31 st october, 2012 cse-435 tashwin kaur khurana
TRANSCRIPT
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31st October, 2012CSE-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 learner’s knowledge acquisition processes Diagnoses learner’s 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 learner’s needs Learner’s performance evaluated as a subset of
an expert’s performance --- Drawback!!
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The Tutor Systems
Automatic Cognitive analysisPath taken by studentGoalInitial competenceLearning 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 teachingConceptsRulesAxioms 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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IndividualizationActions 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 user’s
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
conceptBug 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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•Strengths not purely domain-specificeasy creation of curriculum script (no programming skills needed)robust behaviour
•Weaknesses shallow understanding onlyperformance largely depends on Curriculum Script
Auto tutor
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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-used-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!!