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Adaptive WebBased Leveling Courses

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Adaptive Web-Based Leveling Courses. Shunichi Toida, Chris Wild, M. Zubair. Li Li, Chunxiang Xu ... Inexpensive/Ubiquitous Multi-media PCs. Improving ... – PowerPoint PPT presentation

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Title: Adaptive WebBased Leveling Courses


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
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14
Status
15
Query-based content selection
16
Student/Peer Awareness
  • Problem motivating in a self-paced course
  • Show progress relative to peers
  • Show current class averages in assessment material

17
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18
Artificial Intelligence in Discrete Math
  • Theorem prover and symbolic computation are
    used for exercises on
  • English to logic translation
  • Checking inferences
  • Checking induction proofs

19
Proving Equivalences of Natural Language to Logic
  • Translate the following sentence into predicate
    calculus using likes(x,y) predicateNobody
    likes JOHN
  • There are multiple correct answers

20
Proving Equivalences of Natural Language to Logic
  • Translate the following sentence into predicate
    calculus using likes(x,y) predicateNobody
    likes JOHN

21
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

22
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

23
Induction Proofs
  • Built on the MAPLE symbolic computation system of
    MATLAB
  • Example
  • 12 n n(n1)/2

24
Example of Jtree/and some content
25
On-going and Future Work
  • Continue development of course materials
    (adaptability, exercises)
  • Integrate pieces
  • Define evaluation metrics (market, effectiveness)
  • Run assessment

26
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

27
End of Presentation
28
Student Profile
lt?xml version"1.0"?gt lt!DOCTYPE STUDENT PROFILE
"profile.dtd"gt ltcourse title"cs381 course"
studentJohn Smithgt ltblock title"Propositiona
l Logic" status"U"gt ltblock
title"Proposition" status"U"gt ltlesson
title"What Is Proposition" href"coursecs381,bl
ockcs381-1- block1.2,lessoncs381-less
on01"gt lt/lessongt lt/blockgt
lt/blockgt lt/coursegt
29
Course Navigation
  • Java applet navigation of high level course
    structure
  • Access controlled by student profile

30
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

31
(No Transcript)
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