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SYMPOSIUM
ENTRUSTMENT AND LEARNING ANALYTICS IN E-PORTFOLIOS FOR WORKPLACE LEARNING AND ASSESSMENT
PRESENTERS: WATCHME-TEAMDISCUSSANT: PROF. DR. DAVID BOUD, UNIVERSITY OF TECHNOLOGY,
SYDNEY, AUSTRALIA
www.project-watchme.eu
@Project_WatchMe
Workplace-based e-Assessment Technology for Competency-based Higher Multi-professional Education
This project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 619349
E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
1. Utrecht University, NL
2. University Medical Centre Utrecht, NL
3. Szent Istvan University, Hungary
4. University of Tartu, Estonia
5. Universitätsmedizin Charité Berlin, Germany
6. University of California San Francisco, USA
7. Maastricht University, NL
8. Mateum, NL
9. University of Reading, UK
10.Jayway, Denmark
11.NetRom, Romania/NL
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
WATCHME’S AIM
Improve efficiency and quality of workplace- based feedback and assessment by means of a mobile electronic portfolio system, that is enhanced:
Conceptually with the concept of Entrustable Professional Activities
Technically through Learning Analytics:
Student models that monitor the learners’ competency development and
inform learners and supervisors (based on data of students, supervisors
and peers - probabilistic algorithms that learn from new incoming data)
Personalized feedback and visualization of development.
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
OVERVIEW PRESENTATIONS
1. prof. Harm Peters, Charité - Universitätsmedizin Berlin, Germany
Delphi study into Entrustable Professional Activities
2. dr. Bert Slof, Utrecht University, the Neth. and
dr. Äli Leijen, University of Tartu, Estonia
Competences and Assessment of Student Teachers
3. dr. Marieke van der Schaaf, Utrecht University, the Neth. and
dr. Bert Slof, Utrecht University, the Neth.
Electronic Portfolios and Learning Analytics
4. Ing. Eelco Scheurs, Maastricht University, The Netherlands
Participatory Design of Learning Analytics
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ENHANCING ELECTRONIC PORTFOLIOS FOR WORKPLACE-BASED ASSESSMENT BY LEARNING ANALYTICS
UTRECHT UNIVERSITY: MARIEKE VAN DER SCHAAF AND BERT SLOF
www.project-watchme.eu
@Project_WatchMe
E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
Develop complex competences
Integrated in context
Demands long learning trajectories in workplace
Deliberate practice: feedback and reflection
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
“Well done!”“Pleasure to supervise!”“Reliable candidate”“Poor fund of content knowledge”“Needs lots of supervision”
UNFORTUNATELY, HOW MANY DAILY FEEDBACK PRACTICES LOOK LIKE
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
Personalized Feedback that gives (Sadler, 1989; 2010):
insight into performance
ability to evaluate and monitor own process
suggestions to fill gap between expected norm and performance
That feeds into learners’ major feedback questions (Hattie & Timperley, 2007):
Where am I going? (goals, feedup)
How am I going? (feedback)
Where to next? (feedforward)
WHAT IS NEEDED
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
PRINCIPLES OF GOOD FEEDBACK (NICOL AND MACFARLANE-DICK, 2006)
1. Helps clarify what good performance is
2. Facilitates the development of self-assessment in learning
3. Delivers high quality information to learners about their learning
4. Encourages teacher and peer dialogue around learning
5. Encourages positive motivational beliefs and self-esteem
6. Provides oppurtunities to close the gap between current and desired performance.
7. Provides information to supervisors
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
ENTRUSTABLE PROFESSIONAL ACTIVITY
Task based instead of construct based approach
Crucial question: would I entrust this learner unsupervised with this task? (with my sick mother, animal or teach my daughter/son…)
An EPA is a task that an individual can be trusted to perform unsupervised, in a given professional context, once sufficient competence has been demonstrated. International Competency-Based Medical Education Collaborators, March 18, 2014
Ten Cate, Chen, Hoff, Peters, Bok & Van der Schaaf, 2015
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
COMBINE EPA APPROACH WITH LEARNING ANALYTICS
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Application of probabilistic student models that enable feedback based on multi sorted assessments
Measurement, collection, analysis and reporting of data about trainees in their contexts, for the purpose of understanding, and optimising learning and the utilising of environments in which it occurs (Solar, 2013)
Personalized feedback
Visualizing learners’ development
E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
ELECTRONIC PORTFOLIO
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
THE OVERALL WATCHME ARCHITECTURE
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
ASSESSMENT ARGUMENTS (MISLEVY, 2006)
What EPAs should be assessed and how
does the learner develop during the
curriculum?
What performance indicators should be
used to gain insight into a learner’s
competence?
What instruments should be used to assess
the EPAs?
StudentModel
EvidenceModel
TaskModel
E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
ARCHITECTURE OF BAYESIAN STUDENT MODEL
EPA Level
CompetencyLevel
Assessment outcomes
Narratives
States
Feedback decision
assessment
assessment
assessment
JIT FEEDBACK
PORTFOLIO MEBN FRAGMENTS
EPA decision
Interest selector
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
BAYESIAN STUDENT MODEL
represents the actual internal cognitive state of each learner as well as their
actual learning context.
contains enough pedagogical knowledge in order to be able to translate the
internal state and context into meaningful messages and information for
visualization.
is a back-end component, meaning that no direct user interaction is made
with this module.
will make suggestions to help the learners, assessors or supervisors improve
their performance.
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
Domain SM vs Individual SM
Domain SM (DSM)
Will encapsulate all the pedagogical knowledge required to model one
domain
Possible models are: Anaesthesiology Training, Veterinary Education,
Teacher Education and General Undergraduate Medical Education
Variations of these proposed models:
Veterinary Education (NL vs HU)
Teacher Education (NL vs EE)
General Undergraduate Medical Education (NL vs DE)
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
Domain SM vs Individual SM
Individual SM (ISM)
Will encapsulate all the pedagogical knowledge required to
model one learner.
The Individual SM will be a personalized domain model
⚠ One ISM cannot handle more than one domain at once
⚠ For multiple roles of the same individual (e.g. a learner is
both a supervisor and a trainee) the ISM will cover only the
learner’s role
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
JUST IN TIME PERSONALIZED FEEDBACK
Type of feedbackContent of feedbackMoment of feedback
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The Bayesian student model must indicate appropriateness of:
Some users are motivated by competition? Others
find competition demotivating.
Users want to know whether they are on
track.
Users want to be able to highlight and
save useful feedback.
Users would like an overview of weekly,
monthly and yearly goals.
E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
TASK AND EVIDENCE MODELS
E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
TYPES OF FEEDBACK
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
TYPES OF FEEDBACK
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
SURVEY REQUIREMENTS PERSONALIZED FEEDBACK
Two rounds, n = 8
Experts perceived the preliminary design
for personalized feedback to be indicative
for high quality and useful feedback
Most of the comments experts made
were in congruence of the principles of
good feedback
Result: least attention was paid to
support student’s self-esteem and
motivation
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
Example program codes: JIT Feedback
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{
"EPA": "TT_UU_EPA5", "PerformanceIndicator": "TT_UU_PI1", "PerformanceIndicatorLevel": "TT_UU_LEVEL3", "Translations": { "en": “check more often when existing tests and digital testing systems are inadequate and design new tests (incl. correction sheets).", "nl": “vaker te controleren of bestaande toetsen en digitale toetssystemen ontoereikend zijn en ontwerp hiervoor een nieuwe toetsen (incl. correctiemodel)." }
E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
Example Personalized feedback in EPASS portfolio
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
Example Personalized Feedback in EPASS portfolio
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
1. Utrecht University, NL
2. University Medical Centre Utrecht, NL
3. Szent Istvan University, Hungary
4. University of Tartu, Estonia
5. Universitätsmedizin Charité Berlin, Germany
6. University of California San Francisco, USA
7. Maastricht University, NL
8. Mateum, NL
9. University of Reading, UK
10.Jayway, Denmark
11.NetRom, Rumania/NL
THANK you for your attention!
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
ARCHITECTURE & API DESIGN
Student Model Server
Data Merger
Natural Language Processor
Student Model
API Dispatcher
Bayesian Network Manager
BN Model Storage
EPASS External API
JIT/VIZ External API
Privacy Manager
Numerical Data Processor
Error reporting
Data Storage
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E A R L I C O N F E R E N C E , 2 7 A U G 2 0 1 5 , L I M A S S O L C Y P R U S
2. 2. FACILITATES THE DEVELOPMENT OF SELF-ASSESSMENT
(REFLECTION) IN LEARNING
Timeline overview:Detailed visualization:
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