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

A Framework for applying Quantified Self
approaches to support Reflective Learning

INFORMATIK
FZI FORSCHUNGSZENTRUM
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

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

12.03.2012 © FZI Forschungszentrum Informatik 2

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

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.”


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: a
Model., pages 18-40. Routledge Falmer, New York, 1985.

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

The Quantified Self. http://quantifiedself.com

Quantified Self Examples


A Framework to Apply QS Approaches to support
Reflective Learning

E

Theory: Cognitive process Tools: Experimentation

E
Model analysis Survey of
and information several QS
needs tools


A Framework to Apply QS Approaches to support
Reflective Learning


Tracking Cues


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.

Triggering


Triggering

 Active
 Notification or catching of the user‟s attention explicitly.

 Passive
 No identification of experiences or no active contact to the user.


Recalling and Revisiting Experiences


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...


Recalling and Revisiting Experiences (II)

 Data fusion

Objective Self

Peer Group

 Data analysis: Aggregation, Averages, etc.

 Visualization


Exemplary Application

http://www.moodscope.com

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 others


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 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,12.03.2012 2010. ACM.
NY, USA, © FZI Forschungszentrum Informatik 19

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
processes


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 tools


THANK YOU!


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

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