In the age of information, only personalized learning and education enables the adaption to always changing requirements. In health care, labor forces are especially in a continuous need of improvement and adaption. Maintaining an optimal care sets a dual goal in the education of health professionals: adapt to scientific developments while retaining the compliance with changing laws and regulations.
Computerized adaptive testing (CAT), as a dynamic approach to education, is providing here the right adaptivity by optimizing time and precision of learning and scaling to the ability of the learner.
This paper will provide an overview on the current state of CAT and connected methodologies and shed light on the potentials for the next generation of adaptive testing, which can support the emergence of novel ways of education. Its strength is shown on a scenario from the health care sector.
Influencing policy (training slides from Fast Track Impact)
Extending Computerized Adaptive Testing to Multiple Objectives: Envisioned on a Case from the Health Care.
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Extending Computerized Adaptive
Testing to Multiple Objectives:
Envisioned on a Case from the
Health Care.
Christian Weber, Réka Vas
Egovis 2014
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Background: The eduworks network
University of
Salamanca
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For a better educational testing
Know-ledge
Culture
Cognitive
Type
Matching
Labour
Education
Human
profile
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The health perspective
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Need for ongoing training in the health care
• Immense pressure on doctors within their daily practice
and as well in terms of an (expected) ongoing training
E.g. obligation for training in Germany since 2005
• More and more tasks are transferred to nurses or other
caretaker
E.g. they have a major role in Alzheimer early detection
(World Alzheimer Report 2011)
• But .. a focus for ongoing training for nurses is missing
need for support in education
But the need comes with special demands..
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The demands of medical training
• Time is restricted
For education (but also for the support of domain experts)
• The learning has an educational context
• Must-know knowledge
regulations & mandatory knowledge
• Should-know knowledge
new insights & specializations
• The learning has a cultural context
Care taker and nurses have different cultural backgrounds with
different perception to learning. patients
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7. Time to learn
Time to test
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Designing a new test
Know-ledge
Restricted
time
Culture
Influence
Learning
goal
Nurses
Labour
Education
Human
profile
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Computerized Adaptive Testing - CAT
pass
fail
pass
pass
pass
pass
fail
fail
Ab i l i t y L e v e l
pass
Te s t I t ems
100
I t em Di f f i c u l t y
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9. Item response function (3PL)
as the probability that a person with ability “theta”
answers an item
• b – difficulty, (half way between c(min) and 1)
• a – discrimination, (slope at b, differentiating the users)
• c – chance for guessing
(building on a logistic function)
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Limitations of question organization
Know-ledge
Culture
Cognitive
Type
Q70
Q40
Q60
Q60
Q60
Q60
Q40
Q65
Q70
Q50
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Limitations of question organization
Know-ledge
Add Meta Information
• Cultural context
• Learning type
• Obligation
yes/no
•...
Culture
Cognitive
Type
Q40
Q60
Q60
Q60
Q70
Q60
Q40
Q65
Q70
Q50
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Limitations of question organization
Know-ledge
Exploring the user on the education
Add Meta Information
• Cultural context
• Learning type
• Obligation
yes/no
•...
Culture
Cognitive
Type
Q40
Q60
Q60
Q60
Q70
Q60
Q40
Q65
Q70
Q50
User domain
Educational domain
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Overcoming limitations
• How to grasp the user knowledge in the best possible
way?
Framing question with no final answer.
• How to add more context to it?
Extending the structure and indicators.
• How to detect also his/her understanding of an area?
Using knowledge, structured and in relation.
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The demands of medical training - revisited
• Time is restricted
For education (but also for the support of domain experts)
• The learning has an educational context
• Must-know knowledge as meta data
regulations & mandatory knowledge
• Should-know knowledge as meta data
new insights & specializations
• The learning has a cultural context
Care taker and nurses have different cultural backgrounds with
different perception to learning. through modelling
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Differential Item Functioning - DIF
• Occurs when people of different groups have a different
probability to answer correct, independent of the same
underlying ability.
Groups in terms of culture, educational character or learning
style
• Detection normally through statistical analysis of the
performance on the test.
The designer view: Detection only if a
grouping context is known..
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DIF context through virtualization
• Instead of removing DIF items,
redundant item modelling
(Makransky and Glas)
• DIF biasing
Harder or easier
(difficult level)
• Alternatively:
Redundant question
and real structure
But how could a structure look like?..
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Structure - A new concept of layers
Q12
Q13 Q21
Q11
Q22
Q32
Q33
Q31
Qxy
Qxy+
1
Q42
Q41
1
2
3
4
y
e.g. item difficulty,
cognitive type..
educational domain
complexity / relations
difficulty new ?= f(diff Q xy , compl N x)
++ meta-information
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Testing strategy: Thinking about a new frame with
(Complex) Event Processing (CEP)
Event
Agent
Agent
Consumer
Consumer
Event
Consumer
the system behavior as a stream of data
Pattern
Detection
Trans-for-mation
Filter
Agent
Data
Data
Event
producer
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19. e.g learning type
compatible to job x
fast tester
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A new frame for CAT with context
Domain
Ontology
change/modify
Testing
Strategies
Event
Consumer
Meta-
Strategies
Q
3
2
Q
3
3
Q
xy+
1
Q
4
1
Q
4
2
3
4
y
Data
Context
Agent
Context
Agent
Context
Agent
the system behavior as a stream of data
Intelligent
Test Engine
Q
x
y
Event
producer
e.g right/wrong
~perform well
time for answer
Event
Q
1
2 Q
Q
2
1
Q
2
2
Q
3
1
2
Data + access to the ontology
Q
1
1
1
3
1
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Exploration, yes - but where (how) to go..
Q
Q
Q
Q
?
?
?
Testing
Strategies
Meta-
Strategies
Intelligent
Test Engine
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Next level questions..
• How are the rules to switch and modify the testing
strategy and how to manage it on the fly?
..depth/breadth first, related concepts, maintain main test
• What is the best choice to model a context parameters
for the strategic exploration?
• What is the best fusion of parameters?
.. psychometrics and graph/ontology based complexity measures
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Data Sources
• Now:
• Educational knowledge within the domain ontology as classes
and relations
• Logging feedback from the actual testing
• Future:
• Target-oriented performance feedback from the user
To:
• grouping of users after special criteria
• Feedback on the content – e.g. Improving meta-data on the nodes
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Summary
• Need for a crisp testing to detect training potential in
knowledge intense professions.
Especially in the health care sector
• CAT is a suitable solution for educational testing..
..but there is room for improvement for a context aware testing
• A new frame and thinking could enable a next
generation of context sensitive tests, with..
• structured backing knowledge (ontology)
• a novel testing behaviour (modifying test behaviour online)
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Thank you for
your attention!
Corvinno Technology Transfer Center
H-1093 Budapest, Közraktár utca 12/a.
Tel: +36 1 210 80 62
http://www.corvinno.com
Notes de l'éditeur
In the UK nurses also have the obligation to attend to an ongoing vocational training
CAT has the pros – shorter test time, higher precision, a random test assesment
Cons – items need to be calibrated, time not fixed and depends on the user, tests are not directly comparable (assessment tests)