Intelligent Tutorial Systems


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Slides that explain the history of ITS systems. Based on a chapter in the book of Schulmeister.

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  • Intelligent Tutorial Systems

    1. 1. Intelligent Tutorial Systems Book: HYPERMEDIA LEARNING SYSTEMS THEORY – DIDACTICS – DESIGN Prof. Dr. Rolf Schulmeister Talk by: Martin Homik For ActiveMath Lesegruppe: 08.06.2005
    2. 2. Overview ITS Components Expert Systems Adaptivity Knowledge Model Expert Model Learner Model Diagnose Model Tutor Model Pedagogical Model Interface
    3. 3. <ul><li>Intelligent TS: </li></ul><ul><li>Agent Theory: perceive, reason, act </li></ul><ul><li>Goal: minimize gap between expert and learner </li></ul><ul><li>Flexible and adaptive </li></ul>ITS Components [Barr/Feigenbaum] Knowledge Model Expert Model Learner Model Diagnosis Model Tutor Model Pedagogical Model Interface
    4. 4. Knowledge Domain ( Expert Model) Knowledge Domain Declarative Procedural Heuristic Knowledge Design Models Black Box Glass Box Semantic Nets
    5. 5. Knowledge Domain ( Expert Model) <ul><li>Declarative knowledge defines: </li></ul><ul><ul><li>Terms from knowledge domain by their attributes </li></ul></ul><ul><ul><li>Relationship of the terms (by frames/inheritance) </li></ul></ul><ul><li>Procedural knowledge consists of: </li></ul><ul><ul><li>Arguments/rules which help in solving problems </li></ul></ul><ul><li>Heuristic knowledge consists of: </li></ul><ul><ul><li>Experience/problem-solving knowledge of experts </li></ul></ul><ul><ul><li>… not confined to particular contents </li></ul></ul><ul><li>[Winograd (1975)] </li></ul>
    6. 6. Knowledge Domain Design Models <ul><li>Black box: </li></ul><ul><ul><li>No reproduction of human intelligence </li></ul></ul><ul><ul><li>Example: SOPHIE I [Brown, Burton (1974)] </li></ul></ul><ul><li>Glass box [Goldstein/Papert (1977)] : </li></ul><ul><ul><li>KD: modelled in the form of an expert system </li></ul></ul><ul><ul><li>Transparent: reproduces the problem-solving behaviour of human experts </li></ul></ul><ul><li>Semantic Nets [Jonassen (1992)]: </li></ul><ul><ul><li>Nodes with patterns </li></ul></ul><ul><ul><li>Typed relations </li></ul></ul>
    7. 7. Student Model ( Learner Model, Diagnosis Model) Student Model Declarative Procedural Knowledge Models Subset (Overlay) Deviation Buggy/Perturbation Functions Corrective Elaborating Strategic Diagnostic Predictive Evaluative
    8. 8. Kinds of student model <ul><li>Subset model (overlay model): </li></ul><ul><ul><li>The parts of the expert knowledge which the student has done are ticked off </li></ul></ul><ul><li>Deviation model </li></ul><ul><ul><li>Analyse student’s answers, conclude by way of inference what has been understood, </li></ul></ul><ul><ul><li>… not »explaining« his learning behaviour. </li></ul></ul>
    9. 9. Functions of SMs [Self (1988)] <ul><li>Corrective function: </li></ul><ul><ul><li>find/correct student’s mistakes </li></ul></ul><ul><ul><li>follow the learner’s train of thought step by step </li></ul></ul><ul><li>Elaborating function: </li></ul><ul><ul><li>intervene if learner’s knowledge correct but incomplete </li></ul></ul><ul><ul><li>compare the expert model with the learner’s current state of knowledge and suggest actions </li></ul></ul><ul><li>Strategic function: </li></ul><ul><ul><li>change of the methodical level </li></ul></ul><ul><ul><li>provision of other learning strategies </li></ul></ul>
    10. 10. Functions of SMs [Self (1988)] <ul><li>Diagnostic function: </li></ul><ul><ul><li>find out the learner’s ideas </li></ul></ul><ul><ul><li>the system analyses the SM by itself </li></ul></ul><ul><li>Predictive function: </li></ul><ul><ul><li>simulate the learner </li></ul></ul><ul><ul><li>… make predictions about behaviour </li></ul></ul><ul><li>Evaluative function: </li></ul><ul><ul><li>reconstruct the learner’s learning process </li></ul></ul>
    11. 11. Criticism on Student Models <ul><li>“ These approaches both imply a very simplistic model of the learning process (not far removed from rote learning), which takes no account of the rich range of learning styles and capabilities for which there is psychological evidence.“ [Elsom-Cook] </li></ul>
    12. 12. Criticism: Diagnosis Function (Models) <ul><li>Mostly: in the sense of bug recognition </li></ul><ul><ul><li>Provide fix set of bugs </li></ul></ul><ul><ul><li>Register bugs in the course of the program and then machine learn [Ohlsson and Langley (1988)] </li></ul></ul><ul><ul><li>Problem: compound bugs, noise [see also Hennecke] </li></ul></ul><ul><li>“ Neither the bug library technique nor the machine learning approach is currently used extensively in instructional computing systems” [Ohlsson (1993)]. </li></ul>
    13. 13. Criticism: Learning Behaviour Gaps <ul><li>Wish: evaluate the psychological plausibility of a solution or mistake, but “there are disappointingly few psychologocal principles that can be used for that purpose.” [Ohlson/Langley (1988)] </li></ul><ul><li>Individual learning styles and strategies researched by psychology play a minor role </li></ul>
    14. 14. Tutor Model ( Pedagogical Model) <ul><li>Presentation of learning materials: </li></ul><ul><ul><li>“ What, when, how?” </li></ul></ul><ul><li>Simulates the decision behaviour of a teacher </li></ul><ul><ul><li>Referring to pedagogical intervention </li></ul></ul><ul><ul><li>Generates appropriate instructions </li></ul></ul><ul><ul><li>Basis: difference between expert and student model </li></ul></ul>
    15. 15. Tutorial Strategies <ul><li>Socratic dialogue </li></ul><ul><ul><li>Questions encourage analysis of learner’s mistakes </li></ul></ul><ul><li>Coaching </li></ul><ul><ul><li>Problems and activities for exercising skills </li></ul></ul><ul><ul><li>Trying out solutions to problems </li></ul></ul><ul><ul><li>Feedback provision </li></ul></ul><ul><li>In summary: </li></ul><ul><ul><li>Tutor model follows rather the instructional approach than the concept of discovery learning or the cognitive tool </li></ul></ul>
    16. 16. Tutorial Gaps <ul><li>Everyday reasoning of the teacher </li></ul><ul><li>… his assumptions about the learning process of the pupil or student, </li></ul><ul><li>.. his knowledge of the situation structure and rules of interaction </li></ul><ul><li>Passive student: </li></ul><ul><ul><li>“ the assumption of a given task and given expertise puts students in a passive role with respect to finding their own problems and developing their own expertise” [Bredo (1993)] </li></ul></ul><ul><ul><li>Solution: Assembly Tool </li></ul></ul>
    17. 17. Interface Interactions <ul><li>Socratic Dialogue </li></ul><ul><ul><li>Ask questions and reason on answers </li></ul></ul><ul><li>Coaching </li></ul><ul><ul><li>Analyse help requests </li></ul></ul><ul><li>Learning by Doing </li></ul><ul><ul><li>System demands tasks; difference reasoning </li></ul></ul><ul><li>Learning while Doing </li></ul><ul><ul><li>Tutor stays in the background </li></ul></ul><ul><ul><li>Provides occasionally help </li></ul></ul>
    18. 18. Criticism <ul><li>Current TS are either directive or non-directive </li></ul><ul><li>… but not both yet. [Elsom-Cook, 1988] </li></ul><ul><ul><li>… it is by no means always the case that the dialogue is truly Socratic ” </li></ul></ul><ul><li>[Mandl/Horn] distinguish between: </li></ul><ul><ul><li>Guided learning or instruction as aim ([Anderson/Reiser (1985)]) </li></ul></ul><ul><ul><li>Microworld concept , with discovery learning as aim ([Shute/Glaser et al (1989)]) </li></ul></ul>Smithtown LISP Tutor
    19. 19. Interface Types [Kearsley (1987)] <ul><li>Socratic dialogue </li></ul><ul><li>Coaching </li></ul><ul><li>Debugging </li></ul><ul><li>Microworld </li></ul><ul><li>Explanatory expert systems </li></ul>complete control freedom of learning. Socratic Dialogue Microworld Elsom-Cook Continuum
    20. 20. Natural Language Behaviour <ul><li>Simulates teacher [Mandl/Hron (1990)] </li></ul><ul><li>“… approach a natural language dialogue” [Mandl/Hron (1990)] </li></ul><ul><li>Necessary feature of a tutorial system [Spada/Opwis (1985)] </li></ul><ul><li>Linguistic Research today is much more advanced, but “effective communication requires looking beyond the words that are spoken and determining what the tutor and student should be communicating about” [Woolf’s (1987)] </li></ul>DiBi KAVIS SUOMO
    21. 21. Systems or Prototypes? <ul><li>“… most systems focus on the development of only a single component of what would constitute a fully usable system” [Kearsley (1987)]. </li></ul><ul><li>=> Systems are rather prototypes </li></ul>
    22. 22. Operationalisation of Concepts? <ul><li>Learning bases on a concept of behaviour </li></ul><ul><ul><li>domain model: model of concepts (behavioural objectives) </li></ul></ul><ul><ul><li>student model: model of the student’s behavioural sequences </li></ul></ul><ul><li>In contrast to behaviourism: ITS attempts to define cognitive concepts for the domain. </li></ul><ul><ul><li>Cannot avoid an operationalisation of these concepts as behavioural objectives, if a comparison of student model and knowledge domain are to be possible </li></ul></ul><ul><li>Psychological theories [Pask, Saljö, Martin, Entwistle]: </li></ul><ul><ul><li>help the educator to design and understand </li></ul></ul><ul><ul><li>cannot be operationalised in the sense of ITS </li></ul></ul><ul><ul><li>resist any reduction to if-when rules </li></ul></ul>
    23. 23. Operationalisation of Concepts? <ul><li>Problems: </li></ul><ul><li>Concept of understanding </li></ul><ul><li>Cognitive concepts [Dillenbourg/Self (1992)] </li></ul><ul><ul><li>True cognitive concepts do not exist as yet </li></ul></ul><ul><ul><li>“ most of the work on learner modelling has been concentrated on the […] behavior <–> behavioral knowledge mapping , with a relative neglect of the conceptual knowledge component” </li></ul></ul><ul><ul><ul><li>works of Resnick, Chabay, Larkin, Merrill, Ohlsson and others: Cognition is used in the sense of »cognitive science« </li></ul></ul></ul><ul><ul><ul><li>“ The major problems facing ITS design at present stem from a lack of applicable models of human learning” [Tompsett (1992), 98] </li></ul></ul></ul>
    24. 24. Lack of Success? <ul><li>Commercial failure [McCalla (1992a)] </li></ul><ul><li>Prototypes bound: particular knowledge domain </li></ul><ul><ul><li>Change of domain: effort of developing [Schulm.] </li></ul></ul><ul><ul><li>… CBT had more success than ITS [Duchastel (1992c)] </li></ul></ul><ul><li>Theoretical problems of ITS [Woolf (1987)] </li></ul><ul><li>Clancey (1989): </li></ul><ul><ul><li>His programs are not being used </li></ul></ul><ul><ul><li>“ The effect is that our technological goals–exploring the space of what computers can do for instruction–dominated over our educational goals.” </li></ul></ul><ul><ul><li>… constructivist paradigm of »situated cognition«: “researchers must participate in the community they wish to influence” </li></ul></ul>GUIDON GEO Tutor
    25. 25. Expert Systems ZEERA STAT-EXPERT GUIDON Expert Systems Knowledge Base Inference Application Facts Rules Strategies Forward Backward Ask Interpreter Explanatory Component Tutorial Decisions
    26. 26. Expert Systems <ul><li>Knowledge base (facts, rules, strategies) </li></ul><ul><ul><li>Expert knowledge </li></ul></ul><ul><ul><li>Usually in logical notation </li></ul></ul><ul><ul><li>If-then rules </li></ul></ul><ul><li>Inference (forward/backward reasoning, ask) </li></ul><ul><li>Applications: </li></ul><ul><ul><li>Interpreter </li></ul></ul><ul><ul><li>Explanatory component </li></ul></ul><ul><ul><li>Tutorial decision about didactic strategies </li></ul></ul>
    27. 27. Expert System vs. ITS <ul><li>… do not strive to simulate human reasoning or problem-solving </li></ul><ul><li>… one cannot learn anything from expert systems, since expert systems merely acquire the necessary data by asking the users for information, and then draw their conclusions from them independently and ‘invisibly’. </li></ul><ul><li>Clancey ( -> ): </li></ul><ul><ul><li>“… it cannot explain why a particular rule is correct, and it cannot explain the strategy behind the design of its goal structure […] At a certain level, MYCIN is aphasic – able to perform, but unable to talk about what it knows” . </li></ul></ul>MYCIN GUIDON
    28. 28. Adaptivity <ul><li>Adaptive tutorial strategies: </li></ul><ul><ul><li>Precond.: student model with diagnostic functions </li></ul></ul><ul><ul><li>Determine the learner’s current level and history </li></ul></ul><ul><ul><li>Transmit this information to tutor </li></ul></ul><ul><li>Problem: Adapt to something that has not yet been fully researched by science </li></ul><ul><ul><li>Bastien: concentrate on IUI </li></ul></ul><ul><ul><li>Presuppose a mental or cognitive model of user thought processes </li></ul></ul>
    29. 29. Adaption and Hypermedia Systems <ul><li>“ Hypermedia is a non-pedagogical technology […] which must count on the student’s own intelligence for learning guidance.” [Duchastel] </li></ul><ul><li>“ Didactics […] are essentially goal-directed processes . ” [Duchastel] </li></ul><ul><li>Hypermedia ITS “provoke the student into browsing” [Duchastel] </li></ul><ul><li>Schulmeister: </li></ul><ul><ul><li>ITS supporting self-guided learning are still valuable pedagogical tools </li></ul></ul><ul><ul><li>… better than giving expository instruction </li></ul></ul><ul><ul><li>… didactics should support open, exploratory, constructive learning situations </li></ul></ul>
    30. 30. Planned Adaptivity <ul><li>Instructional adaptivity [Duchastel]: </li></ul><ul><ul><li>Not individuum-oriented </li></ul></ul><ul><ul><li>Adaption ≈ pedagogical knowledge </li></ul></ul><ul><ul><li>Adaptation to pre-imagined types of learners invested into the program design by the designer </li></ul></ul><ul><li>Hermeneutic adaptivity [Schulmeister]: </li></ul><ul><ul><li>Individuum-oriented </li></ul></ul><ul><ul><li>Learner furnishes the interpretative and the “subject gives way“ </li></ul></ul><ul><ul><li>ITS pedagogics cannot do other than plan adaptivity. </li></ul></ul>
    31. 31. Planned Adaptivity <ul><li>Good adaptivity -> (different) learner parameters </li></ul><ul><li>Problems: </li></ul><ul><li>Combinatoric explosion </li></ul><ul><li>Logical consistency (if too many parameters) </li></ul><ul><li>Internal consistency (parameters overlap) </li></ul>
    32. 32. Microadaption <ul><li>Adaptation to student models by different strategies in the instructional system </li></ul><ul><li>Example [VanLehn (1991)]: </li></ul><ul><ul><li>Explanation-based learning </li></ul></ul><ul><ul><ul><li>assumes complete mastery of the domain </li></ul></ul></ul><ul><ul><ul><li>presupposes stored knowledge can be accessed/applied </li></ul></ul></ul><ul><ul><li>Similarity-based learning </li></ul></ul><ul><ul><li>-> Able to change the level of explanation </li></ul></ul><ul><li>Problems: </li></ul><ul><li>Inadequate reproduction of learning processes </li></ul><ul><li>Cannot react to individual problems (are not recognized by the diagnostic component) </li></ul>Sierra
    33. 33. Adaptivity through Teaching Methods <ul><li>Not as teachers do … </li></ul><ul><li>… multitude of teaching methods/analyse a multitude of learner variables: </li></ul><ul><ul><li>Drill & practice </li></ul></ul><ul><ul><li>Tutorials with exercises </li></ul></ul><ul><ul><li>Interactive construction </li></ul></ul><ul><ul><li>Socratic dialogues </li></ul></ul><ul><ul><li>Exploratory learning environments </li></ul></ul><ul><li>Limits to the modification of didactic strategies: </li></ul><ul><ul><li>Restricted to the knowledge domain </li></ul></ul><ul><ul><li>Restricted to observable learner behaviour </li></ul></ul>
    34. 34. Summary ITS Components Expert Systems Adaptivity Knowledge Model Expert Model Learner Model Diagnose Model Tutor Model Pedagogical Model Interface
    35. 35. Understanding <ul><li>Simon and Hayes (1976): </li></ul><ul><ul><li>Solving the logic of a problem, e.g. understanding the operative structure of fractions. </li></ul></ul><ul><li>Greeno and Riley (1987): </li></ul><ul><ul><li>Exclusively grasping concepts of natural science </li></ul></ul><ul><li>Why self-limitation to simple cognition levels? </li></ul><ul><ul><li>“ The major problems facing ITS design at present stem from a lack of applicable models of human learning” [Tompsett (1992), 98] </li></ul></ul><ul><li>… starting point is the observation that students approach scientific problems in different ways than experts, and whose aim it is to approximate the student’s knowledge model to that of the expert. </li></ul>
    36. 36. Expert Systems and ITS <ul><li>ECAL: Elsom-Cook, O’Malley(1990) </li></ul><ul><ul><li>CAL <-> ITS </li></ul></ul><ul><li>BIOMEC: Giardana (1992) </li></ul><ul><ul><li>Allow discovery learning and apprenticeship </li></ul></ul><ul><ul><li>Dynamic: links between expert and student knowledge </li></ul></ul><ul><li>Physics Tutor: Jonassen, Wang </li></ul><ul><ul><li>ITS with an expert system and a hypertext </li></ul></ul><ul><ul><li>Explore the practicability and generalizability of the ITS concept </li></ul></ul>ITS Authoring Systems Simulators Constructive Learning Situations
    37. 37. Adaptivity and Control <ul><li>… control over learner? </li></ul>