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Adaptive Web-Based Leveling Courses

This presentation discusses the design and implementation of adaptive web-based leveling courses aimed at non-traditional students in Computer Science. It addresses the diverse backgrounds of students returning for education and presents an AI-supported system that personalizes the learning experience. Key features include immediate feedback, peer awareness mechanisms, and a structured course outline. The initiative provides flexibility for learners, utilizing online delivery, interactions, and multimedia resources to enhance accessibility and effectiveness.

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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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