METHOD: The review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis - Scoping Review extension. Two authors extracted data items from 45 eligible studies.

RESULTS: Three research aims were identified in previous studies: examining the effects of learning-related factors on attention captured by portable EEG (n = 23), developing attention classification algorithms (n = 7) and software for monitoring and promoting attention (n = 10), and verifying the signal quality of EEG derived from portable EEG in attentional tasks (n = 5). The testing sites and tasks were predominantly out-of-lab controlled settings and structured learning materials. To quantify attention, 8 studies employed a theory-driven approach, e.g., using EEG measures based on prior research correlating specific spectral power with attention. In contrast, 37 studies used data-driven approaches, e.g., using spectral power as input features for machine learning models to index attention.