1. Tutoring & Help Systems Deepthi Bollu for CSE495 10/31/2003

2. “I Have A Plan Too” Introduction General Aspects of Case Based THS Examples Perspectives ELM-Learner model Demo of ELM-ART

3. Tell me and I forget. Show me and I remember. Involve me and I understand. - Chinese proverb Take Off

4. Introduction Traced back to 1926 -Sidney L. Pressey TS - provides individualized tutoring or instruction. Each ITS must have these 3 components:  knowledge of the domain  knowledge of the learner  knowledge of teaching strategies

5. Conventional Model Of ITS

6. DRAWBACK  Limited in it’s ability to take into account the Learner’s intentions or their Personal problem solving Style.  What can the REMEDIES be then? individual info. about how much a particular learner solved tasks should be kept for a long while. This knowledge must be used in subsequent diagnoses and tutorial decisions. HOW MUCH? & HOW LONG?

7. These issues lead to what? CASE BASED TUTORING SYSTEM

8. Where are these cases obtained from?  Cases produced by the learners themselves.  Collected from experiences with other learners.  Cases generated on demand (sub cases that get created during problem solving).  Predefined Cases:Foreseen & valued as helpful by experienced tutors.

9. GENERAL ASPECTS OF CASE BASED TUTORING SYSTEMS

10. General Aspects Of CBTHS Where in THS are CBR techniques used? Problem Solving Phase. Not just limited to only that but are used in Case Based Adaptation. Case Based Teaching.

11. General Aspects Of CBTHS  Case Based Adaptation.  Adaptable Features for interactive applications.  So, What are Adaptable systems? Ex: CHEF  Case Based Teaching. Main goal-to provide cases that are useful to him/her  to understand new units of knowledge.  to support Problem Solving. Static Case Based Systems, Other systems, Adaptive Case based Teaching systems (INDIVIDUALISED REMINDINGS).

12. General Aspects Of CBTHS Types of Case Based Reasoning Methods *differ with respect to the goals of these Systems  Classification approach (Systems that provide Help )  Problem Solving approach (Systems that support Learning)  Planning approach (Systems that support Planning)

13. Reminder of the topics we learnt The Taxonomy of problem solving Analysis (interpreting the solution) Classification Possible outcomes are known in advance.Ex:Yes/No problems Diagnosis Not all the outcomes are known in advance.Ex:the brake lights Synthesis ( Problem solving / Constructing / generating the solution ) Planning Configuration

14. Reminder of the Topics we learnt Classes of Adaptation Generative solution Adaptation Transformational Analogy (Ex:CHEF) Derivational Analogy

15. General Aspects Of CBTHS 2 Different Types Of Case Representation (tutoring systems make use of both)  Complete Cases  Partial Cases (Snippets) describe Sub goals of problems and their Solutions within different Contexts. Case is sequence of Planning Decisions.

16. EXAMPLES Wide range of applications Physics, Biology, Business…….Jurisprudence Two Areas are of Special Interest. CHESS PROGRAMMING Why are these both areas of special interest? No complete Domain theories covering all aspects of these domains.

17. PERSPECTIVES Showing the examples from the user’s own learning history (how is it useful?) On-line Help Systems rely not only on introductory questionnaire. remembering the last selection user made but even does this. interpret, Store & use cases from user’s history

18. Episodic Learner Model LISP Code Diagnosis (Explanation) Derivation Tree (Explanation Structure) Task Description Domain Knowledge Learner ModelGeneralization

19.  ELM-learner model used in ELM-PE and ELM- ART that stores knowledge about user in terms of collection of Episodes (cases)  Utilized in the diagnostic process in the case of complete solutions in the case of incomplete solutions  ELM used to analyze solutions to retrieve examples and remindings to user  Construction of learner model and architecture of ELM.

20. DOMAIN KNOWLEDGE REPRESENTATION Thru a hybrid model consisting of CONCEPTS and RULES in terms of hierarchically organized FRAMES. CONCEPTS (~instance variables) comprise knowledge about programming language algorithmic and problem solving knowledge CONCEPT FRAMES (~classes) contain information about plan transformations.(?) rules to solve the goal stated by this concept. ADITIONALLY, bug rules describing errors.

21. THE DIAGNOSTIC PROCESS Starts with TASK DESCRIPTION. Concepts will have GOALS & can have TRANSFORMATIONS. ex: altering of the order of clauses. Every transformation-indexed by set of RULES. All rules within CASE are checked and hence different EXPLANATIONS. DIAGNOSIS recursively called. Results in a DERIVATION TREE built from all concepts and rules identified to explain the learner’s solution. Set of all instances (plus GENERALIZATIONS) constitutes the EPISODIC LEARNER MODEL.

22. EXPLANATION BASED RETRIEVAL OF CASES Advantage of episodic modeling-Potential to predict code that learner will produce Predictions used to search for examples and remindings useful for solving new task. Procedure……….. Generate an expected solution used by Diagnosis Component. Results in an explanation structure. Resulting explanation stored temporarily in the ELM. Computes similarity to other episodes (examples & remindings) Best match found and offered to the learner as example solution Temporarily stored case is removed.

23. Putting it all Together INTERFACEINTERFACE Comm- unication Model Learner Model Expert Model Pedagogical Model Domain Know- ledge

24. ELM-PE  to support novices learning LISP  consists of ALFRED-a Syntax driven Structure Editor Intelligent Analysis of task solutions. Example based Programming Example based Explanation  limitations of ELM-PE.  ELM-ART – www based LISP course.

25. DEMO of ELM-ART http://apsymac33.uni-trier.de:8080/elm-art/login-e

26. CACHET CAse based CHess Endgame Tutor

27. References Dr. Munoz’s lecture classes. Various sources over web Lecture notes in Case based Reasoning(Weber and Schult) THANK YOU VERY MUCH