Download pdf - Doctoral Consortium: Applying Quantified Self Approaches to Support Reflective Learning

FZI F

ORSC

HUNG

SZEN

TRUM

IN

FORM

ATIK

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




Setting the Scene




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


http://quantifiedself.com/

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



Theory: Cognitive process Tools: Experimentation

E

E



Theory: Cognitive process Tools: Experimentation



[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



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

LA IS

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?


[email protected]

@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

The MIRROR Solution

29