Renner Bettina, Wesiak Gudrun, Cress, U.
2015
Quantified self app usage tested in the workplace.
17th congress of the European Association of Work and Organizational Psychology (EAWOP)
Purpose: This contribution relates the Quantified Self approach to computer supported
workplace learning. It shows results of a large field study where 12 different apps where used in
several work contexts. Design/Methodology: Participants used the apps during their work and during training sessions
to track their behaviour and mood at work and capture problematic experiences. Data capturing
was either automatically, e.g. tracking program usage on a computer, or by participants
manually documenting their experiences. Users then reflected individually or collaboratively
about their experiences. Results: Results show that participants liked the apps and used the opportunity to learn
something from their work experiences. Users evaluated apps as useful for professional training
and having long-term benefits when used in the work life. Computer supported reflection about
own data and experiences seems to have especially potential where new processes happen, e.g.
with unexperienced workers or in training settings.
Limitations: Apps were used in the wild so control about potential external influencing factors is
limited. Research/Practical Implications: Results show a successful application of apps supporting
individual learning in the work life. This shows that the concept of Quantified Self is not
limited to private life but also has chances to foster vocational development. Originality/Value: This contribution combines the pragmatic Quantified Self approach with the
theoretical background of reflective learning. It presents data from a broad-based study of using
such apps in real work life. The results of the study give insights about its potential in this area
and about possible influencing factors and barriers.
Moskaliuk, J., Rath, A.S., Devaurs, D., Weber, N., Lindstaedt Stefanie , Kimmerle, J., Cress, U.
2011
Automatic detection of accommodation steps as an indicator of knowledge maturing (in-press)
Interacting with computers: the interdisciplinary journal of human-computer interaction Murray, D. M. Elsevier B.V.
Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed.