Renner Bettina, Wesiak Gudrun, Cress, U.
2015
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
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.
Pozzi, Francesca, Persico, Donatella, Fischer, Frank, Hofmann, Lena, Lindstaedt Stefanie , Cress, Ulrike, Rath Andreas S., Moskaliuk, Johannes, Weber, Nicolas, Kimmerle, Joachim, Devaurs Didier, Ney, Muriel, Gonçalves, Celso, Balacheff, Nicolas, Schwartz, Claudine, Bosson, Jean-Luc, Dillenbourg, Pierre, Jermann, Patrick, Zufferey, Guillaume, Brown, Elisabeth, Sharples, Mike, Windrum, Caroline, Specht, Marcus, Börner, Dirk, Glahn, Christian, Fiedler, Sebastian, Fisichella, Marco, Herder, Eelco, Marenzi, Ivana, Nejdl, Wolfgang, Kawese, Ricardo, Papadakis, George
2010
In this first STELLAR trend report we survey the more distant future of TEL, as reflected in the roadmaps; we compare the visions with trends in TEL research and TEL practice. This generic overview is complemented by a number of small-scale studies, which focus on a specific technology, approach or pedagogical model.