Intelligent Tutoring Systems

1. Intelligent Tutoring Systems Prof. Dr. Mohamed M. El Hadi Sadat Academy for Management Sciences M. M. El Hadi

2. OVERVIEW • Introduction: Concepts, Objectives and Main Topics. • Learning Scenarios and Knowledge Representation • Factors Influencing ITS • ITS Conventional Model and Main Components • Case Based Reasoning and ITS • Perfect Teacher and ITS • Development Process to Create ITS M. M. El Hadi

3. 1. INTRODUCTION:What Are Intelligent Tutoring Systems? • System that provides personalized tutoring by: • Generating problem solutions automatically • Representing the learner’s knowledge acquisition processes • Diagnosing learner’s activities • Providing advices and feedback M. M. El Hadi

4. 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 M. M. El Hadi

5. ITS - Objectives • Practising environment (learn by doing) • Provide useful feedback on a student’s answer to a problem • Model the content and the student • Allow to make inferences about a student’s knowledge in order to adapt the content. M. M. El Hadi

6. ITS Main Topics • Learning Scenarios • Domain Knowledge Representation • Student Modeling • Student Diagnosis • Problem Generation • User Interface M. M. El Hadi

7. 2. LEARNING SCENARIOSAND KNOWLEDGE REPRESENTATIONLearning Senarios • 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 M. M. El Hadi

8. 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 M. M. El Hadi

9. 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 M. M. El Hadi

10. 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 M. M. El Hadi

11. Knowledge Representation Techniques • 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 M. M. El Hadi

12. 3. FACTORS INFLUENCING ITSStudent 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 M. M. El Hadi

13. 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 M. M. El Hadi

14. 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 M. M. El Hadi

15. 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 M. M. El Hadi

16. 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 M. M. El Hadi

17. 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 M. M. El Hadi

18. 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 M. M. El Hadi

19. 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 M. M. El Hadi

20. 4. ITS CONVENTIONAL MODEL AND MAIN COMPONENTS M. M. El Hadi

21. The Three Main Components of an ITS • The Student Model • The Pedagogical or Tutor Model • The Domain Knowledge M. M. El Hadi

22. ITSs and Their Interaction M. M. El Hadi

23. The Student Model Keeps track of all information related to the learner : • Description of student behavior with regard to a specific problem • Performance concerning the material being taught • Misconceptions • Knowledge gap How long should we keep the information? M. M. El Hadi

24. The Tutor Model • Information about the teaching process: • When to review ? • When to present new topics? • What topics to teach? • Get input from the Learner model to make its decision to reflect the differing needs of each student. M. M. El Hadi

25. The Domain Knowledge • Contains the information the tutor is teaching • Most important part of the ITS • Issues: • How to represent knowledge so it easily scales up to large domain? • How to represent domain knowledge other than facts and M. M. El Hadi

26. 5. CASE-BASED REASONING (CBR)AND CBITS • To represent the Student model and Domain Knowledge • There are different sources to obtain cases: • Produced by the learner himself • Experience from other learner • On-demand case generation • Predefined cases given by human tutors M. M. El Hadi

27. Concepts of CBITS • Where CBR technique become useful ? • During the Problem Solving phase : Find similar problem solved in the past to provide learner with past experience feedback. • Case-Based Adaptation • Case-Base Teaching M. M. El Hadi

28. Case Based Adaptation • Where CBR technique become useful ? • During the Problem Solving phase : Find similar problem solved in the past to provide learner with past experience feedback. • Case-Based Adaptation • Case-Base Teaching M. M. El Hadi

29. Case-Based Teaching (Cont.) • Main goal is to provide learners with useful information (in order to understand new topics and to help during the problem solving phase). • Case-Based Teaching system are either: • Static (use given case base) • Adaptive (learn new case from learner experience) M. M. El Hadi

30. Different Types of Case-Based Reasoning • Different type of CBR methods: • Classification Approach (used to provide help on well known pre-analyzed cases) • Problem Solving Approach (to diagnose solution proposed by the learner and to identify the problem solving path used) • Planning Approach (to support planning in the system) M. M. El Hadi

31. Case Representation • As a Complete case: Problem definition + detailed solution • As Partial Case (Snippet) : Subgoals of problems + solution within different contexts M. M. El Hadi

32. CBITS In Real Life • CBITS have been used in many different areas: • Biology : INVISSIBLE (under construction) • Physics : ANDES • Math : ActiveMATH • Jurisprudence • Economics • The most popular ones are: • Programming : ELM-Art, SQL-Tutor, • Chess : CACHET But Why? M. M. El Hadi

33. Further Work of CBITS • Reduce development time and cost: • Using Authoring tools (API that would simplify programmer’s task to represent knowledge and teaching strategies) • Using Modularity of the student, tutor and domain models for future reuse • Collaborative Learning • Allowing student to interact (help) with each other while learning with an ITS • But problem concerning modeling student knowledge and defining teaching strategies M. M. El Hadi

34. SQL-Tutor • Developed in 1996 by Dr. Mitrovic from University of Canterbury, New-Zeland. • Provide a good “on-hand” practice to student discovering SQL • Teaching with example and built-in Database relations. • Useful feedback is given by the system M. M. El Hadi

35. SQL-Tutor (Cont.) M. M. El Hadi

36. Current State of Intelligent Tutoring • ITS failed to recognised the fact that knowledge has contextual component (everything does not work everywhere). • Natural intelligence of student is ignored. • ITS attempted to replace human teacher! Attempts to create a perfect teacher rather than a teacher’s tool. M. M. El Hadi

37. 6. PERFECT TEACHER AND ITS • Different roles of teacher: • ITS designer teacher • ITS implementer teacher • (personality attributes, styles, preferences …) • Many implementer teachers distrust ITS as employing beliefs of the ‘designer teacher M. M. El Hadi

38. Participants of ITS • ITS is a joint cognitive system (Dalal & Kasper, 1994) involving: • a tutoring software • a student, and • an implementing teacher. • Tutoring software contains attributes from designer teacher. M. M. El Hadi

39. Context of ITS Besides the interactional context, the environmental and objectival contexts are important for any educational system. M. M. El Hadi

40. Teacher and ITS M. M. El Hadi

41. Teacher as an Implementer of ITS • provides context (background, culture, policies..) • selects and schedules other educational technologies • manages the curriculum, and • oversees the learning progression. In the ensuing power relationship, tutor’s preferences may be more important than the learning styles of a student! M. M. El Hadi

42. Teacher as an Implementer of ITS (Cont) • Different teaching styles may result in points of divergence within the joint cognitive learning. • Clark (in press) notes: • Instructional methods, not the media cause learning. • Human brain can be overloaded by technologically delivered sensory output. • However, the situation is not so straight forward! M. M. El Hadi

43. Teacher as an Implementer of ITS (Cont) • Novice learners may benefit from richer content, but may also get distracted without directed learning. • Different teachers would constrain the learning process in different ways reflecting their teaching styles! • Where does the role of teacher fit in overall environmental context of ITS? M. M. El Hadi

44. Environmental Context of ITS M. M. El Hadi

45. Teacher as an Environmental Context • Implementer Teacher provides power relationship. • Preferences of a teacher prove to be more important than learning styles of students. • Human teacher plays very important role in the acceptance of a tutoring system. • Designer Teacher needs to take into account of implementer teacher’s preferences! M. M. El Hadi

46. Modeling Human Teacher • Human teachers may have: • different personalities • different teaching styles (born out of their traditional, progressive or vocational outlook and their own learning style) • It is not possible to envisage all the preferences of implementer teacher at design time. M. M. El Hadi

47. Human Teacher Model • We recommend a re-configurable human teacher model to be incorporated in the design of ITS: • to recognise the different teaching styles • to put on record the teaching style(s) adopted in the design, and • enable manual or automatic adaptation to suit the implementing teacher M. M. El Hadi

48. Why Explicit Record of Designer’s Teaching Style (s) • Better understanding of designer’s rationale by implementing teacher • Help in dealing with the cognitive dissonance arising from any differences in teaching styles M. M. El Hadi

49. Why Explicit Record of Designer’s Teaching Style (s) Cont. • Clear rationale behind adopted teaching strategy may also help in the student learning in less adaptive systems • Easier understanding of representations which are difficult due to cultural differences • If designer’s teaching style is unproductive in a culture, the system may be localised M. M. El Hadi

50. ITS Incremental Growth • ITS, in their current stage, cannot replace all the functions of human teacher. • Efforts should be on increasing productivity (just like initial word processors for steno-typists). • ITS designers should treat human teacher as their target user. • Human teacher model is next logical approach in that direction. M. M. El Hadi