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Intelligent Tutoring Systems: The DynaLearn Approach


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Intelligent Tutoring Systems: The DynaLearn Approach

  1. 1. Intelligent Tutoring Systems The DynaLearn Approach Wouter Beek, Bert Bredeweg, Informatics Institute University of Amsterdam The NetherlandsThis work is co-funded by the EC within FP7, Project no. 231526,
  2. 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. 3. Conceptual science educationHaving learners acquiring conceptual knowledgeof 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. 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.
  6. 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. 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. 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. 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. 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. 11. Constructing knowledge (1) Generic class Specific instance QuantityCurrent value Derivative Possible values (direction of change)
  12. 12. Constructing knowledge (2)Influence: The amount of Birth increases Number ofProportionality: Changes in Number of determine changes in Birth
  13. 13. Constructing knowledge (3)Positive influence Negative influence
  14. 14. Simulation results
  16. 16. Grounding StudentExpert/teacher grounding Semantic repository
  17. 17. Feedback & Recommendations e.g., “You can complete your feedback model with a P+ proportionality” Expert Student Community of userse.g., “Users who modelled recommendationsdeath also modelled birth”
  19. 19. Blueprint Component Library Build CCM Initial OBS Inputs CCM/SD OutputsDevice Diagnose Diagnoses Repair Measuring Read OBS result Observe Measuring ProbePerform action point
  20. 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. 21. ExampleI 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.
  23. 23. Character roles• Feedback & Recommendations• Diagnosis & Repair• Causal Explanation (why?)• Teachable agent• What is?• How to?• Quiz
  24. 24. Teachable agent Biswas, (Bettys Brain)
  25. 25. Causal Explanation (why?)
  26. 26. 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. 27. Project PartnersUVA (Netherlands) TAU (Israel)UPM (Spain) UH (UK)UAU (Germany) IBER (Bulgaria)FUB (Brazil) BOKU (Austria)