1. A database describing the student’s knowledge of the domain topics

2. Student Knowledge: Tutor inferences about student knowledge General student information Student preference, e.g., she likes examples or graphics Acquisition and retention information

3. Representing Student Knowledge Overlay Model Overlay with “Buggy Knowledge Bayesian networks

4. How Much Student Knowledge Can Be predicted? Use internal, external, static and dynamic predictors Include pretests, number of hints, time to problem solve Question/argue with student

5. Shute [95] predicted Posttest Score Statistics Tutor: SMART: Modeled Incoming knowledge and skills Computed Student Model values

6. Education Prescore Aptitude SM

7. According to Shute [1995], Posttest Ability is Predictable

8. Ideally, the Student Model: Encodes the system’s changing view of the student’s strengths and weaknesses Self [1988] emphasized the need for the SM and cautioned against making it all things to all people. Problem--SM can contain all of cognitive knowledge

9. Go beyond right and wrong. Indicate: Late action Wrong protocol Missing action Early action As compared to Expert action

10. Improve the Student Model Integrate a simulation, planner, plan recognizer and user model Make assumptions about the user's state of knowledge and learning needs Inaccuracies in the Student model can be anticipated and avoided

11. Avoid Guessing--have student supply goal

12. Move simulation toward a pedagogical goal 1) Use plan execution and monitoring 2) Use tight interaction between simulation management and user modeling techniques 3) Goal state can be transformed into a set of simulation states

13. Cardiac Resuscitation [Eliot, 1994] Student Model vfib brady asys vtach sinus brady asys vtach vfib 30% 60% in 10 sec 10% 10% in 10 sec 25% 65% GOAL Domain Model Biased to reach goal state

14. Student Model Cardiac Resuscitation, [Eliot, 1994] Activities: presented in a context to challenge the student Goal state: predicted to give student most opportunity to learn However, simulation events must appear realistic Student model integrates medical/pedagogical constraints to derive tutoring goals

15. Cardiac Resuscitation [Eliot, 1994] Student Model addresses the difficulty of controlling system response Uses goal selection, plan formation, plan instantiation Manipulates the simulation Creates opportunities for learning

16. Viewable Student Model Lesgold [1992] Sherlock system –Teaches troubleshooting with a 1000+ component electric panel –Represented expert solution path to help novice learn to constrain problem –Did not discuss student model with student

17. Model Student Abilities Mathematics Tutor –Prediction used to construct problems of correct difficulty; to scaffold problem solving Regression Model [Beck et. Al, 1998] –Future performance =k 1 A + k 2 B + y-intercepts »Tutor’s Student’s »Estimateestimate

18. Student’s Response Almost as useful as tutor’s response Clarifies belief of ability and skill Is evaluated along with –History of hints (not much variance here) –Time to solve problem (too much variance)

19. Problems with Math Tutor Must be evaluated on a case-by-case basis, aggregate; student predictions are useless Tutor is too rigid and obtrusive --not using new models of math learning Student should do more reasoning and less symbol manipulation Student does not have control

20. Student Model Singularly the most robust predictor of posttest performance Potential problems –System might ignore student prediction if it is not useful –Assumes independence of observations within each group--student and tutor