The Cognitive Load of Learning Anatomy in a Virtual World
Bibliographic record
Abstract
Introduction Cognitive load refers to the amount of working memory that is being used in a task, like memorizing the anatomical landmarks on distinct boney specimens. Critically, cognitive load may be compromised when the load imposed by the environment and the content to be learned together exceeds a student’s capacity. Previous research shows that stereoscopic materials delivered in virtual reality (VR) can be more mentally taxing compared to desktop (i.e., two dimensional) delivery but may be similar to that encountered in real life. There is no data on the cognitive load of autostereoscopic displays. Given the increased reliance upon digital media for teaching in learning in anatomy classrooms, it is prudent to better understand the cognitive load imposed on learners across a variety of modalities employed. Methods Cognitive load will be compared across three different learning modalities: immersive virtual reality (VR, displayed on the Oculus Quest 2 TM ), autostereoscopic (displayed on the Alioscopy TM screen), and an identical printed, physical model. During a four‐minute learning phase, undergraduate students, with no prior formal anatomy education, will learn 10 anatomical landmarks on a displayed bony model (calcaneus, zygomatic bone, or hemipelvis) in each of the three modalities. A Stroop test will be administered as a secondary task throughout the learning phase to evaluate cognitive load. Stroop test reaction time, and accuracy of the participants' responses to the Stroop test will be recorded. After the learning phase, an untimed, recognition‐based test will be administered wherein participants will be asked to recall the ten landmarks learned with the aid of a 3D‐printed bone identical to the one used in the learning phase. Performance will be evaluated based on landmarks correctly identified and the results will be correlated with cognitive load measured in each learning modality. Results We hypothesize that the cognitive load will be highest for VR when compared to the cognitive load on the Alioscopy TM and physical model modalities which would manifest as lower reaction times and/or accuracy on the Stroop test. Further, we hypothesize that cognitive load will inversely correlate with the recognition test performance. Conclusion The results of this study will allow educators and students to make informed decisions when deciding which learning modalities should be used for anatomy education or any other education that requires nominative learning on complex objects. Understanding which modalities minimize cognitive load and improve learning will help improve outcomes and allow for more efficient anatomical education.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".