  This study investigated the neural dynamics associated with short-term
exposure to different virtual classroom designs with different window placement
and room dimension. Participants engaged in five brief cognitive tasks in each
design condition including the Stroop Test, the Digit Span Test, the Benton
Test, a Visual Memory Test, and an Arithmetic Test. Performance on the
cognitive tests and Electroencephalogram (EEG) data were analyzed by
contrasting various classroom design conditions. The cognitive-test-performance
results showed no significant differences related to the architectural design
features studied. We computed frequency band-power and connectivity EEG
features to identify neural patterns associated to environmental conditions. A
leave one out machine learning classification scheme was implemented to assess
the robustness of the EEG features, with the classification accuracy evaluation
of the trained model repeatedly performed against an unseen participant's data.
The classification results located consistent differences in the EEG features
across participants in the different classroom design conditions, with a
predictive power that was significantly higher compared to a baseline
classification learning outcome using scrambled data. These findings were most
robust during the Visual Memory Test, and were not found during the Stroop Test
and the Arithmetic Test. The most discriminative EEG features were observed in
bilateral occipital, parietal, and frontal regions in the theta and alpha
frequency bands. While the implications of these findings for student learning
are yet to be determined, this study provides rigorous evidence that brain
activity features during cognitive tasks are affected by the design elements of
window placement and room dimensions.
