Tutoring and Help SystemsPrevious ApproachA New ApproachIntelligent Tutoring SystemModel of Traditional ITSComponents in Tutoring SystemStudent ModelStudent Model RepresentationOverlay Student ModelOverlay With Buggy ExtensionsPedagogical ModuleLow LevelMeta-Strategy…Enter CBRTwo Goals of CBR TutoringTwo Ways to Store a CaseEpisodic Learner Model ELMELMELM Knowledge RepresentationBug RuleELM DiagnosticELM Diagnostic Cont.ELM Derivation TreeDerivation TreeSlide 25Explanation-Based RetrievalELM-Programming EnvironmentELM-Adaptive Remote TutorStatic vs Dynamic CB TeachingCase-based Chess Endgame TutorCACHET Case LibrariesRoger SchankCognitive Learning TheorySchank’s CriticismsSchank’s IdeaConclusionTutoring and Help SystemsTell me and I forget.Show me and I remember.Involve me and I understand.- Chinese proverbPrevious Approach•Used for over 20 years–Computer-based training (CBT)–Computer aided instruction (CAI)•Effective in helping learners, but do not provide the same attention a human tutor can provide–Approach to a solution–Individual problem solving styleA New Approach•Previous approaches focused on scripted information about the domain•New approach must reason about both the domain and the learner•Allowing greater versatility in systems interaction with studentsIntelligent Tutoring SystemPossesses two intelligent properties:1. Generate problem solution–Flexibility within problem domain–Able to explain errors 2. Adapt to user needs–User knowledge modelsModel of Traditional ITSComponents in Tutoring SystemDomain KnowledgeExpert ModelPedagogical ModuleCommunication ModelStudent ModelStudent Model•Used to tailor instruction for each student•Must represent the student’s knowledge with respect to domain–Choice of representation•Must store pedagogical information about the student–Student’s preferences–Problem solving style•General information–Acquisition and retentionStudent Model Representation•Typically represented with overlays•Student’s knowledge as a subset of expert’s knowledgeOverlay Student ModelStudent’sKnowledgeExpert’s KnowledgeOverlay With Buggy ExtensionsExpert’s KnowledgeStudent’s Buggy KnowledgeShared KnowledgePedagogical Module•Uses information from the student model to determine what to present to learner–New Material from the Domain–Review of Previous Topic–Feedback on Current Topic•Teaching Meta-Strategy vs. Low Level IssuesLow Level•Topic Selection–Examine student model for areas of focus•Problem Generation–Difficulty based on student’s ability level (taken from student model)–Size of question depend on granularity of domain•Feedback–How much & What kindMeta-Strategy•Implementing strategy has been a formidable problem•Ideal to have many strategies to choose from based on student model–Realistically many ITSs only have one•Difficulty in representing knowledge impedes some methods–Socratic method requires “Common Sense”…Enter CBR•Individualizing will depend on two issues1. Information about how learner solved tasks2. Using this information in subsequent tutorial decisions•Storing this information builds cases•Cases from other learners•Pre-stored cases - Pitfalls domain experts have foreseenTwo Goals of CBR Tutoring•Case-based Adaptation–Adapt interface components to the user’s needs–CBR that not only uses pre-stored cases but also stores new cases can be adapted–CHEF: Recipe and Taste•Case-based Teaching–Provide user with cases that help solve current problem–Observe user solving problem – cases can be used as a reminderTwo Ways to Store a Case1. Case is stored as a whole–Most systems use this approach–Show examples or give advice2. Case is stored as a snippet–Describes sub goals of problems within particular context–Used to find problem solving path–Application used in ELMEpisodic Learner ModelELM•Analyzes solutions (or partial solutions) to programming problems in LISP•Looks for problem solving errors and returns feedback•Used in diagnostic process•Able to return examples and remindings–EBR: Explanation-based retrievalELM•Stores user model in a collection of episodes (cases)•User code is analyzed to create a derivation tree consisting of concepts and rules•These concepts and rules are instantiations of units from the knowledge baseELM Knowledge Representation•Represented in hierarchically organized frames•Concepts–Knowledge about the language (concrete procedures and semantic concepts)–Schemata of common algorithmic and problem solving knowledge (eg recursion)•Additional information–Plan transformations for semantically equivalent solutions•Bug rules for derivations which may result from confusionBug Rule•Bug Code•Ideal CodeAppend:(APPEND “a” “bcd”)(APPEND (a) (bcd))Append:(APPEND ‘(a) ‘(bcd))ELM Diagnostic•Code is at least syntactically correct•Starts with task description related to higher concepts in the knowledge base•Most concepts have transformations describing semantically equivalent variations–Ordering of clauses or sequence of arguments•The sequence of testing transformations is determined by the student modelELM Diagnostic Cont.•A set of rules is indexed by concepts describing different ways to solve the goal–Good–Bad–Buggy•Applying a rule results in comparison between plan and student’s code•Diagnostic process is called recursively on further concepts–Results in derivation treeELM Derivation Tree•Information in tree added to episodic model–Instances of concepts and rules•Context•Transformations and argument bindings•Each concept (level) in tree creates a frame•The set of episodic frames of a particular episode constitutes a case–Can later be indexed by first frame in case to rebuild treePartial Derivation Tree:(NIL-TEST(FIRST-ELEMENT(PARAMETER?LIST)))NIL-TESTEmpty-List-Nil-Test-Rule(NULLTEST(FIRST-ELEMENT(PARAMETER?LIST)))NULLTESTUnary-Func-Rule (NULL-OP) (FIRST-ELEMENT(PARAMETER?LIST)) NULL-OP FIRST-ELEMENT Correct-Coding-Rule Unary-Func-Rule null (FIRST-ELEM-OP) (PARAMETER ?LIST) FIRST-ELEM-OP PARAMTER Correct-Coding-Rule Correct-Param-Rule car liStudent CodeSimple And:(defun simple-and(li) (cond ((null li) t) ((null (car li)) nil) (t