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Jan Claes
Supervisors UGent : Geert Poels & Frederik Gailly
Supervisors TU/e : Paul Grefen & Irene Vanderfeesten
Investigating the process of process modeling
and its relation to modeling quality
The Role of Structure Serialization
2. 2/15
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Study 1: visualization
Research Objective 1
Build knowledge about
how people create models
Overall objective
Curiosity-driven
Build knowledge about PPM
Research Objective 2
Build knowledge about
relation with quality
Research Objective 3
Build knowledge about
structured modeling
Study 3: theorizationStudy 2: exploration
Research Objectives
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Study 1 – Visualization
Evaluation
Sample of intended users (6 academic researchers)
Five extreme examples in PPMChart or Dotted Chart
Observe and measure amount, quality, and timing
of insights gained through the visualization
Observe and ask about perceived usefulness
Results
Perceived as useful
More cognitive effective than Dotted Chart
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Study 2 – Exploration
Based on dataset of 40 unique modeling executions
Fast
modeling
Slow
modeling
Initial
delay
Many
pauzes
Few
elements
Many
elements
No
(separate)
lay-outing
Quick
lay-outing
Dedicated
lay-outing
phase
Continuous
lay-outing
Unpaired
event
creation
Paired
event
creation
No pauzesSerializationPaired
gateway
creation
Delayed
edge
creation
Chunked
modeling
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Study 2 – Exploration
Structuredness Movement Speed
Based on dataset of 40 unique modeling executions
Fast
modeling
Slow
modeling
Quick
lay-outing
Dedicated
lay-outing
phase
Continuous
lay-outing
Serialization
Chunked
modeling
CONJECTURE 1
CONJECTURE 2
CONJECTURE 3
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Study 2 – Exploration
Conjecture 1: Structured modeling
results in
more understandable models
Conjecture 2: A high number of move operations
results in
less understandable models
Conjecture 3: Slow modeling
results in
less understandable models
10. 10/15
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Study 2 – Exploration
Based on dataset of 103 unique modeling executions
T-test
t=-2,231
(p=0,028)
T-test
t=2,199
(p=0,030)
CONJECTURE 1
structuredness
T-test
t=-1,984
(p=0,049)
T-test
t=0,457
(p=0,648)
T-test
t=-2,183
(p=0,031)
T-test
t=2,505
(p=0,014)
CONJECTURE 2
movement
CONJECTURE 3
speed
11. 11/15
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Study 3 – Theorization
Based on dataset of 103 unique modeling executions
T-test
t=-2,231
(p=0,028)
T-test
t=2,199
(p=0,030)
CONJECTURE 1
structuredness
T-test
t=-1,984
(p=0,049)
T-test
t=0,457
(p=0,648)
T-test
t=-2,183
(p=0,031)
T-test
t=2,505
(p=0,014)
CONJECTURE 2
movement
CONJECTURE 3
speed
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Study 3 – Theorization
Combined
Flow-oriented Aspect-oriented
Undirected
Based on dataset of 118 unique modeling executions
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Study 3 – Theorization
A B A determines BA B The more A, the more B+ A B The more A, the less B– A B A translates into B
learning style
degree of serialization
adopted serialization style
field-dependency need for structure
– +
course of intrinsic cognitive load
for process modeling phases
course of intrinsic cognitive load
for aggregation phases
course of cognitive overload
course of intrinsic cognitive load
for strategy building phases
+ + +
serialization style fitstructuredness of serialization
– –– –
1 2 3
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Study 3 – Theorization
Evaluation of utility
Novelty (uses existing theories in fundamental new way)
Parsimony (11 constructs, 15 associations)
Consistency (can explain additional observations)
Plausibility (accurate and profound explanation)
Credibility (building blocks are established theories)
Transferability (problem solving in general)
Consistency based on dataset of 143 unique modeling executions
15. 15/15
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FUTURE
WORK
Knowledge gaps
Knowledge contribution Research instrument
Knowledge gap 4
How to change one’s
modeling strategy?
Knowledge gap 3
How should one model
in a specific context?
Study 1: visualization
Contributions
A – PPMChart
B – 22 patterns
C – 13 observations
Study 2: exploration
Contributions
D – 8 patterns
E – 3 conjectures
F – 1 metric
Knowledge gap 1
How do people
currently model?
Knowledge gap 2
Which strategy is
intrinsically better?
A B
C
D E F
H
HG
I
Study 3: theorization
Contributions
G – 5 styles
H1 – 6 observations
H2 – 3 impressions
I – SPMT
I