How distributed settings affect individuals’ cognitive performance and learning is not known. We propose to identify, formalize, and test innovative learning analytics, indicators, and methods to determine and assess cognitive load, degrees of engagement, and well-being as they relate to global learning context.
PBL Lab Stanford University will use as a STEM test-bed the "Architecture, Engineering, Construction (AEC) Global Teamwork" course that engages university partners worldwide from the Americas, Europe and Asia.
The project uses a longitudinal research design and will generate evidence that is descriptive [design research, ethnography, observational], associative/correlational [quasi-experimental], causal [experimental, data mining methods for causal and behavioral patterns], and synthetic [meta-analysis]. Original data are being collected using diaries, observation [personal observation, videography, Web logs], survey research [self-completion questionnaire, semi-structured or informal interviews], and physiological measurements as well as self-report data.
Instruments or measures being used include: (1) accelerometer measurements to capture and assess sleep patterns; (2) heart-rate variability (HRV) monitoring to capture the participants’ stress reactions, recovery and sleep-wake patterns; (3) Brainwave Engagement Sensor to record raw EEG brainwave data and measure the level of “attention” indicating the degree of mental focus and alertness (Beta waves), “meditation” indicating the level of a participant’s mental calmness (Alpha waves); (4) cognitive learning memory tests; and (5) well-being surveys. The project plans to analyze data using video protocol analysis, statistical data analysis and visualization methods, and data mining.
Findings will be posted as they become available.
Learning analytics, indicators, and methods to determine and assess cognitive load, degrees of engagement, and well-being.