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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

Adaptive Web-Based Leveling Courses

Shunichi Toida, Chris Wild, M. ZubairLi Li, Chunxiang Xu

Computer Science Department

Old Dominion University

outline
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
needs
Needs
  • Non-traditional Student
    • Second Career
    • Transfer
    • Second Major
  • Non-traditional Delivery
    • At Work/Home - Anywhere
    • Evenings?weekends – Anytime
    • Less expensive
technology
Technology
  • Inexpensive/Ubiquitous Multi-media PCs
  • Improving Communications (internet)

Effective Utilization will require

  • Learning models
  • Methods of organization and delivery
  • Motivational mechanisms
background
Background
  • ODU CS Dept TechEd initiative
    • BS degree for AA graduates
    • Target non-traditional students
    • Web-centric delivery of course material
background1
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

background2
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

objectives
Objectives

To develop courses that are

  • adaptive
  • web based
  • leveling
  • supported by AI technologies
  • managed
functional requirements
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
architectural features
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
student peer awareness
Student/Peer Awareness
  • Problem: motivating in a self-paced course
  • Show progress relative to peers
  • Show current class averages in assessment material
artificial intelligence in discrete math
Artificial Intelligence in Discrete Math

Theorem prover and symbolic computation are used for exercises on:

  • English to logic translation
  • Checking inferences
  • Checking induction proofs
proving equivalences of natural language to logic
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
proving equivalences of natural language to logic1
Proving Equivalences of Natural Language to Logic
  • Translate the following sentence into predicate calculus using “likes(x,y)” predicate“Nobody likes JOHN”
handling multiple solutions
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
handling multiple solutions1
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

induction proofs
Induction Proofs
  • Built on the MAPLE symbolic computation system of MATLAB

Example

1+2+… + n = n(n+1)/2

on going and future work
On-going and Future Work
  • Continue development of course materials (adaptability, exercises)
  • Integrate pieces
  • Define evaluation metrics (market, effectiveness)
  • Run assessment
conclusions
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
course navigation
Course Navigation
  • Java applet navigation of high level course structure
  • Access controlled by student profile
course development
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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