Chapter 2 The Logic of Quantified Statements. Section 2.4 Arguments with Quantified Statements.
A Framework for Applying Quantified Self Approaches to Support Reflective Learning.
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Transcript of A Framework for Applying Quantified Self Approaches to Support Reflective Learning.
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A Framework for applying Quantified Self
approaches to support Reflective Learning
V. Rivera-Pelayo, V. Zacharias, L. Müller, and S. Braun
FZI Research Center for Information Technologies, Karlsruhe, Germany
IADIS Mobile Learning Conference 2012 – Berlin, Germany
12th March 2012
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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
Conclusions
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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
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Reflective Learning at Work
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Introduction
How can Quantified Self tools aid Reflective Learning at work?
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“I want to treat my patients better.”
“I need to reduce my stress.”
“I would like to improve my
communication and teaching skills.”
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Reflective Learning
Returning to and evaluating past work performances and personal
experiences in order to promote continuous learning and improve
future experiences.
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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.
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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
thoughts
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The Quantified Self. http://quantifiedself.com
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Quantified Self Examples
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E
E
A Framework to Apply QS Approaches to support
Reflective Learning
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Theory: Cognitive process Tools: Experimentation
Survey of
several QS
tools
Model analysis
and information
needs
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A Framework to Apply QS Approaches to support
Reflective Learning
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Tracking Cues
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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, browser's 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.
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Triggering
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Triggering
Active
Notification or catching of the user’s attention explicitly.
Passive
No identification of experiences or no active contact to the user.
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Recalling and Revisiting Experiences
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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...
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Recalling and Revisiting Experiences (II)
Data fusion
Data analysis: Aggregation, Averages, etc.
Visualization
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Objective Self
Peer Group
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Exemplary Application
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http://www.moodscope.com
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Exemplary Application: Moodscope
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web-based application
emotional aspects
being happier and
thereby feeling better
“The Hawthorne Effect”
passive and active triggering
timeline graph & historical context
contextualization: notes
min., max. and avg. of the moods
no comparison with others
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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
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[1] I. Li, A. Dey, and J. Forlizzi. A stage-based Model of Personal Informatics Systems. In Proceedings of the 28th international conference on
Human 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 of
the 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, New York, NY, USA,
2010. ACM.
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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
Discussion
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Learning processes
Rich source of data
Awareness augmentation
Analysis of data
Quantification of abstract measures
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Design and implementation of new
QS tools
Validate the framework to support reflective learning
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
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THANK YOU!
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