doctoral consortium: applying quantified self approaches to support reflective learning
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FZI F
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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
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Setting the Scene
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„Returning to and evaluating past work experiences…“
„… in order to learn and improve future experiences“
Setting the Scene
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„Returning to and evaluating past work experiences…“
„… in order to learn and improve future experiences“
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“
Setting the Scene
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„Returning to and evaluating past work experiences…“
„… in order to learn 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.
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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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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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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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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E
Integrated Model of Reflective Learning and Quantified Self
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Theory: Cognitive process
E
Integrated Model of Reflective Learning and Quantified Self
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Theory: Cognitive process Tools: Experimentation
E
E
Integrated Model of Reflective Learning and Quantified Self
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Theory: Cognitive process Tools: Experimentation
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 Process Experience(s) Outcomes
Tracking Cues
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Reflective Process Experience(s) Outcomes
Triggering
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Reflective Process Experience(s) Outcomes
Recalling and Revisiting Experiences
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Reflective Process Experience(s) Outcomes
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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LIM App: Live Interest Meter
First Prototype Refinements for second prototype
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Use Case 2: reflecting on Mood
Telecommunications company Capturing mood Weekly virtual meetings Lack of non-verbal communication
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MoodMap App
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MoodMap App
Reviewing mood evolution Comparing own values with the
rest of the team
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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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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
LA IS
MT
Wrap Up: Stepping forward
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LIM App (UC1) Evaluation and
Publication
MoodMap App (UC2) Prototyping
and Studies
IMRLQS Model extension
THANKS FOR YOUR ATTENTION! Any questions?
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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
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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
The MIRROR Solution
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