Adaptive web based leveling courses
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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

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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


System overview

System Overview


Use case summary

Use Case Summary


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


Status

Status


Query based content selection

Query-based content selection


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


Example of jtree and some content

Example of Jtree/and some content


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


End of presentation

End of Presentation


Student profile

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>


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


  • Login