Cognitive Load and fNIRS Data from a VR vs. 2D Anatomy Learning Study

A study of 21 first-year medical students learning cardiac anatomy, comparing immersive 3D virtual reality to traditional 2D materials. The dataset includes pre- and post-learning assessment scores, subjective cognitive load questionnaire results, and functional near-infrared spectroscopy (fNIRS) neuroimaging data. It was authored by Erika Johannessen and last updated on 2026-05-05.

Use Cases

• Training machine learning classifiers to detect cognitive load levels based on fNIRS features.
• Comparing neural dynamics and learning outcomes between VR and 2D educational modalities.
• Analyzing the relationship between subjective questionnaire scores and objective neuroimaging measures of cognitive load.

Strengths

• Includes multimodal data: fNIRS neuroimaging, cognitive load questionnaires, and pre/post knowledge assessments.
• Dataset is openly licensed under CC-BY-4.0, facilitating reuse.
• Study design includes a controlled comparison between two learning modalities (VR vs. 2D) with 21 participants.

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 (13.2 KB), indicating a limited scope.

Provenance

Source

figshare

Collection Method

Data collected from a controlled experiment with 21 medical students randomly assigned to VR or 2D learning groups.

Freshness

Last updated 2026-05-05 05:28:29; freshness should be verified.