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SimStudent: A Computational Model of Learning as a Research Toolbox for the Sciences of Learning

SimStudent: A Computational Model of Learning as a Research Toolbox for the Sciences of Learning. Noboru Matsuda Human-Computer Interaction Institute C arnegie M ellon U niversity. Research Questions.

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SimStudent: A Computational Model of Learning as a Research Toolbox for the Sciences of Learning

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  1. SimStudent:A Computational Model of Learning as a Research Toolbox for the Sciences of Learning Noboru Matsuda Human-Computer Interaction Institute Carnegie Mellon University

  2. Research Questions • Building a cognitive model is hard. Can machine-learning techniques help non-expert authors build a Cognitive Tutor? • Would like to simulate students’ learning. Can machine-learning techniques help us build a computational model of learning with a cognitive fidelity? • I heard that students learn by teaching others. Can we use the computational model of learning to study the theory of learning by teaching?

  3. Solution: SimStudent Machine learning agent Learns procedural skills, by Observing model solutions & solving problems Fundamental technology Programming by Demonstration Inductive Logic Programming Knowledge representation Production rules (Jess) Lau & Weld (1998). Blessing (1997).

  4. SimStudent Projects • Intelligent Authoring • Building a Cognitive Tutor as a CTAT Plug-in • Student Modeling and Simulation • Controlled educational studies • Error formation study • Prerequisite conceptual knowledge study • Teachable Peer Learner • Learning by teaching

  5. Authoring a Cognitive Tutor Example-Tracing Tutor Little programming A cognitive model specific to a particular problem Limited generalization by editing a behavior graph Model-TracingTutor Powerful student model Cognitive task analysis is hard Writing production rules is even more challenging Performing a task is relatively easy…

  6. Next Generation Authoring Build a tutor GUI Teaching a solution SimSt. learning Production Rules Rule simplify-LHS: IF is-equation( Eq ), is-lhs( Eq, Lhs ), polynomial( Lhs ), all-var-terms( Lhs ) Then simplify( Lhs, S-lhs ), enter( S-lhs ) Rule simplify-LHS: IF is-equation( Eq ), is-lhs( Eq, Lhs ), polynomial( Lhs ), all-var-terms( Lhs ) Then simplify( Lhs, S-lhs ), enter( S-lhs ) Rule simplify-LHS: IF is-equation( Eq ), is-lhs( Eq, Lhs ), polynomial( Lhs ), all-var-terms( Lhs ) Then simplify( Lhs, S-lhs ), enter( S-lhs )

  7. Demo Authoring by Tutoring

  8. Example: Learning to subtract a constant term Learning to subtract a constant number First example Subtract the difference between 4 and 3… subtract 1 Subtract the coefficient of X… I see 3x, 1, x, and 4 in the equation. I wonder where the ‘1’ came from… Subtract the last term on the left-hand side… PSLC Summer School 2012 :: SimStudent :: Noboru Matsuda (CMU)

  9. Example: Learning to subtract a constant term Learning to subtract a constant number First example Subtract the difference between 4 and 3… subtract 1 Subtract the coefficient of X… I see 3x, 1, x, and 4 in the equation. I wonder where the ‘1’ came from… Subtract the last term on the left-hand side… PSLC Summer School 2012 :: SimStudent :: Noboru Matsuda (CMU)

  10. Prior Knowledge • Feature predicates • 18 predicates • isFractionTerm(X), isConstant(X), isPolynomial(X),… • Operators • 42 operators • add(X,Y), coefficient(X), getFrstNumber(X), … PSLC Summer School 2012 :: SimStudent :: Noboru Matsuda (CMU)

  11. Example: Stoichiometry Tutor

  12. SimStudent Projects • Intelligent Authoring • Building a Cognitive Tutor as a CTAT Plug-in • Student Modeling and Simulation • Controlled educational studies • Error formation study • Prerequisite conceptual knowledge study • Teachable Peer Learner • Learning by teaching

  13. Model of Incorrect Learning • Identify errors students commonly make • Weaken SimStudent’s background knowledge • Let SimStudent make an induction error

  14. Weak Prior Knowledge Hypothesis • Multiple ways to make sense of examples Get a coefficient and divide Get a denominator and multiply “multiply by x” 3x=5 “divide by 3” 4/x=5 “divide by 4” Get a number and divide

  15. Results: Learning Rate Steps Score = 0 (if there is no rule applicable) # correct rule applications / # all rule applications Step Score # training problems

  16. Student Model (Li et al. EDM2011) A Machine Learning Approach for Automatic Student Model Discovery • A set of knowledge components (KCs) • Encoded in intelligent tutors to model how students solve problems • E.g. How to proceed given problems of the form Nv=N • One of the key factors that affects automated tutoring systems in making instructional decisions • Previous Approach: • Require expert input • Highly subjective • Proposed Approach: • Use a machine-learning agent, SimStudent, to acquire knowledge • 1 Skill  1 KC • Skill application for that step KC for each step

  17. Human-generated vsSimStudentKCs 4x = 20 vs. –x = 5

  18. Results • Significance Test • SimStudent outperforms the human-generated model in 4260 out of 6494 steps • p < 0.001 • SimStudent outperforms the human-generated model across 20 runs of cross validation • p < 0.001

  19. SimStudent Projects • Intelligent Authoring • Building a Cognitive Tutor as a CTAT Plug-in • Student Modeling and Simulation • Controlled educational studies • Error formation study • Prerequisite conceptual knowledge study • Teachable Peer Learner • Learning by teaching

  20. Learning by Teaching SimStudent

  21. Learn more about SimStudents • Project Web • www.SimStudent.org • Contact us • Noboru Matsuda • mazda@cs.cmu.edu

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