dimensional (3D) digital anatomical models have been shown to promote student engagement and satisfaction, while demonstrating complex spatial anatomical relationships. However, there is limited research to suggest that 3D digital anatomy models are more effective in improving spatial anatomy performance over traditional resources, particularly for students with varying spatial visualization ability (Vz). The present study aims to investigate the educational effectiveness of a digital 3D stereoscopic anatomy model compared to a 2D visualization and cadaveric specimen of the human pelvis. This study also aims to examine the relationship between Vz and learning from a 3D stereoscopic anatomy model of the human pelvis. We hypothesized that participants learning from the 3D stereoscopic model of the pelvis would improve performance overall on an anatomy knowledge post‐test more than participants learning from a 2D visualization or cadaveric specimen, and that low Vz participants would benefit more from the use of the 3D stereoscopic model compared to their high Vz counterparts. Participants were recruited from the Post‐Baccalaureate, Medical Year 1, and Medical Year 2 classes at Lake Erie College of Osteopathic Medicine (n=64). All participants completed a demographic survey and Mental Rotations Test (MRT) pretest to determine initial Vz. Participants were then sorted into three groups, normalized for MRT score, sex, and education level. Each group was invited to a brief learning session using a 3D stereoscopic model (n=21), 2D visualization (n=21), or cadaveric specimen of the pelvis (n=22) respectively. The 3D model, developed using Amira ® software, was projected stereoscopically using a dual projection system. All participants completed a pre‐ and post‐test in each session, consisting of 10 spatially‐oriented multiple choice questions pertaining to pelvis anatomy. Results show no significant difference in improvement from pre‐test to post‐test between the three groups. A linear regression analysis showed no significant correlations between MRT score and anatomy test improvement; however, Post‐Baccalaureate (novice) participants with high Vz tended to improve more after learning with the 3D model than Post‐Baccalaureate participants with low Vz. Participants with high Vz, irrespective of education level, improved more on a spatial anatomy test after learning with the 3D model compared to either of the other resources. Contrary to current literature, participants with low Vz appeared to benefit most from the 2D visualizations. Results of this study can be used to inform resource selection and curriculum design for health professional students, with special attention to the impact of Vz on learning. Support or Funding Information Funded by a LECOMT Research Support Grant. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".