Doctoral Consortium: Applying Quantified Self Approaches to Support Reflective Learning

FZIFORSCHUNGSZENTRUM
INFORMATIK
Applying Quantified Self Approaches to
Support Reflective Learning
Verónica Rivera-Pelayo
FZI Research Center for Information Technology, Germany
21 September 2012

Agenda
 Setting the Scene
 Background: Reflective Learning and The Quantified Self
 Research Questions and Approach
 Integrated Model
 Use Cases
 LIM App
 MoodMap App
 Wrap up
21.09.2012 © FZI Forschungszentrum Informatik 2

Setting the Scene
„Returning to and evaluating past
work experiences…“
„… in order to learn and improve
future experiences“

Setting the Scene

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.
Reflective Learning
 Returning to and evaluating past work performances and personal
experiences in order to promote continuous learning and improve
future experiences.

The Quantified Self. http://quantifiedself.com
The Quantified Self
 Quantified Self (QS)
 Collaboration of users and tool makers
 Self-knowledge through self-tracking
 Gaining self-knowledge about one‘s experiences, behaviors, habits and
thoughts

Research Questions
 Can Quantified Self aid Learning at work?
 How can Quantified Self principles and tools support reflective learning
at work scenarios?
 Can the use of QS tools capture data about our daily work-activities,
which can be relevant for reflective learning?
 What different mechanisms can we use for visualizing the data to foster
learning processes and motivate users to track data about themselves?
 Is this information useful to learn from our own experiences and
improve our work? e.g. Can users get to feel better, suffer less
emotional load and perform their work better?

Research Approach
 Theory Model
 Unification of QS approaches and Reflective Learning
 Identification of support dimensions
 2 Use Cases
 Identification of a work context and its requirements
 Development of an application (user-centered)
 Interaction design and motivational techniques
 Incremental refinement
 Evaluation in real contexts
 Summative Evaluation
 Success of the application for learning purposes
 End-user benefits

E
Integrated Model of Reflective Learning and
Quantified Self
Theory: Cognitive process

E
Quantified Self
Theory: Cognitive process Tools: Experimentation

E
E
Quantified Self
Theory: Cognitive process Tools: Experimentation

Quantified Self
[1] Applying Quantified Self Approaches to Support Reflective Learning. Verónica Rivera-Pelayo, Valentin Zacharias, Lars Müller,
Simone Braun. Learning Analytics and Knowledge 2012 (LAK 2012), Vancouver, Canada
[2] A Framework for Applying Quantified Self Approaches to Support Reflective Learning. Verónica Rivera-Pelayo, Valentin
Zacharias, Lars Müller, Simone Braun. IADIS International Conference on Mobile Learning (Mlearning 2012), Berlin, Germany
Reflective ProcessExperience(s) Outcomes

Tracking Cues

Triggering

Recalling and Revisiting Experiences

Use Case 1: reflecting on Feedback
 Academic and research context
 Capturing of feedback
 Support researchers, professors, lecturers, students
 Lectures and conferences
 In-action & On-action

LIM App: Live Interest Meter
 First Prototype
 Refinements for second prototype

Use Case 2: reflecting on Mood
 Telecommunications company
 Capturing mood
 Weekly virtual meetings
 Lack of non-verbal communication

MoodMap App

MoodMap App
 Reviewing mood evolution
 Comparing own values with the
rest of the team

MoodMap App: 3 scenarios for 2nd Prototype
 IT Company – Netherlands
 Project meetings (whole project and comparison with documents)
 Feedback and support of the RL process (triggering)
 Call takers at Emergency Service – Italy
 Contextualization
 Market Experiment – Germany
 Evaluation of benefit of RL (better negotiation/trading, avoiding bubbles)

Wrap Up: My contribution
 Integrated model to inform technological support of reflective
learning at work
 New tools to support learners in different work environments
 HCI perspective
 Quantification and analysis of abstract data
 Visualization of affective data and its context
 Interaction design for mobile applications
 Techniques for user’s motivation
TEL
HCI
LAIS
MT

Wrap Up: Stepping forward
LIM App (UC1)
Evaluation and
Publication
MoodMap App
(UC2) Prototyping
and Studies
IMRLQS Model
extension

THANKS FOR YOUR ATTENTION!
Any questions?
rivera@fzi.de
@veronicarp
vriverapelayo

About me…
 M.Sc. Degree in Informatics Engineering
 FIB, UPC-BARCELONA TECH
 Research Student Assistant at SAP AG Research Centre, Karlsruhe
 Research scientist at FZI (Information Process Engineering)
 PhD student of Prof. Rudi Studer at AIFB, Karlsruhe Institute of
Technology
 EU FP7 Project MIRROR, Reflective Learning at Work

 Individuals learn most 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
28
Reflective Learning at Work
• EU funded project (IST-FP7) started in July 2010
• 15 Partners around Europe

Doctoral Consortium: Applying Quantified Self Approaches to Support Reflective Learning

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