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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

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

09.05.2012 © FZI Forschungszentrum Informatik 2

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

Reflective Learning at Work

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

http://quantifiedself.com/

Quantified Self Examples


E

E

A Framework to Apply QS Approaches to support

Reflective Learning


Theory: Cognitive process Tools: Experimentation

Survey of

several QS

tools

Model analysis

and information

needs

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

Data analysis: Aggregation, Averages, etc.

Visualization


Objective Self

Peer Group

Exemplary Application


http://www.moodscope.com

Exemplary Application: Moodscope


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

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

09.05.2012 © FZI Forschungszentrum Informatik

[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


Learning processes

Rich source of data

Awareness augmentation

Analysis of data

Quantification of abstract measures

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


THANK YOU!
