Expanding the scope of learning analytics data

Abstract

The ability to pay attention and self-regulate is a fundamental skill required of learners of all ages. Learning analytics researchers have to date relied on data generated by a computing system (such as a learning management system, click stream or log data) to examine learners' self-regulatory abilities. The development of wearable computing through fitness trackers, watches, heart rate monitors, and clinical grade devices such as Empatica's E4 wristband now provides researchers with access to biometric data as students interact with learning content or software systems. This level of data collection promises to provide valuable insight into cognitive and affective experiences of individuals, especially when combined with traditional learning analytics data sources. Our study details the use of wearable technologies to assess the relationship between heart rate variability and the self-regulatory abilities of an individual. This is relevant for the field of learning analytics as methods become more complex and the assessment of learner performance becomes more nuanced and attentive to the affective factors that contribute to learner success.