Tutoring and Help Systems Tell me and I forget. Show me and I remember. Involve me and I understand. - Chinese proverb

Previous 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 style

A 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 students

Intelligent Tutoring System Possesses two intelligent properties: 1.Generate problem solution –Flexibility within problem domain –Able to explain errors 2.Adapt to user needs –User knowledge models

Model of Traditional ITS

Components in Tutoring System Domain Knowledge Expert Model Pedagogical Module Communication Model Student 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 retention

Student Model Representation Typically represented with overlays Student’s knowledge as a subset of expert’s knowledge

Overlay Student Model Student’s Knowledge Expert’s Knowledge

Overlay With Buggy Extensions Expert’s Knowledge Student’s Buggy Knowledge Shared Knowledge

Pedagogical 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 Issues

Low 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 kind

Meta-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 issues 1.Information about how learner solved tasks 2.Using this information in subsequent tutorial decisions Storing this information builds cases Cases from other learners Pre-stored cases - Pitfalls domain experts have foreseen

Two 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 reminder

Two Ways to Store a Case 1.Case is stored as a whole –Most systems use this approach –Show examples or give advice 2.Case is stored as a snippet –Describes sub goals of problems within particular context –Used to find problem solving path –Application used in ELM

Episodic Learner Model ELM 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 retrieval

ELM 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 base

ELM 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 confusion

Bug Rule Bug Code Ideal Code Append: (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 model

ELM 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 tree

ELM 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 tree

Partial Derivation Tree: (NIL-TEST(FIRST-ELEMENT(PARAMETER?LIST))) NIL-TEST Empty-List-Nil-Test-Rule (NULLTEST(FIRST-ELEMENT(PARAMETER?LIST))) NULLTEST Unary-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 li Student Code Simple And: (defun simple-and(li) (cond ((null li) t) ((null (car li)) nil) (t (simple-and (cdr li))))) Derivation Tree

ELM LISP Code Diagnosis (Explanation) Derivation Tree (Explanation Structure) Task Description Domain Knowledge Learner ModelGeneralization

Explanation-Based Retrieval System generates a solution based on concepts and rules and temporarily stores this solution in case base All episodic frames that are neighbors contribute to computing weights for similarity Most similar case is retrieved (based on previous explanations) and temporary solution is deleted

ELM-Programming Environment Intelligent analysis of task solutions –Diagnostic tool based on ELM –Gives user feedback on purposed solution –Directs user with hints Example-based Programming –Can reuse code from pre-installed cases or the user’s own previous experience Example-based Explanation –Shows examples based on matching of expected solution with previous cases already in learner model

ELM-Adaptive Remote Tutor HTML Implementation of ELM-PE Conceptual network of topics –Red light, green light Example-based programming –Can find the most relevant example from case history Demonstration of ELM-ART –

Static vs Dynamic CB Teaching Static –Problem design facilitates the diagnosis of failure –Cases (failures) are associated with supporting case to help overcome failure –Limited by case-base Dynamic –Problems solved twice, by learner and system –System solution used as an index for supporting cases Model Tracing –Similar to dynamic, but solution used for direct feedback (could limit multiple solution paths)

Case-based Chess Endgame Tutor Dynamic Teaching Chess heuristics are not given, instead must be inferred –First given examples to watch –Next examples to solve CACHET structures this learning by recognizing sub optimal moves and providing hints that lead in the right direction

CACHET Case Libraries Pre-defined cases –Prototypical informative games Cases generated on demand –Able to generate scenarios for learner Cases produced by the learners themselves –Self-generated cases are very successful for remindings –Useful to system as point of intervention

Roger Schank President and CEO of Socratic Arts Founder of Institute for the Learning Sciences Research on AI and cognitive learning theory Focus on e-learning

Cognitive Learning Theory A general approach that views learning as an active mental process of acquiring, remembering, and using knowledge. Learning is evident by a change in knowledge which makes a change in behavior possible. Learning itself is not directly observable.

Schank’s Criticisms Schools act as if learning can be disassociated from doing Schools believe they have the job of assessment as part of their natural role Schools believe they have an obligation to create standard curricula Schools believe studying is an important part of learning Schools believe students have a basic interest in learning whatever it is schools decide to teach them

Schank’s Idea Case-based reasoning: Understand the universe by matching incoming events to past experiences –The Steak and the Haircut Knowledge is built on the ability to index and make sense of cases –It is not a set of facts! You must question to learn

Conclusion CBR can effectively be applied to enhance tutoring systems Cases can be complete or snippets Cases can include buggy information Cases are applied either diagnostically or adaptively ELM-PE and ELM-ART use cases diagnostically forming a derivation tree