The role of spatial ability in mixed reality learning with the HoloLens
Bibliographic record
Abstract
The use of mixed reality in science education has been increasing and as such it has become more important to understand how information is learned in these virtual environments. Spatial ability is important in many learning contexts, but especially in neuroanatomy education where learning the locations and spatial relationships between brain regions is paramount. It is currently unclear what role spatial ability plays in mixed reality learning environments, and whether it is different compared to traditional physical environments. To test this, a learning experiment was conducted where students learned neuroanatomy using both mixed reality and a physical plastic model of a brain (N = 27). Spatial ability was assessed and analyzed to determine its effect on performance across the two learning modalities. The results showed that spatial ability facilitated learning in mixed reality (β = 0.21, P = 0.003), but not when using a plastic model (β = 0.08, P = 0.318). A non-significant difference was observed between the modalities in terms of knowledge test performance (d = 0.39, P = 0.052); however, mixed reality was more engaging (d = 0.59, P = 0.005) and learners were more confident in the information they learned compared to using a physical model (d = 0.56, P = 0.007). Overall, these findings suggest that spatial ability is more relevant in virtual learning environments, where the ability to manipulate and interact with an object is diminished or abstracted through a virtual user interface.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".