Data Sheet 1_Exploring cognitive load in anatomy education: a study of 3D virtual reality

A study of 21 first-year medical students learning cardiac anatomy, comparing traditional 2D materials with immersive 3D virtual reality. Prefrontal hemodynamic activity was monitored with functional near-infrared spectroscopy (fNIRS), and cognitive load was assessed via the Klepsch questionnaire and pre-/post-learning tests. The dataset, authored by Erika Johannessen and last updated on 2026-05-05, demonstrates the feasibility of fNIRS-based cognitive load classification.

Use Cases

• Training machine learning classifiers to detect cognitive load levels based on fNIRS signal features mentioned in the description.
• Comparing neural dynamics, such as time-to-peak oxygenation, between 2D and 3D VR learning modalities.
• Correlating subjective questionnaire scores for intrinsic, extraneous, and germane load with objective neuroimaging data.
• Benchmarking knowledge gain outcomes against physiological and self-reported measures of cognitive load.

Strengths

• Includes multimodal data: fNIRS neuroimaging, cognitive load questionnaires, and pre-/post-learning assessment scores.
• Reports strong classifier performance for extraneous cognitive load, with 92% accuracy and 91% F1-score under leave-one-subject-out validation.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small in size (12.0 KB), indicating a limited scope from a single study with 21 participants.

Provenance

Source

figshare, authored by Erika Johannessen.

Collection Method

Data collected from a controlled study where 21 medical students were randomly assigned to 2D or 3D VR learning groups, with fNIRS monitoring and questionnaire administration.

Freshness

Last updated 2026-05-05 05:28:24; freshness should be verified.