Effect of teaching tools in spatial understanding in health science education: a systematic review
Bibliographic record
Abstract
Background: The concept of spatial orientation is integral to health education. Students studying to be healthcare professionals use their visual intelligence to develop 3D mental models from 2D images, like X-rays, MRI, and CT scans, which exerts a heavy cognitive load on them. Innovative teaching tools and technologies are being developed to improve students' learning experiences. However, the impact of these teaching modalities on spatial understanding is not often evaluated. This systematic review aims to investigate current literature to identify which teaching tools and techniques are intended to improve the 3D sense of students and how these tools impact learners' spatial understanding. Methods: The preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines were followed for the systematic review. Four databases were searched with multiple search terms. The articles were screened based on inclusion and exclusion criteria and assessed for quality. Results: Nineteen articles were eligible for our systematic review. Teaching tools focused on improving spatial concepts can be grouped into five categories. The review findings reveal that the experimental groups have performed equally well or significantly better in tests and tasks with access to the teaching tool than the control groups. Conclusion: Our review investigated the current literature to identify and categorize teaching tools shown to improve spatial understanding in healthcare professionals. The teaching tools identified in our review showed improvement in measured, and perceived spatial intelligence. However, a wide variation exists among the teaching tools and assessment techniques. We also identified knowledge gaps and future research opportunities.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".