Design and Application of Quantified Self Approaches for Reflective Learning in the Workplace.

Learning by reflection has been identified as one of the core processes for improving work performance. However, theories of reflective learning are of a cognitive or sociological nature and do not sufficiently consider the use of technologies to enhance reflective learning processes. The aim of this thesis is to investigate if and how Quantified Self approaches can aid reflective learning at the workplace. Quantified Self (QS) is a collaboration of users and tool makers who share an interest in self-knowledge through self-tracking, resulting in a variety of tools to collect personally relevant information. These are rather experimental approaches and currently there is no unifying framework that clusters and connects these many emergent tools with the goals and benefits of their use. From a theoretical perspective, this thesis contributes with an integrated model that provides a framework for technical support of reflective learning, derived from unifying reflective learning theory with a conceptual framework of Quantified Self tools. To instantiate our approach, mobile and web-based applications have been iteratively designed and developed following a design-based research methodology. Within the first use case of the empirical validation, we introduce and evaluate mood self-tracking in distinct work scenarios of the telecommunications and IT sector. The second use case explores the impact of reflection on trading behavior by integrating mood self-tracking in experimental asset markets. In the third use case, an application to track feedback from audiences enables reflective learning support for lecturers and public speakers. These applications have been evaluated in thirteen studies that demonstrate the support of reflective learning and measure the impact on work performance. The results of these user studies lead to the validation of the holistic approach through a body of empirical application-oriented insights. This thesis provides a novel approach for reflective learning support by transferring and adapting practices from the Quantified Self to workplace settings.