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Adaptive Web-Based Leveling Courses

Adaptive Web-Based Leveling Courses. Shunichi Toida, Chris Wild, M. Zubair Li Li, Chunxiang Xu Computer Science Department Old Dominion University. Outline. Motivation and background Objectives System Overview functional requirements implementation Status Course structure Jtree

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Adaptive Web-Based Leveling Courses

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  1. Adaptive Web-Based Leveling Courses Shunichi Toida, Chris Wild, M. ZubairLi Li, Chunxiang Xu Computer Science Department Old Dominion University

  2. Outline • Motivation and background • Objectives • System Overview • functional requirements • implementation • Status • Course structure Jtree • Artificial intelligence in discrete math • Student/peer awareness • Future Work • Conclusions

  3. Needs • Non-traditional Student • Second Career • Transfer • Second Major • Non-traditional Delivery • At Work/Home - Anywhere • Evenings?weekends – Anytime • Less expensive

  4. Technology • Inexpensive/Ubiquitous Multi-media PCs • Improving Communications (internet) Effective Utilization will require • Learning models • Methods of organization and delivery • Motivational mechanisms

  5. Background • ODU CS Dept TechEd initiative • BS degree for AA graduates • Target non-traditional students • Web-centric delivery of course material

  6. Background • ODU CS Dept TechEd initiative • BS degree for AA graduates • Target non-traditional students • Web-centric delivery of course material Problem: Diverse backgrounds of entering students

  7. Background • ODU CS Dept TechEd initiative • BS degree for AA graduates • Target non-traditional students • Web-centric delivery of course material Problem: Diverse backgrounds of entering students Solution: Leveling courses in discrete math and programming

  8. Objectives To develop courses that are • adaptive • web based • leveling • supported by AI technologies • managed

  9. System Overview

  10. Use Case Summary

  11. Functional Requirements • Students • Navigate the course based on his profile and progress • Get status on his/her progress and his relative performance • Immediate feedback where possible • Instructor • Specify courses structure • Classify course contents • Monitor students performance • Trouble Alerts

  12. Architectural Features • Course description including pre-requisite structure (Oracle) • IEEE Learning Objects Metadata Standard • Student profile and progress (Oracle) • Browsing support for course structure using applet • Content access based on student progress

  13. Status

  14. Query-based content selection

  15. Student/Peer Awareness • Problem: motivating in a self-paced course • Show progress relative to peers • Show current class averages in assessment material

  16. Artificial Intelligence in Discrete Math Theorem prover and symbolic computation are used for exercises on: • English to logic translation • Checking inferences • Checking induction proofs

  17. Proving Equivalences of Natural Language to Logic • Translate the following sentence into predicate calculus using “likes(x,y)” predicate“Nobody likes JOHN” • There are multiple correct answers

  18. Proving Equivalences of Natural Language to Logic • Translate the following sentence into predicate calculus using “likes(x,y)” predicate“Nobody likes JOHN”

  19. Handling Multiple Solutions • Restrict response to unique canonical form • Compare student response to “all” correct/obvious answers • Prove equivalence of student response to any correct answer

  20. Handling Multiple Solutions • Restrict response to unique canonical form • Compare student response to “all” correct/obvious answers • Prove equivalence of student response to any correct answer TPS: Theorem Proving System

  21. Induction Proofs • Built on the MAPLE symbolic computation system of MATLAB Example 1+2+… + n = n(n+1)/2

  22. Example of Jtree/and some content

  23. On-going and Future Work • Continue development of course materials (adaptability, exercises) • Integrate pieces • Define evaluation metrics (market, effectiveness) • Run assessment

  24. Conclusions • Need to serve non-traditional students • Need to adapt to diverse backgrounds • Need learning environment architectures and technologies • Need effective learning strategies which leverage the potential of web connectivity

  25. End of Presentation

  26. Student Profile <?xml version="1.0"?> <!DOCTYPE STUDENT PROFILE "profile.dtd"> <course title="cs381 course" student=”John Smith”> <block title="Propositional Logic" status="U"> <block title="Proposition" status="U"> <lesson title="What Is Proposition" href="course=cs381,block=cs381-1- block1.2,lesson=cs381-lesson01"> </lesson> </block> </block> </course>

  27. Course Navigation • Java applet navigation of high level course structure • Access controlled by student profile

  28. Course Development • XML Course Mark-up Language Customized for course structure e.g. course, block, lesson (marks) • Web-based Development Tools • Servlet (Tomcat) • Java Server Page (Tomcat) • Java

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