A Framework for Applying Quantified Self Approaches to Support Reflective Learning.


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Slides of my presentation at the IADIS Mobile Conference 2012, March 2012, Berlin.
We present a framework to combine Quantified Self approaches with Reflective Learning at work, as part of the work conducted in the EU Project MIRROR.

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A Framework for Applying Quantified Self Approaches to Support Reflective Learning.

  1. 1. A Framework for applying Quantified Self approaches to support Reflective Learning INFORMATIK FZI FORSCHUNGSZENTRUMV. Rivera-Pelayo, V. Zacharias, L. Müller, and S. BraunFZI Research Center for Information Technologies, Karlsruhe, GermanyIADIS Mobile Learning Conference 2012 – Berlin, Germany12th March 2012
  2. 2. Agenda Introduction Background  Theoretical: Reflective Learning  Pragmatical: The Quantified Self A Framework to Apply QS Approaches to support Reflective Learning  Tracking Cues  Triggering  Recalling and Revisiting Experiences Exemplary Application: Moodscope Conclusions12.03.2012 © FZI Forschungszentrum Informatik 2
  3. 3. Reflective Learning at Work Learn by observing others and from experiences Support learning-on-the-job and experience sharing Learning by reflection on observed practices and collected data Focus on acquisition of tacit knowledge 3
  4. 4. Introduction How can Quantified Self tools aid Reflective Learning at work? “I want to treat my patients better.” “I need to reduce my stress.” “I would like to improve my communication and teaching skills.”12.03.2012 © FZI Forschungszentrum Informatik 4
  5. 5. Reflective Learning  Returning to and evaluating past work performances and personal experiences in order to promote continuous learning and improve future experiences.D. Boud, R. Keogh, and D. Walker. Reflection: Turning Experience into Learning, chapter Promoting Reflection in Learning: aModel., pages 18-40. Routledge Falmer, New York, 1985. 12.03.2012 © FZI Forschungszentrum Informatik 5
  6. 6. The Quantified Self  Quantified Self (QS)  Collaboration of users and tool makers  Self-knowledge through self-tracking  Tools to collect personally relevant information  Self-reflection and self-monitoring  Gaining self-knowledge about one„s experiences, behaviors, habits and thoughtsThe Quantified Self. http://quantifiedself.com 12.03.2012 © FZI Forschungszentrum Informatik 6
  7. 7. Quantified Self Examples12.03.2012 © FZI Forschungszentrum Informatik 7
  8. 8. A Framework to Apply QS Approaches to supportReflective Learning E Theory: Cognitive process Tools: Experimentation EModel analysis Survey ofand information several QS needs tools 12.03.2012 © FZI Forschungszentrum Informatik 8
  9. 9. A Framework to Apply QS Approaches to supportReflective Learning12.03.2012 © FZI Forschungszentrum Informatik 9
  10. 10. Tracking Cues12.03.2012 © FZI Forschungszentrum Informatik 10
  11. 11. Tracking Cues Tracking means  Software sensors: applications – experiences not directly measurable  Hardware sensors: devices – automatic capture  environmental & physiological Tracked aspects/object  Emotional aspects: mood, stress, interest, anxiety.  Private and work data: photos, browsers history, music.  Physiological data: physical activity and health.  General activity: #cigarettes, cups of coffee, hours spent in a certain activity. Purposes  the goal which the user tries to achieve by using it.12.03.2012 © FZI Forschungszentrum Informatik 11
  12. 12. Triggering12.03.2012 © FZI Forschungszentrum Informatik 12
  13. 13. Triggering Active  Notification or catching of the user‟s attention explicitly. Passive  No identification of experiences or no active contact to the user.12.03.2012 © FZI Forschungszentrum Informatik 13
  14. 14. Recalling and Revisiting Experiences12.03.2012 © FZI Forschungszentrum Informatik 14
  15. 15. Recalling and Revisiting Experiences (I) Contextualizing  Social Context  relationship and comparison to others  Spacial Context  Location in terms of city, street, room…  Historical Context  Evolution of the data in time  Item Metadata  Extra information and meaning  Context from other datasets  Weather, work schedules...12.03.2012 © FZI Forschungszentrum Informatik 15
  16. 16. Recalling and Revisiting Experiences (II) Data fusion Objective Self Peer Group Data analysis: Aggregation, Averages, etc. Visualization12.03.2012 © FZI Forschungszentrum Informatik 16
  17. 17. Exemplary Application http://www.moodscope.com12.03.2012 © FZI Forschungszentrum Informatik 17
  18. 18. Exemplary Application: Moodscope  “The Hawthorne Effect”  passive and active triggering web-based application  timeline graph & historical context emotional aspects  contextualization: notes being happier and  min., max. and avg. of the moods thereby feeling better  no comparison with others12.03.2012 © FZI Forschungszentrum Informatik 18
  19. 19. Related Work  Few related work on QS approaches towards reflection  Li et al. [1,2]  HCI design perspective  Stage-based Model of Personal Informatics  Physical activity (sport and diseases)  IMPACT System  Fleck and Fitzpatrick [3]  Psychological perspective  Design landscape and guiding questions  SenseCam – passive image capture[1] I. Li, A. Dey, and J. Forlizzi. A stage-based Model of Personal Informatics Systems. In Proceedings of the 28th international conference onHuman Factors in computing systems, CHI 10, pages 557-566, New York, NY, USA, 2010. ACM.[2] I. Li, A. K. Dey, and J. Forlizzi. Understanding my Data, Myself: Supporting Self-reflection with Ubicomp Technologies. In Proceedings ofthe 13th international conference on Ubiquitous computing, UbiComp 11, pages 405-414, New York, NY, USA, 2011. ACM.[3] R. Fleck and G. Fitzpatrick. Reflecting on reflection: framing a design landscape. In Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction, OZCHI 10, pages 216-223, NewYork,12.03.2012 2010. ACM. NY, USA, © FZI Forschungszentrum Informatik 19
  20. 20. Discussion Which properties of QS applications make them more or less useful. Understanding on how to identify the situations. Which are the right aspects to track. Spread these tools among more users. QS approaches Awareness Analysis of augmentation data Quantification Rich source of abstract of data measures Learning processes12.03.2012 © FZI Forschungszentrum Informatik 20
  21. 21. Conclusions A framework for the application of QS tools to support reflective learning Structured review of this strand of research Understand the design space of QS tools for reflective learning Understanding which parts have not been addressed by research Learning in daily life Design and Validate the framework to implementation of new support reflective learning QS tools12.03.2012 © FZI Forschungszentrum Informatik 21
  22. 22. THANK YOU!12.03.2012 © FZI Forschungszentrum Informatik 22