1. Bayesian Nets in Student Modeling ITS- Sept 30, 2004

2. Sources of Uncertainty Incomplete and/or incorrect knowledge Slips and/or guesses Multiple derivations Invisible inferences Not showing all work Help messages Self-explaining ahead Incomplete and/or incorrect knowledge Slips and/or guesses Multiple derivations Invisible inferences Not showing all work Help messages Self-explaining ahead

3. Andes student model Knowledge tracing Plan recognition 1st to use student’s domain knowledge Action prediction Andes first to support all three Knowledge tracing Plan recognition 1st to use student’s domain knowledge Action prediction Andes first to support all three

4. Goals of Andes Students work as much as possible alone React to student’s incorrect action, signal error, explain React to student’s impasse, provide procedural help Assure student understands examples, prompt self-explaining Students work as much as possible alone React to student’s incorrect action, signal error, explain React to student’s impasse, provide procedural help Assure student understands examples, prompt self-explaining

5. Types of help Error help Procedural help (ask for hints) Unsolicited help (for non-physics errors) Different levels of hints ‘til “bottom-out hint” Error help Procedural help (ask for hints) Unsolicited help (for non-physics errors) Different levels of hints ‘til “bottom-out hint”

6. Usage of student model Plan recognition: recognize and support goals (requires prediction) Asses knowledge: help presentation (reminder v. minilesson) Assess mastery level: prompt self- explanation or not Plan recognition: recognize and support goals (requires prediction) Asses knowledge: help presentation (reminder v. minilesson) Assess mastery level: prompt self- explanation or not

7. Self-Explaining Coach Step correctness (domain) Rule Browser E.g.: using force or acceleration Step utility (role in solution plan) Plan Browser Recognize goals Step correctness (domain) Rule Browser E.g.: using force or acceleration Step utility (role in solution plan) Plan Browser Recognize goals

8. Bayesian network Solution graph: map of all solutions with no variables (propositional)

9. Types of nodes Domain-general: rules 2 values indicating mastery Task-specific: facts, goals, rule apps, strategy nodes Doable (done already or knows all needed) Not-doable Domain-general: rules 2 values indicating mastery Task-specific: facts, goals, rule apps, strategy nodes Doable (done already or knows all needed) Not-doable

10. Knowledge evolution Dynamic Bayesian network Analyze each exercise alone Roll-up: prior probabilities set to marginal probabilities for previous Improvements: could model dependencies & knowledge decay Dynamic Bayesian network Analyze each exercise alone Roll-up: prior probabilities set to marginal probabilities for previous Improvements: could model dependencies & knowledge decay

11. Intention or ability? Probability that student can and IS implementing a certain goal Decision-theoretic tutor keeps probabilites of “focus of attention” Probability that student can and IS implementing a certain goal Decision-theoretic tutor keeps probabilites of “focus of attention”

12. Problem creation Givens Goals Problem-solver applies rules, generating subgoals until done Solution graph created Givens Goals Problem-solver applies rules, generating subgoals until done Solution graph created

13. Andes assessor Dynamic belief network for domain- general nodes Rules - priors set by test scores Context-Rules P(CR=true|R=true)=1 P(CR=true|R=false)=difficulty One context changes, adjust rest Dynamic belief network for domain- general nodes Rules - priors set by test scores Context-Rules P(CR=true|R=true)=1 P(CR=true|R=false)=difficulty One context changes, adjust rest

14. Task-specific nodes Fact, goal, rule application, strategy Context-Rule nodes link task-specific to domain-general rules Fact, goal, rule application, strategy Context-Rule nodes link task-specific to domain-general rules

15. Fact & Goal Nodes A.k.a. Propositional Nodes 1 parent for each way to derive Leaky-OR: T if 1 parent T, also sometimes true if not Reasons: guessing, analogy, etc A.k.a. Propositional Nodes 1 parent for each way to derive Leaky-OR: T if 1 parent T, also sometimes true if not Reasons: guessing, analogy, etc

16. Rule-Application Nodes Connect CR,Strategy & Prop nodes to new derived Prop nodes Doable or not-doable Parents: 1 CR, pre-condition Props, sometimes one Strategy node Noisy-AND: T if ALL parents T, but sometimes not, 1-alpha Connect CR,Strategy & Prop nodes to new derived Prop nodes Doable or not-doable Parents: 1 CR, pre-condition Props, sometimes one Strategy node Noisy-AND: T if ALL parents T, but sometimes not, 1-alpha

17. Strategy Nodes Used when >1 way to reach a Goal Paired with a Goal Node Values are mutually exclusive No parents in network Priors=freq. students use this strat. Used when >1 way to reach a Goal Paired with a Goal Node Values are mutually exclusive No parents in network Priors=freq. students use this strat.

18. Compare Figures Figure 9 before observing A-is-body Figure 10 after observing A-is-body Figure 9 before observing A-is-body Figure 10 after observing A-is-body

19. Hints Add a new parent to a Prop node Accounts for guessing Add a new parent to a Prop node Accounts for guessing

20. SE-Coach Adds nodes for Read Link these to Prop nodes Longer read time, higher prob knows Prop (p 26) Adds nodes for plan selection Link these to Context-Rules Rule Application node prob T if knows CR & all preconditions=Noisy-AND Adds nodes for Read Link these to Prop nodes Longer read time, higher prob knows Prop (p 26) Adds nodes for plan selection Link these to Context-Rules Rule Application node prob T if knows CR & all preconditions=Noisy-AND

21. Evaluation Simulated students, 65% correct for rule mastery 95% if no “invis inferences” and has to “show all work” Post-test shows significant learning Voluntary acceptance? Accuracy of plan recognition Simulated students, 65% correct for rule mastery 95% if no “invis inferences” and has to “show all work” Post-test shows significant learning Voluntary acceptance? Accuracy of plan recognition