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Persuasion and Reflective Learning: Closing the Feedback Loop

  • Lars Müller
  • Verónica Rivera-Pelayo
  • Stephan Heuer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7284)

Abstract

Reflecting about past experiences can lead to new insights and changes in behavior that are similar to the goals of persuasive technology. This paper compares both research directions by examining the underlying feedback loops. Persuasive technology aims at reinforcing clearly defined behaviors to achieve measurable goals and therefore focuses on the optimal form of feedback to the user. Reflective learning aims at establishing goals and insights. Hence, the design of tools is mainly concerned with providing the right data to trigger a reflection process. In summary, both approaches differ mainly in the amount of guidance and this opens up a design space between reflective learning and persuasive computing. Both approaches may learn from each other and can use common capturing technologies. However, tools for reflective learning require additional concepts and cues to account for the unpredictability of relevance of captured data.

Keywords

Feedback Loop Learning Tool Acceleration Sensor Reflective Learning Computer Mediate Communication 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lars Müller
    • 1
  • Verónica Rivera-Pelayo
    • 1
  • Stephan Heuer
    • 1
  1. 1.FZI Research Center of Information TechnologyKarlsruheGermany

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