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Intelligent Computer-Aided Instruction: A Survey Organized Around System Components

Intelligent Computer-Aided Instruction: A Survey Organized Around System Components. Author : Jeff W. Rickel, 1989 Speaker : Amy Davis CSCE 976 (Advanced AI) April 29 th , 2002. Outline of Presentation. Why ICAI? Overview of main systems and technologies discussed in this paper

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Intelligent Computer-Aided Instruction: A Survey Organized Around System Components

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  1. Intelligent Computer-Aided Instruction: A Survey Organized Around System Components Author: Jeff W. Rickel, 1989 Speaker: Amy Davis CSCE 976 (Advanced AI) April 29th, 2002 April 29th, 2002

  2. Outline of Presentation • Why ICAI? • Overview of main systems and technologies discussed in this paper • Contributions of seminal systems to various components of ICAI systems April 29th, 2002

  3. ICAI – better than CAI • First came CAI • Fully specifies presentations • All questions and their answers • Strict flow of control • “electronic page-turning” • Need for Intelligence recognized • Rich domain knowledge (and representation) • Ability to use knowledge in unspecified ways • Individualize instruction for student April 29th, 2002

  4. ICAI – Representative of AI • No commercial ICAI systems exist • ICAI – an active research topic in AI • ICAI employs many AI techniques • Require reasoning from rich knowledge representation • Models user • Needs communication and information structures • Needs “common sense” reasoning April 29th, 2002

  5. ICAI systems (I) • WEST (R. R. Burton and J.S. Brown, 1982) • “Conquer the west” with mathematical equations that evaluate to the number of spaces you want to move. • SCHOLAR (Jaime Carbonell, 1970) • Learn geography by holding natural-language dialog with the computer. April 29th, 2002

  6. ICAI systems (II) • WHY (Stevens and Collins, 1977) • Understand rainfall, when and why it happens by holding a discussion with the computer. • SOPHIE (Sleeman and Brown, 1982) • Learn by example how to troubleshoot electronic circuits. April 29th, 2002

  7. ICAI systems (III) • STEAMER (Hollan, Hutchins and Weitzman, 1984) • Manipulate controls to a steam propulsion system to gain an understanding of how each control effects the system. • RBT: Recovery Boiler Tutor (Woolf, 1986) • Solve problems in real time on a simulated boiler. April 29th, 2002

  8. ICAI systems (IV) • WUMPUS (Goldstein, 1978) • “Hunt the Wumpus” using mathematical and logical skills • MYCIN, GUIDON • Find the likely bacterial cause for the symptoms provided. April 29th, 2002

  9. ICAI Goals • More effective computer-based tutors • More economical computer-based tutors • Reflect current state of AI research April 29th, 2002

  10. Components of ICAI systems • Learning Scenarios • Forms of Knowledge Representation • Student modeling • Student diagnosis • Pedagogical knowledge • Discourse management • Automatic problem generation • User Interfaces April 29th, 2002

  11. ICAI Learning Scenarios • Goal: Involve more senses • Retain information longer • Make student an active participant • Methods • Coaching • Socratic • Mixed-Initiative • Dialogue • Articulate Expert • Simulation • Discovery Learning April 29th, 2002

  12. Learning Scenarios: Coaching • Only give advice when needed • Coach “looks over student’s shoulder” • Offer timely but unobtrusive advice • Expose key knowledge when student’s performance plateaus • Like MS help • Common in Gaming environment (ex. WEST) • Determine if student is using correct skills • Determine when student needs guidance April 29th, 2002

  13. Learning Scenarios: Mixed Initiative Dialog • Hold conversation with student • Student responds to computer questions OR • Student initiates a line of questioning and computer answers • SCHOLAR • More reactive to student • Allows student initiative April 29th, 2002

  14. Learning Scenarios: Socratic “Education can not be attained through passive exercises such as reading or listening, but instead from actual problem solving” • Ask thought-probing questions • Require use of new knowledge • Point out gaps in knowledge • Expose misconceptions • WHY tutor April 29th, 2002

  15. Learning Scenarios: Articulate Expert • SOPHIE • Teach by example • Solve problems with student watching • Explain reasons for decisions • Demonstrate troubleshooting tactics • Then make student solve problems • Occasionally provide guidance • Force student to give rationale for choices • Students should know “Why am I doing this action?” April 29th, 2002

  16. Learning Scenarios: Interactive, Inspectable Simulation • Provide a simulation of a domain • Allow exploration of actions • See the effects of actions • No fear for real-world consequences • Potential to carry into real-life situations • STEAMER, RBT April 29th, 2002

  17. Learning Scenarios: Discovery-Based Learning • Opposite of CAI • Student explores • Micro-world emulation • Discover rules and knowledge • Full student control – driven by curiosity • Prepares student for scientific inquiry, real life research, creative thinking • Outside scope of this paper April 29th, 2002

  18. Learning Scenarios:Summary • Determines “Look and Feel” of tutoring system. • Based on student-tutor balance of control • Requires support from the Knowledge base of the system April 29th, 2002

  19. ICAI Domain Knowledge Representation • CAI: poor knowledge of their domain • Canned presentation • Canned questions • Canned answers • ICAI: More knowledge  fewer limitations • Support understanding • Allow flexibility in teaching • Knowledge is key to intelligent behavior • Way knowledge is stored dictates its use April 29th, 2002

  20. Domain Knowledge • No general form suitable for all knowledge • Challenge: • Determine types of knowledge required • Find suitable representations • Support teaching particular subjects • Forms examined • Rule Based • Script • Semantic Network • Simulation • Condition Action Rules April 29th, 2002

  21. Domain Knowledge: Rule-Based KR • Generally a failure • Miss low-level detail • Miss relations necessary for learning and tutoring • No analogies, multiple views • No levels of explanation • Need to know how rules fit together • MYCIN, GUIDON • Need knowledge + perspective to communicate knowledge to student April 29th, 2002

  22. Domain Knowledge: Scripts • WHY • Nodes  processes, events; • Edges  relations between nodes • X enables Y • X causesY • Script  partially-ordered sequence of processes and events linked by temporal or causal connections. • Hierarchy of scripts: lower levels describe causal relationships within higher levels. April 29th, 2002

  23. Domain Knowledge: Semantic Networks • Highly structured data base • Stores concepts and facts • Stores connections along many dimensions • Embeds linguistic information • Avoids storing redundant information through use of many connections • Use data base to generate questions • Common in other disciplines of AI April 29th, 2002

  24. Domain Knowledge: Simulation • STEAMER: • Mathematically simulate the steam propulsion system • Tie graphics to the simulation • SOPHIE • Propagates constraints to explain why a behavior is caused April 29th, 2002

  25. Domain Knowledge:Condition/action rules • Popular in AI • Model of human intelligence (?) • “Recognize a condition, initiate an action” • Attractive because rules are modular April 29th, 2002

  26. Domain Knowledge:Summary • One representation doesn’t work for everything. • Often need multiple representations within one problem, WHY • Must be determined by how knowledge is to be used April 29th, 2002

  27. ICAI Student Modeling • Goal: Know what the student knows • CAI: Keep a tally of correct and incorrect answers • Little adaptation to student • Methods: • Overlay modeling (Goldstein, 1977) • Buggy modeling (R. R. Burton, 1982) April 29th, 2002

  28. Student Modeling:Overlay • Represent student knowledge as some function of the teacher’s knowledge. • Allows comparison between what student knows and what student should know. • WEST, SCHOLAR, WUMPUS April 29th, 2002

  29. Student Modeling:Buggy Modeling • Include both “buggy” and correct rules which the student may be following • Allows students error to be understood • May require enumeration of all possible errors! April 29th, 2002

  30. Student Modeling:Summary • Student Modeling still very open-ended • A full discussion is beyond scope of paper • Allows computer to find reasons behind student errors – student diagnosis. April 29th, 2002

  31. ICAI Student Diagnosis • Goal: Allow student to make mistakes, capitalize on them for better learning. • Methods: • Differential modeling • Direct interpretation • Plan recognition (buggy model) • Error taxonomy April 29th, 2002

  32. Student Diagnosis:Differential Modeling • Like overlay modeling: View a student error as a shortcoming that is detected with comparison to the tutor’s knowledge. • WEST April 29th, 2002

  33. Student Diagnosis:Direct Interpretation • Remove constraints on question, until student’s answer becomes valid: • Example: “What is the capital of Texas?” “Madison” “Madison is the capital of Wisconsin.” • Reasons through a semantic net April 29th, 2002

  34. Student Diagnosis:Plan recognition • Buggy model: try to find path in the model, (correct or incorrect) leading to student’s answer • Plan recognition: finding the goals which underlie student actions • Similar to language parsing April 29th, 2002

  35. Student Diagnosis:Error Taxonomy • Classify errors into types • Example of categories: • Mission information • Lack of concept • Misfiled fact • Overgeneralization • SCHOLAR April 29th, 2002

  36. Student Diagnosis:Summary • Student diagnosis is not goal: teaching is • Most diagnosis can be made easier by asking a few more questions • Allowing student to discover own errors is more effective (Socratic) • “A little meaningful feedback goes a long way” April 29th, 2002

  37. ICAI Pedagogical Knowledge • Teachers need to know more than just their subject: they need to know how to teach. • Main problems • Lesson planning • Dealing with student errors • Production rules April 29th, 2002

  38. Pedagogy:Lesson Planning • Develop strategies for ordering topics • Decide how to present material • Decide balance of control between tutor and student April 29th, 2002

  39. Pedagogy:Dealing with student errors • Two big decisions: • Decide when to interrupt student • Decide what to say • Common ideologies: • Trap student into discovering error • Allow student to see consequences of actions • Redirect the student • Affirm correct choices April 29th, 2002

  40. Pedagogy:Summary • Just knowing the problem domain isn’t enough • Effective teachers have teaching “common sense” • Effective teachers respond to students April 29th, 2002

  41. ICAI Discourse Management • Goal: Flexibility in the tutorial discourse • CAI: Hard-code syllabus, sometimes with alternate paths • Methods: • Reactive • Incremental knowledge-building • Context dependent • Hierarchical planning April 29th, 2002

  42. Discourse Management:Reaction • “Allow responses and misconceptions of student to drive the dialog” • SCHOLAR, WHY • Have a few initial goals (WHY), and modify them as session proceeds April 29th, 2002

  43. Discourse Management:Incremental Building • Add on to student’s current knowledge • Further develop a strong base • Explore new topics • WUMPUS April 29th, 2002

  44. Discourse Management:Context Dependent • Use context to disambiguate questions, find answers • Context = Position, progress and current task of student • Object Oriented Tutoring incorporates this into a subject object April 29th, 2002

  45. Discourse Management:Hierarchical planning • PhD dissertation of Beverly Woolf, 1984 • Top-down refinement of goals • Domain independent April 29th, 2002

  46. Discourse Management:Summary • Discourse management requires knowledge • Knowledge needed not just in subject area • Authors vary in opinion of how much flexibility is best. April 29th, 2002

  47. ICAI Problem Generation • CAI: canned problems, canned answers • Hard for course author • No adaptation to student • Limited meaningful feedback • Generative CAI: programs generate new problems • Methods: • Problem-generation trees • Slot filling April 29th, 2002

  48. Problem Generation:Trees • Concept tree: • Student is at a level in the tree • Tree determines what to include in question • Use context-free grammar to form actual question April 29th, 2002

  49. Problem Generation:Slot filling • Choose a kind of problem • Example: fill-in-the-blank, multiple choice • Fill in information to problem from information in semantic net • Requires rich knowledge base April 29th, 2002

  50. Problem Generation:Summary • Tree-like structures are used for generating problems • Problems that are generated must also be solved April 29th, 2002

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