Context becomes content: Sensor data for computer-supported reflective learning

Abstract

Wearable devices and ambient sensors can monitor a growing number of aspects of daily life and work. We propose to use this context data as content for learning applications in workplace settings to enable employees to reflect on experiences from their work. Learning by reflection is essential for today's dynamic work environments, as employees have to adapt their behavior according to their experiences. Building on research on computer-supported reflective learning as well as persuasive technology, and inspired by the Quantified Self community, we present an approach to the design of tools supporting reflective learning at work by turning context information collected through sensors into learning content. The proposed approach has been implemented and evaluated with care staff in a care home and voluntary crisis workers. In both domains, tailored wearable sensors were designed and evaluated. The evaluations show that participants learned by reflecting on their work experiences based on their recorded context. The results highlight the potential of sensors to support learning from context data itself and outline lessons learned for the design of sensor-based capturing methods for reflective learning.