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Applying quantified self approaches to support reflective learning

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Published:29 April 2012Publication History

ABSTRACT

This paper presents a framework for technical support of reflective learning, derived from a unification of reflective learning theory with a conceptual framework of Quantified Self tools -- tools for collecting personally relevant information for gaining self-knowledge. Reflective learning means returning to and evaluating past experiences in order to promote continuous learning and improve future experiences. Whilst the reflective learning theories do not sufficiently consider technical support, Quantified Self (QS) approaches are rather experimental and the many emergent tools are disconnected from the goals and benefits of their use. This paper brings these two strands into one unified framework that shows how QS approaches can support reflective learning processes on the one hand and how reflective learning can inform the design of new QS tools for informal learning purposes on the other hand.

References

  1. D. Boud, R. Keogh, and D. Walker. Reflection: Turning Experience into Learning, chapter Promoting Reflection in Learning: a Model., pages 18--40. Routledge Falmer, New York, 1985.Google ScholarGoogle Scholar
  2. J. Brophy-Warren. The New Examined Life. http://online.wsj.com/article/SB122852285532784401.html, Dec. 2008.Google ScholarGoogle Scholar
  3. J. Dewey. Experience and Education. Macmillan, London & New York, 1938.Google ScholarGoogle Scholar
  4. A. K. Dey. Understanding and using context. Personal and Ubiquitous Computing, 5:4--7, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. L. Festinger. A theory of cognitive dissonance. Stanford Univ. Press, 1957.Google ScholarGoogle ScholarCross RefCross Ref
  6. R. Fleck. Supporting reflection on experience with sensecam. In CHI Workshop on Designing for Reflection on Experience, 2009.Google ScholarGoogle Scholar
  7. D. A. Kolb. Experiential Learning: Experience as the source of learning and development. Englewood Cliffs, N. J.: Prentice Hall, 1984.Google ScholarGoogle Scholar
  8. B. Krogstie and M. Divitini. Shared timeline and individual experience: Supporting retrospective reflection in student software engineering teams. In Proc. of CESEET 2009, pages 85--92, Washington, DC, USA, 2009. IEEE Computer Society. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. I. Li, A. Dey, and J. Forlizzi. A stage-based model of personal informatics systems. In Proc. of CHI 2010, pages 557--566, New York, NY, USA, 2010. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. L. Müller, B. Krogstie, and A. Schmidt. Towards capturing learning experiences. In ConTEL: Theory, methodology and design, ECTEL 2011, 2011.Google ScholarGoogle Scholar
  11. L. Müller, V. Rivera-Pelayo, and A. Schmidt. MIRROR D3.1 - User studies, requirements, and design studies for capturing learning experiences. MIRROR project deliverable D3.1, June 2011.Google ScholarGoogle Scholar
  12. D. A. Schön. Educating the Reflective Practitioner. Jossey-Bass, San Fransisco, 1 edition, 1987.Google ScholarGoogle Scholar
  13. K. Strampel and R. Oliver. Using technology to foster reflection in higher education. In Proc. of ascilite Singapore 2007, 2007.Google ScholarGoogle Scholar

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  1. Applying quantified self approaches to support reflective learning

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                cover image ACM Conferences
                LAK '12: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
                April 2012
                282 pages
                ISBN:9781450311113
                DOI:10.1145/2330601

                Copyright © 2012 ACM

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 29 April 2012

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