The Impact of Spatial Ability on Learning with a 3D Stereoscopic Model of the Mediastinum
Bibliographic record
Abstract
Three‐dimensional (3D) stereoscopic anatomy models have shown success in demonstrating complex spatial anatomical relationships. However, there is conflicting evidence as to whether or not such models are beneficial for students with low spatial visualization ability (Vz). A previous study suggests that students with low Vz may be hindered by the complex spatial cues provided by a 3D stereoscopic resource compared to students with high Vz. This study aimed to investigate the educational effectiveness of a digital 3D stereoscopic model of the human mediastinum, compared to a cadaveric specimen, for novice students in a biomedical masters‐level graduate program. In addition, this study aimed to further examine the effect of Vz on learning with a 3D stereoscopic anatomy model. We hypothesized that participants learning with the 3D stereoscopic model of the mediastinum would improve performance overall on an anatomy knowledge post‐test more than participants learning with a cadaveric specimen, and that high Vz participants would benefit more from the use of the 3D stereoscopic model compared to their low Vz counterparts. In addition, we hypothesized that participants learning with the 3D model of the mediastinum would rate the model more favorably on a post‐session survey compared to those learning with a cadaveric specimen. Participants were recruited from the Master of Medical Science (MMS) program at Lake Erie College of Osteopathic Medicine (LECOM), prior to the start of their Gross Anatomy course. Participants completed a demographic survey and Mental Rotations Test (MRT) pretest to determine their level of Vz. Participants were then assigned to either a 3D Group or a Cadaver Group, normalized for MRT score and sex. Each group was invited to a brief learning session using either a 3D stereoscopic model or cadaveric specimen of the mediastinum respectively. The 3D model was developed at LECOM using Amira ® software, and projected stereoscopically using a dual projection system. All participants completed a pre‐ and post‐test in each session, consisting of spatially‐oriented multiple choice questions pertaining to anatomy of the mediastinum. Student satisfaction with either resource was also examined using a qualitative survey. Data collection and analysis will be completed in Spring 2020. Results of this study should be considered when developing and implementing anatomical resources for health professional education, to maximize learning for students of varying Vz. Support or Funding Information Funded by the Lake Erie Consortium for Osteopathic Medical Training and the LECOM Internal Seed Grant
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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 | 0.000 | 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 | 0.001 |
| 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".