Cognitive Load in Anatomy Education Using VR and fNIRS

21 first-year medical students were randomly assigned to learn cardiac anatomy using either 2D materials or 3D VR while their prefrontal hemodynamic activity was monitored. The dataset likely contains fNIRS neural data and cognitive load questionnaire scores from three learning periods, collected by Erika Johannessen and last updated in 2026. Deep learning classifiers using fNIRS features achieved up to 92% accuracy in distinguishing cognitive load levels.

Use Cases

• Train classifiers to detect cognitive load levels based on fNIRS neural features.
• Compare neural dynamics between 2D and 3D VR learning modalities.
• Correlate subjective questionnaire scores with objective neurophysiological measures.
• Analyze time-to-peak oxygenation as a metric for cognitive processing differences.

Strengths

• Includes neurophysiological (fNIRS) and subjective (Klepsch questionnaire) measures for 21 participants.
• Reports strong classifier performance, with extraneous load detection achieving 92% accuracy.
• Data is shared under a permissive CC-BY-4.0 license.

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 very small at 11.3 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Experimental study with 21 first-year medical students randomly assigned to 2D or VR learning groups.

Freshness

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