Weitere ähnliche Inhalte Ähnlich wie Doctoral Consortium: Applying Quantified Self Approaches to Support Reflective Learning (20) Kürzlich hochgeladen (20) Doctoral Consortium: Applying Quantified Self Approaches to Support Reflective Learning2. 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
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3. Setting the Scene
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„Returning to and evaluating past
work experiences…“
„… in order to learn and improve
future experiences“
4. Setting the Scene
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„Returning to and evaluating past
work experiences…“
„… in order to learn and improve
future experiences“
5. Setting the Scene
21.09.2012 © FZI Forschungszentrum Informatik 5
„Returning to and evaluating past
work experiences…“
„… in order to learn and improve
future experiences“
6. Setting the Scene
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„Returning to and evaluating past
work experiences…“
„… in order to learn and improve
future experiences“
7. 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.
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8. 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
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9. 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?
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10. 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
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11. E
Integrated Model of Reflective Learning and
Quantified Self
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Theory: Cognitive process
12. E
Integrated Model of Reflective Learning and
Quantified Self
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Theory: Cognitive process Tools: Experimentation
13. E
E
Integrated Model of Reflective Learning and
Quantified Self
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Theory: Cognitive process Tools: Experimentation
14. Integrated Model of Reflective Learning and
Quantified Self
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[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
17. Recalling and Revisiting Experiences
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Reflective ProcessExperience(s) Outcomes
18. 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
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19. LIM App: Live Interest Meter
First Prototype
Refinements for second prototype
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20. Use Case 2: reflecting on Mood
Telecommunications company
Capturing mood
Weekly virtual meetings
Lack of non-verbal communication
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22. MoodMap App
Reviewing mood evolution
Comparing own values with the
rest of the team
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23. 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)
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24. 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
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TEL
HCI
LAIS
MT
25. Wrap Up: Stepping forward
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LIM App (UC1)
Evaluation and
Publication
MoodMap App
(UC2) Prototyping
and Studies
IMRLQS Model
extension
26. THANKS FOR YOUR ATTENTION!
Any questions?
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rivera@fzi.de
@veronicarp
vriverapelayo
27. 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
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28. 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
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Reflective Learning at Work
• EU funded project (IST-FP7) started in July 2010
• 15 Partners around Europe