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.

Text of A Framework for Applying Quantified Self Approaches to Support Reflective Learning

  • 1. A Framework for applying Quantified Selfapproaches to support Reflective Learning INFORMATIK FZI FORSCHUNGSZENTRUMV. Rivera-Pelayo, V. Zacharias, L. Mller, and S. BraunFZI Research Center for Information Technologies, Karlsruhe, GermanyIADIS Mobile Learning Conference 2012 Berlin, Germany12th March 2012

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. 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 knowledge3 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. 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 Informatik5 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 ones experiences, behaviors, habits and thoughtsThe Quantified Self. http://quantifiedself.com12.03.2012 FZI Forschungszentrum Informatik6 7. Quantified Self Examples12.03.2012 FZI Forschungszentrum Informatik 7 8. A Framework to Apply QS Approaches to supportReflective LearningETheory: Cognitive processTools: ExperimentationEModel analysis Survey ofand information several QS needs tools 12.03.2012 FZI Forschungszentrum Informatik8 9. A Framework to Apply QS Approaches to supportReflective Learning12.03.2012 FZI Forschungszentrum Informatik 9 10. Tracking Cues12.03.2012 FZI Forschungszentrum Informatik 10 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. Triggering12.03.2012 FZI Forschungszentrum Informatik 12 13. Triggering Active Notification or catching of the users attention explicitly. Passive No identification of experiences or no active contact to the user.12.03.2012 FZI Forschungszentrum Informatik13 14. Recalling and Revisiting Experiences12.03.2012 FZI Forschungszentrum Informatik 14 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. Recalling and Revisiting Experiences (II) Data fusion Objective Self PeerGroup Data analysis: Aggregation, Averages, etc. Visualization12.03.2012 FZI Forschungszentrum Informatik 16 17. Exemplary Application http://www.moodscope.com12.03.2012 FZI Forschungszentrum Informatik 17 18. Exemplary Application: MoodscopeThe Hawthorne Effectpassive and active triggeringweb-based application timeline graph & historical contextemotional aspects contextualization: notesbeing happier and min., max. and avg. of the moods thereby feeling better no comparison with others12.03.2012 FZI Forschungszentrum Informatik 18 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 perspectiveDesign landscape and guiding questionsSenseCam 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 Informatik19 20. DiscussionWhich 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 ofaugmentation dataQuantification Rich source of abstract of data measuresLearning processes12.03.2012 FZI Forschungszentrum Informatik20 21. Conclusions A framework for the application of QS tools to support reflective learningStructured review of this strand of researchUnderstand the design space of QS tools for reflective learningUnderstanding which parts have not been addressed by researchLearning in daily lifeDesign andValidate the framework to implementation of newsupport reflective learning QS tools12.03.2012 FZI Forschungszentrum Informatik 21 22. THANK YOU!12.03.2012 FZI Forschungszentrum Informatik 22