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

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Presentation on theme: "Prof. Dr. Mohamed M. El Hadi Sadat Academy for Management Sciences M. M. El Hadi 1 Intelligent Tutoring Systems."— Presentation transcript:

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

2 OVERVIEW M. M. El Hadi 2 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

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

4 Intelligent Tutoring Systems M. M. El Hadi 4 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

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

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

7 2. LEARNING SCENARIOS AND KNOWLEDGE REPRESENTATION Learning Senarios M. M. El Hadi 7 The situation in which the students 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

8 Knowledge Representation M. M. El Hadi 8 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

9 Script Representation M. M. El Hadi 9 WHY, a Socratic tutoring system Test students 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

10 Semantic Network M. M. El Hadi 10 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

11 Knowledge Representation Techniques M. M. El Hadi 11 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

12 3. FACTORS INFLUENCING ITS Student Modeling M. M. El Hadi 12 Overlay Modeling students knowledge is viewed in terms of the tutors 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

13 Buggy Model M. M. El Hadi 13 Fact: the novices error can not be explained by the experts 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

14 Student Diagnosis M. M. El Hadi 14 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

15 Student Diagnosis (Cont.) M. M. El Hadi 15 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

16 Problem Generation M. M. El Hadi 16 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

17 Problem Generation (Cont.) M. M. El Hadi 17 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

18 User Interface M. M. El Hadi 18 Text generation in tutoring systems Most avoid true natural language mechanisms SCHOLAR incorporate rich natural language in two distinct levels: semantic and syntactic

19 User Interface (Cont.) M. M. El Hadi 19 Natural language parsing Rich natural language facilities Semantic grammars: look for understandable fragments in the input Using graphical or menu-based input

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

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

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

23 The Student Model M. M. El Hadi 23 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 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?

24 The Tutor Model M. M. El Hadi 24 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.

25 The Domain Knowledge M. M. El Hadi 25 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

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

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

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

29 Case-Based Teaching (Cont.) M. M. El Hadi 29 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: oStatic (use given case base) oAdaptive (learn new case from learner experience)

30 Different Types of Case-Based Reasoning M. M. El Hadi 30 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)

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

32 CBITS In Real Life M. M. El Hadi 32 CBITS have been used in many different areas: oBiology : INVISSIBLE (under construction) oPhysics : ANDES oMath : ActiveMATH oJurisprudence oEconomics The most popular ones are: oProgramming : ELM-Art, SQL-Tutor, oChess : CACHET But Why?

33 Further Work of CBITS M. M. El Hadi 33 Reduce development time and cost: Using Authoring tools (API that would simplify programmers 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

34 SQL-Tutor M. M. El Hadi 34 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

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

36 Current State of Intelligent Tutoring M. M. El Hadi 36 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 teachers tool.

37 6. PERFECT TEACHER AND ITS M. M. El Hadi 37 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

38 Participants of ITS M. M. El Hadi 38 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.

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

40 Teacher and ITS M. M. El Hadi 40

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

42 Teacher as an Implementer of ITS (Cont) M. M. El Hadi 42 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!

43 Teacher as an Implementer of ITS (Cont) M. M. El Hadi 43 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?

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

45 Teacher as an Environmental Context M. M. El Hadi 45 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 teachers preferences!

46 Modeling Human Teacher M. M. El Hadi 46 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.

47 Human Teacher Model M. M. El Hadi 47 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

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

49 Why Explicit Record of Designers Teaching Style (s) Cont. M. M. El Hadi 49 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 designers teaching style is unproductive in a culture, the system may be localised

50 ITS Incremental Growth M. M. El Hadi 50 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.

51 7. DEVELOPMENT PROCESS TO CREAT ITS M. M. El Hadi 51 Select Domain Choose Learning Environment Choose ITS Model Interface Learning Environmen t and ITS Author ITS Create Problems Validate ITS Deploy ITS Evaluate ITS

52 Main Stages of ITS Development Process M. M. El Hadi 52 Phase 1: Select Domain: - Choose domain where problem solving plays a major role. - Building an ITS requires high investment of efficient developers and their expertise. Phase 2: Choose ITS Model - Most popular models are cognitive tutors (Model Tracing, Knowledge Tracing) [Developed by Carnegie Mellon University, Pittsburgh, PA] and Constraint Based Tutor [Developed by Conterbury University, New Zeland] - Choose CT to teach procedural skills. - Choose CBT to teach open-ended domains.

53 Main Stages of ITS Development Process (Cont.) M. M. El Hadi 53 Stage 3: Choose Learning Environment: - Student interface has to be simple and adapted to the domain (reduce memory load, motivate, etc.) - A lot of learning environments exist already and can be reused) Stage 4: Interface Learning Environment (LE) and ITS: - Convert solution from learning environment into format required by ITS. - Possibly integrate LE into ITS to make it Web accessible. - Convert student solution to the XML web language format required by ITS. - Convert Java language application to Java applet required by Web

54 Main Stages of ITS Development Process (cont.) M. M. El Hadi 54 Stage 5: Author ITS: - Existing ITS Examples: * WETAS: a development environment authoring tool done through text files. WETAS is a domain model (knowledge base) * ASPIRE: a deployment environment authoring tool (ASPIRE Tutor). It guides author in the development process and considered a semi-automated process for building the domain model. - Constraint Based Tutor Model domain with constraints * IF X (relevance condition) is true Then Y (satisfaction condition) has to be also true - Feedback generated when: * Relevance condition is true * satisfaction condition is false

55 Main Stages of ITS Development Process (cont.) M. M. El Hadi 55 Stage 6: Validate ITS: - Test the knowledge base (constraints) - Test if generated feedback is appropriate. Stage 7: Deploy ITS: - Make ITS available to students Stage 7: Evaluate ITS: - Identify aspects to be evaluated (example: effectiveness of the feedback). - Use real students learning the domain - Pre- and post – test - Comparison group (uses full version of ITS) - Control group (uses version with no feedback).


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