The use of Hololens increases the engagement while reducing the cognitive load of senior medical students when overlaying medical imaging of body donors during the dissection
Bibliographic record
Abstract
Augmented reality (AR) has recently been implemented in medicine as an integrative way to view virtual objects while simultaneously interacting with the physical environment. The technology offers many benefits for education in fields that require interactive and visual learning activities, such as human anatomy. However, AR is a novel and very advanced technology, so justifying its use in the field or classroom necessitates extensive study of its effects on mental processes. Accordingly, the purpose of this study was to evaluate cognitive markers of students’ engagement and cognitive load while they used AR technology to overlay donor‐specific diagnostic imaging (DSDI) onto the corresponding body donors in a fourth‐year medical elective course at McGill University. Each participnt (n = 12) used DSDI on a head‐mounted Microsoft HoloLens and DSDI on an Apple iPad to examine the underlying anatomy of their assigned body donor before beginning their dissection. Participants wore portable five‐lead electroencephalographic (EEG) devices to collect cognitive processing data. Engagement (engagement index; EI) and cognitive load (theta‐alpha ratio; TAR) were compared between HoloLens and iPad use conditions. Mean EI under the HoloLens condition (0.499 ± 0.038) was significantly higher than the mean EI under the iPad condition (0.297 ± 0.037; P = 0.002) while the mean TAR under the HoloLens condition (1.508 ± 0.047) was significantly lower than that collected during the iPad trial (1.813 ± 0.071; P = 0.012). Together, these results indicate that use of the HoloLens to superimpose radiographic images onto a human cadaver during dissection is significantly more engaging than examining the same images on a 2D iPad screen, and also imposes a lesser cognitive load for the same task.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".