The document describes the DynaLearn approach to developing intelligent tutoring systems. It focuses on using conceptual modeling to help students construct knowledge about systems. Students build qualitative models and receive feedback to improve their understanding. The approach includes several interactive learning spaces to provide guidance, diagnosis of errors, and engagement through virtual characters. The goal is to develop an environment that supports open-ended conceptual modeling to address declines in science education.

1. Intelligent Tutoring
Systems
The DynaLearn Approach
Wouter Beek, Bert Bredeweg
me@wouterbeek.com, B.Bredeweg@uva.nl
Informatics Institute
University of Amsterdam
The Netherlands
This work is co-funded by the EC within FP7, Project no. 231526, http://www.DynaLearn.eu

2. Problem statement
• Worrying decline in science curricula
– Less students sign up; more students drop out.
• Main reasons:
– Lack of engagement and motivation in science teaching
(Osborne et al. 2003).
– Teaching practice involves surface knowledge in terms of
formulas and uninterpreted numeric data.
– Lack of interactive tools to construct conceptual
knowledge.

3. Conceptual science education
Having learners acquiring conceptual knowledge
of system’s behaviour:
• Deep knowledge in terms of the concepts that are involved.
• Learn basic principles that can be carried over to other
problem instances.
• Learn to adequately explain and predict the behaviour of
systems to utilise their functioning for human benefit.
• A prerequisite for working with numerical models and
equations.
• Communicate insights to the general public.

4. Knowledge construction
• Develop an interactive learning environment that
allows learners to construct their conceptual system
knowledge.
• Characteristics:
– Accommodate the true nature of conceptual knowledge.
– Automate feedback for open-ended construction tasks.
– React to the individual knowledge needs of learners.
– Applied to the interdisciplinary curriculum of
environmental science.
– Be engaging by using personified agent technology.

5. Part I
LEARNING BY CONCEPTUAL
MODELLING

6. Learning by Conceptual
Modelling
• Modelling is fundamental to human cognition and
scientific inquiry (Schwarz & White 2005)
• Simulations mimic the behaviour of real-world
systems.
• Conceptual Reasoning captures the human
interpretation of reality:
– Couched in the appropriate vocabulary.
– Remove numerical ‘overhead’.
– Provides handles to automate interaction.

7. Quantitative & Qualitative
Knowledge
F
m F=m*a
– An increase (or decrease) in Force causes an increase (or
decrease) in Acceleration
– An increase (or decrease) in Mass causes an decrease (or
increase) in Acceleration
– An increase (or decrease) in Acceleration causes a
decrease (or increase) in Mass.

8. Explicitizing the semantics of the
domain
• Scope: Which aspects of the system should be
included in the model? (relevant/irrelevant)
• Granularity: What is the level of detail that should
be modeled?
• Compositionality: How must knowledge be put in
modules in order to allow knowledge reuse?
• Conditionality: Under what conditions do certain
knowledge modules apply?

9. build simulate
1 1
1
 Causal relations
 Derivative values 2 2
2
 State graph

 Magnitude values
 Quantity spaces
3 3
3 

Value history
Transition history
Quantity values
e
 Causal influence n


Correspondences
(In)equalities
4
4
g 4
4  Equation history
 External factors i
 Calculi  Dependencies
 Conditionals
5
5 n
e 5
5  Causal view
 Model fragments
 Knowledge library
 Hierarchies
 Multiple scenarios
6
6 6
6

10. Example: Population dynamics
• How do populations in general behave?
• What processes determine their behaviour?
• Issues:
– Size (number of individuals)
– Birth / Natality
– Death / Mortality

11. Constructing knowledge (1)
Generic class
Specific instance
Quantity
Current value
Derivative
Possible values (direction of change)

12. Constructing knowledge (2)
Influence:
The amount of Birth
increases Number of
Proportionality:
Changes in Number of
determine changes in Birth

13. Constructing knowledge (3)
Positive influence Negative influence

14. Simulation results

15. Part II
GROUNDING, FEEDBACK,
RECOMMENDATIONS

16. Grounding
Student
Expert/teacher
http://dbpedia.org/resource/Size
http://dbpedia.org/resource/Population
http://dbpedia.org/resource/Mortality_rate
grounding
Semantic repository

17. Feedback & Recommendations
e.g., “You can complete your
feedback
model with a P+ proportionality”
Expert
Student
Community of users
e.g., “Users who modelled recommendations
death also modelled birth”

18. Part III
DIAGNOSIS & REPAIR

19. Blueprint Component
Library
Build CCM
Initial OBS
Inputs
CCM/SD
Outputs
Device Diagnose Diagnoses Repair
Measuring
Read OBS
result
Observe
Measuring Probe
Perform
action point

20. QR Model Simulate Blueprint
QR Sim Component
Library
Inspect
Build CCM
Initial OBS
Expectation
Inputs
Modelling CCM/SD
Outputs
Goals
Model Communicate
Automatic
Student
Device Diagnose Diagnoses Repair
Repair
Measuring
Read
Responds Answer OBS
result
Observe
Measuring Probe
Ask
Perform Question
action point

21. Example
I expect Free Space Then this directed
to be Low. correspondence
What should be the cannot be right.
value of Inhabited
Space in state 2?
Inhabited Space
should be High there.

22. Part IV
MOTIVATION &
ENGAGEMENT

23. Character roles
• Feedback & Recommendations
• Diagnosis & Repair
• Causal Explanation (why?)
• Teachable agent
• What is?
• How to?
• Quiz

24. Teachable agent
Biswas, et.al
(Betty's Brain)

25. Causal Explanation (why?)

26. http://www.DynaLearn.eu
Concluding remarks
• Problem statement and Context
– Communicative interaction (science education)
• Knowledge representation and Reasoning
– Qualitative system dynamics / Conceptual knowledge
• Progressive learning spaces (6 spaces)
• Feedback for Reflective thought
– Semantic Web techniques (model ingredients)
– Consistency-based Diagnosis (simulation results)
• Inducing Motivation (virtual characters & modes)

27. http://www.DynaLearn.eu
Project Partners
UVA (Netherlands) TAU (Israel)
UPM (Spain) UH (UK)
UAU (Germany) IBER (Bulgaria)
FUB (Brazil) BOKU (Austria)