The role of spatial ability in mixed reality learning with the HoloLens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it