Automated Physiological-Based Detection of Mind Wandering during Learning
Unintentional lapses of attention, or mind wandering, are ubiquitous and detrimental during learning. Hence, automated methods that detect and combat mind wandering might be beneficial to learning. As an initial step in this direction, we propose to detect mind wandering by monitoring physiological measures of skin conductance and skin temperature. We conducted a study in which student’s physiology signals were measured while they learned topics in research methods from instructional texts. Momentary self-reports of mind wandering were collected with standard probe-based methods. We computed features from the physiological signals in windows leading up to the probes and trained supervised classification models to detect mind wandering. We obtained a kappa, a measurement of accuracy corrected for random guessing, of .22, signaling feasibility of detecting MW in a student-independent manner. Though modest, we consider this result to be an important step towards fully-automated unobtrusive detection of mind wandering during learning.
Keywordsskin conductance skin temperature mind wandering machine learning
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