Intelligent Tutoring Systems

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Intelligent Tutoring Systems

• 31st October, 2012
• CSE-435
• Tashwin Kaur Khurana

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Overview

• Intelligent Tutoring Systems
• Components of an ITS
• Problems
• Case Based Reasoning in ITS
• CBR Methods
• Examples
• Demo
• Summary
• Research areas

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ITS

• System that provides personalized tutoring by
• Generates problem solutions automatically
• Represents the learners knowledge acquisition
  processes
• Diagnoses learners approach to the solution
• Provides advices and feedback
• Intelligent Tutoring System (ITS) -
  computer-based training system that incorporate
  techniques for communicating / transferring
  knowledge and skills to students.
• ITS combination of Computer-Aided Instruction
  (CAI) and Artificial Intelligence (AI) technology

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Conventional Model
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Components of an ITS and their interaction

• The Student Model
• The Pedagogical or Tutor Model
• The Domain Knowledge

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The Student Model

• Keeps track of all information related to the
  learner
• Records performance of all the learners
• Problems assigned
• Complex uncommon problem solutions
• Description of approach to the solution with
  regard to a specific problem
• Allows system to adapt to learners needs
• Learners performance evaluated as a subset of an
  experts performance --- Drawback!!

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The Tutor Systems

• Automatic Cognitive analysis
• Path taken by student
• Goal
• Initial competence
• Learning rate
• Gets input from the Student model to make its
  decision to reflect the differing needs of each
  student.

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The Domain Knowledge

• Contains the information the tutor is teaching
• Concepts
• Rules
• Axioms
• Facts, etc
• Information on how to link the data for optimum
  performance of the system
• Should be updated if there are any changes in the
  domain !

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Individualization

• Actions required

• Problems!!!

• Problem solving information about each student
  should be stored for a long time
• This knowledge must be used for subsequent
  diagnoses and tutorial decisions!

• How to represent knowledge so it easily scales up
  to large domain?
• How to represent domain knowledge other than
  facts and procedure (i.e. concepts and mental
  model)?

Case Based Reasoning !!!!!!!!!!!!!!!
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CBR in ITS

• Represents the Student model and Domain Knowledge
  in the form of cases
• These cases can be used to train the tutorial
  system for a particular user or someone with
  similar properties as that user
• Cases
• Produced by the learner himself
• Experience from other learners
• On-demand case generation
• Predefined cases given by human tutors

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Case Based ITS- Uses of CBR

• Problem Solving phase
• Find similar problem solved in the past to
  provide learner with past experience feedback.
• Case-Based Adaptation
• Allows interactive system to adapt to a specific
  user (i.e CHEF cooking tutor).
• Can be used to adapt interface component
  depending on the users knowledge of the software
• Case-Base Teaching
• Assists the learner by providing with useful
  cases for learning new information
• Types
• Static Adaptive
• (Pre-defined case base) (adapts case base from
  learner experience)

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Methods

• Different type of CBR methods
• Classification Approach
• Systems that provide help on well known
  pre-analyzed cases
• Problem Solving Approach
• Systems that diagnose solution proposed by the
  learner and to identify the problem solving path
  used
• Systems that support planning
• Planning Approach
• Systems that support planning
• Representation of cases
• Complete cases Problem definition detailed
  solution
• Snippets or partial cases Sub goals solution
  within of problems different
  contexts

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CBITS Examples

• CBITS have been used in many different areas
• Medical CARE-PARTNER
• Project Management
• Math PAT
• Jurisprudence
• Economics
• Programming ELM-Art, SQL-Tutor,
• Chess CACHET
• Auto tutor

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Example 1 ELM-ART LISP Tutor

• Weber and Specht (1997)
• Episodic learner model
• Stores knowledge about the user in terms of a
  collection of episodes which can be viewed as
  cases.
• Every solution stated by the user is diagnosed
  completely or partially to find problem errors.
• Keeps track of what components were used and
  when.
• ELM-PE and ELM-ART - only systems that use this
  model

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ELM Architecture
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Representation of subject domain

• Consists of rules and concepts in the form of
  hierarchically organized frames
• Concepts
• comprise knowledge about
• Programming language LISP
• Common algorithms and problem solving knowledge
• Consists of
• plan transformation leading to semantically
  equivalent solutions
• rules
• Rules
• describe different ways to solve the goal stated
  by the concept
• Bug rules

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Example 2 AutoTutor

• Web-based intelligent tutoring system developed
  by an interdisciplinary research team - Tutoring
  Research Group (TRG)
• Student contributions Text box at the bottom of
  the screen.
• AutoTutor response one or a combination of
  pedagogically appropriate dialog moves conveyed
  via synthesized speech, appropriate intonation,
  facial expressions, and gestures and also text
  form on the screen.

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AUTOTUTOR- Authoring Tools

• Case-based help - a case study replicating the
  process that teacher would go through to create a
  curriculum script using the tool. The scenario
  was created through an analysis of think aloud
  protocols with actual teachers during the
  evaluation process.
• Problems and solutions with the terminology,
  interface, or concepts were used to generate the
  case study components, which were then
  incorporated into an overall composite scenario
  accessible at any time during the authoring
  process.

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Auto tutor

• Strengths
• not purely domain-specific
• easy creation of curriculum script (no
  programming skills needed)
• robust behaviour
• Weaknesses
• shallow understanding only
• performance largely depends on Curriculum Script

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Demo!!

• Elm ART
• http//art2.ph-freiburg.de/Lisp-Course
• Auto tutor
• http//rhea.memphis.edu/JSONWebService/StartFrame1
  .htm
• Auto tutor emotions
• http//wreg.com/2012/05/01/computer-technology-use
  d-as-tutor/

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Summary

• ITS give personalized instruction
• 3 main parts are
• The Student Model
• The Tutor Model
• The Domain Knowledge
• CBITS use different approach
• Case-Based Adaptation
• Case-Based Teaching (Static or Adaptive)
• Classification
• Problem-Solving
• Planning

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Research Areas

• Developing Authoring tools
• Increase modularity of ITS
• Natural language Modeling
• Emotion recognition
• Collaborative Learning

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• ITS are becoming more and more popular as a good
  assistant to human tutors
• 6 of Schools in America are using these tools to
  teach students in each and every area !

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Thank you!!