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Intelligent Tutoring Systems. Traditional CAI Fully specified presentation text Canned questions and associated answers Lack the ability to adapt to students ICAI: intelligent computer-aided instruction Reasoning Rich representation of domain User modeling

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intelligent tutoring systems
Intelligent Tutoring Systems
  • Traditional CAI
    • Fully specified presentation text
    • Canned questions and associated answers
    • Lack the ability to adapt to students
  • ICAI: intelligent computer-aided instruction
    • Reasoning
    • Rich representation of domain
    • User modeling
    • Communication of information structures
topics
Topics
  • Learning Scenarios
  • Domain Knowledge Representation
  • Student Modeling
  • Student Diagnosis
  • Problem Generation
  • User Interface
learning scenarios
Learning Scenarios
  • The situation in which the student’s learning is to take place
  • Coaching: offer a student advice and guide him when misdirected
  • Gaming environment: combine both coaching and discovering learning
  • Socratic teaching method
  • Simulation-base training
  • Discovery learning
knowledge representation
Knowledge Representation
  • Knowledge is the key to intelligent behavior
    • The form in which we store the knowledge is crucial to our abilities to use it
    • No general form suitable for all knowledge
  • Challenge
    • determine the type of knowledge required, and suitable representation for that knowledge, to support teaching particular subjects
script representation
Script Representation
  • WHY, a Socratic tutoring system
    • Test student’s understanding of the major casual factors involved in rainfall
    • Require a representation with different levels of abstraction
  • Script
    • Nodes represent processes and events, links represent such relations as X enables Y or X causes Y
    • Each node have a hierarchically-embedded subscript
    • Roles are bound to geographic or meteorological entities in a particular case
semantic network
Semantic network
  • SCHOLAR
    • A mixed-initiative, fact-oriented system
    • Requires a highly-structured data base in which concepts and facts are connected along many dimensions
  • Semantic network
    • Nodes and links represent objects and properties
    • Generate questions, answers, errors and branching information from the semantic network of knowledge
    • Support flexible query and reasoning
knowledge representation1
Knowledge Representation
  • More technologies
    • Simulation-base training
    • Constraint-base reasoning
    • Condition/action rules
    • Multiple representation viewpoints
student modeling
Student Modeling
  • Overlay Modeling
    • student’s knowledge is viewed in terms of the tutor’s domain knowledge
  • Several approaches
    • Semantic net with nodes and links are added as they are taught
    • Stars with the expert knowledge base and annotates deviations that are subsequently discovered
    • Skill modeler: student modeled by the set of skills he has mastered
buggy model
Buggy Model
  • Fact: the novice’s error can not be explained by the expert’s knowledge
  • Buggy model employs both correct and “buggy” rules
  • To understand an error, a combination of these correct and buggy rules has to be found to produce the same incorrect answer
student diagnosis
Student Diagnosis
  • Buggy model
    • Procedural networks: partially-ordered sequences of operations
    • Answer is evaluated by search for a path through this network of skills
    • Problem:
      • The number of paths grows exponentially
      • Require an explicit enumeration of bugs
student diagnosis cont
Student Diagnosis (cont)
  • Error taxonomy
    • The knowledge of the types of misconceptions in a particular domain
  • Object-oriented approach
    • Each knowledge class inherits diagnostic capabilities from a particular Diagnoser class
problem generation
Problem Generation
  • A tree-structured decision process
    • Each level represents another decision on what to include in the problem
    • Each branch represents one alternatives
    • The branches can be augmented with probabilities
  • Semantic net
    • Encode the types of objects and relevant attributes of these objects
    • A generative procedure fill in the particulars of the problem
problem generation cont
Problem Generation (cont)
  • Problem generation, expert problem solving and student diagnosis can be viewed as a set of constraints on their solution
  • We can evaluate student answers by checking that al constraints are satisfied
  • Give student feedback on wrong answers by telling him which constraints he failed to satisfy
user interface
User Interface
  • Text generation in tutoring systems
    • Most avoid true natural language mechanisms
    • SCHOLAR incorporate rich natural language in two distinct levels: semantic and syntactic
user interface cont
User Interface (cont)
  • Natural language parsing
    • Rich natural language facilities
    • Semantic grammars: look for understandable fragments in the input
    • Using graphical or menu-based input