Intelligent Tutoring Systems

1. 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

2. Topics • Learning Scenarios • Domain Knowledge Representation • Student Modeling • Student Diagnosis • Problem Generation • User Interface

3. 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

4. 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

5. 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

6. 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

7. Knowledge Representation • More technologies • Simulation-base training • Constraint-base reasoning • Condition/action rules • Multiple representation viewpoints

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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