This work is co-funded by the EC within FP7, Project no. 231526, http://www.DynaLearn.eu
DynaLearn Learning conceptual
knowledge
Bert Bredeweg
University of Amsterdam, Netherlands [email protected]
http://www.DynaLearn.eu
Outline
• Context • Problem (evaluation study) • Problem (cause) • Our solution • Does it work? • Call for collaboration
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Context (e.g. Physics)
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Context (e.g. Physics)
Text books a.o. resources
Teachers Models & simulation
Assignments
Investigate Reflect
Create Discuss
Ask
Inquiry-‐based
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Outline
• Context • Problem (evaluation study) • Problem (cause) • Our solution • Does it work? • Call for collaboration
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But does it work?���Study with 212 MIT graduate students
Scenario (goal): • CO2 in atmosphere stabilizes in 2100.
• Sketch the likely CO2 emissions • Sketch the likely net removal
Example
Sterman, J.D. (2008). Risk Communication on Climate: Mental Models and Mass Balance, Science, Vol. 322, pp. 532-533.
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Incorrect answer by���84% of MIT graduate students
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Incorrect answer by���84% of MIT graduate students
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Outline
• Context • Problem (evaluation study) • Problem (cause) • Our solution • Does it work? • Call for collaboration
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What is the problem? Conceptual knowledge is implicit…
Causality?
Distinct states?
Structure or Behaviour?
Processes?
Force causes Acceleration
Ice / Water / Steam Heat-‐Olow / Boiling
Car versus Velocity
Something is missing…
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Outline
• Context • Problem (evaluation study) • Problem (cause) • Our solution • Does it work? • Call for collaboration
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BeeHabitat
Live inHuman
Live in
FoodLive in
Size
ZpPlusZero
Death
ZpPlusZero
Birth
ZpPlusZero
Pesticide
IInterval
Number of
ZpPlusZero
Technology
ZpPlusZero
SizeZpPlusZero
Creating conceptual knowledge
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Working with conceptual knowledge – An impression…
Model
Carbon dioxide Plant populationGlobal population
Atmosphere
AbsorbsEmitsContains
Amount
ZscdDangerousCriticalSomeZero
Photosynthesis !
ZpPlusZero
Carbon emissions
ZpPlusZero
Energy use !
ZpPlusZero
Physical structure
Behaviour
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Working with conceptual knowledge – An impression…
Model
Carbon dioxide Plant populationGlobal population
Atmosphere
AbsorbsEmitsContains
Amount
ZscdDangerousCriticalSomeZero
Photosynthesis !
ZpPlusZero
Carbon emissions
ZpPlusZero
Energy use !
ZpPlusZero
Value history
Carbon dioxide: Amount
ZeroSomeCriticalDangerous
1 2 3 4 5
Global population: Carbon emissions
ZeroPlus
1 2 3 4 5
Global population: Energy use
ZeroPlus
1 2 3 4 5
Plant population: Photosynthesis
ZeroPlus
1 2 3 4 5
State-graph
1
2
3 4 5
Agent Entity
Quantity Causality Causality
Three distinct behaviour paths
Distinct values
Logical foundation
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The DynaLearn solution
• Learning by Conceptual modelling – Systems thinking – Concepts – Active (incl. inquiry) – ReOlective
State
Landmark Cause Propagation
Quantity Structure Behavior
Feedback
Qspace
Ambiguity Behavior-path
Process Agent
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Outline
• Context • Problem (evaluation study) • Problem (cause) • Our solution • Does it work? • Call for collaboration
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Does it work?
• Evaluation studies show that DynaLearn: – Is usable from secondary education onwards – Can be used to learn domain knowledge – Contributes to systems thinking – Allows gradual acquisition of modelling expertise (using 6 learning spaces)
– Motivates students towards learning science
Mioduser et al., 2012. Final report on DynaLearn evaluation studies. Deliverable 7.4, DynaLearn, EU FP7 project 231526.
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Outline
• Context • Problem (evaluation study) • Problem (cause) • Our solution • Does it work? • Call for collaboration
Move beyond the proof of concept
Establish more value in classrooms
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Call for collaboration!
• Work with speciOic teachers in Europe – Secondary education – Biology / Physics / Computer science / Interdisciplinary – Inquiry-‐based approach (?)
• To work on: – Developing the learning experience
§ Scenario’s & Questions, Assignments, Example models, etc... – Classroom implementation & evaluation
• Support from us: – Co-‐development & Online coaching
Preferably 1st half of 2015
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Overview paper
Bredeweg, et al. (2013). DynaLearn – An Intelligent Learning Environment for Learning Conceptual Knowledge, AI
Magazine, 34(4), 46-65.