Intelligent Tutors for All:the Constraint-based Approach

Tanja Mitrovic

Intelligent Computer Tutoring Group

University of Canterbury

Intelligent Tutoring Systems

Goal: one-to-one teaching without the expense of human tutoring

Simulate a human teacher

Problem-solving environments (learning by doing)

Based on Artificial Intelligence

Student modeling

Student modeling

System

Data about user

Student Model

ProcessesColle

cts

Adapts

Adaptation Effect

Architecture of ITSs

Domain knowledg

e

Pedagogical module

Interface

Student

Domainmodule

Studentmodeler Pedagogic

alexpertise

Communication knowledgeStuden

t Models

Learning from performance errors

Ohlsson, 1992

Declarative/procedural knowledge

Constraints as a knowledge-representation formalism

Constraints do not assert anything

Constraints encode correctness for a domain “If the relevance condition R is true,

then the satisfaction condition S ought to be true, otherwise something is wrong.”

Constraints support judgment, not inference

Learning from performance errors

Learning phases: Error detection

Error correction

How can we catch ourselves making errors? If the knowledge is there, then why the error?

If not, then how is the error detected?

CBM: domain and student modeling

Constraint-based Modeling

The space of incorrect knowledge is vast

Therefore: abstractions are needed

Represent only basic domain principles

Group the states into equivalence classes according to their pedagogical importance

Constraint-Based Modeling

Domain knowledge represented by a set of constraints

A constraint is a pattern of form <Cr, Cs>

If a solution matches the Cr then it must also match the Cs, else something is wrong

“Innocent until proven guilty” approach

Example constraints

If you are driving in New Zealand,

you better be on the left side of the road.

If the current problem is a/b + c/d,

and the student’s solution is (a+c)/n,

then it had better be the case that n=b=d.

Advantages of CBM

Very efficient computationally

No need for a problem solver

No need for a bug library

Insensitive to the radical strategy variability phenomenon

Neutral with respect to pedagogy

Implications for ITS Design: CBM

Represent the domain in terms of constraints Model the student in terms of constraints Pedagogy:

Augment student’s constraint base When should the ITS take an initiative? What to instruction to deliver?

Models of meta-cognitive skills Student’s meta-cognitive skills

CBM: Model the Student

A violated constraint implies incomplete or incorrect knowledge

Short-term student model: the set of violated constraints the set of satisfied constraints

No one-to-one mapping between problems and constraints Long-term student model:

Constraint histories (overlay/probabilistic)

CBM: Pedagogy

Constraint-based tutors function by augmenting the student’s own knowledge base

Choose practice problems that exercise constraints

Interrupt when a constraint is violated Attach feedback messages to the constraints Tell the student which constraint he/she just

violated and how

History of ICTG

SQL-Tutor Solaris (1997), Windows (1998), Web (1999)

CAPIT (2000)

KERMIT (2000)

WETAS (2002)

LBITS (2002)

NORMIT (2002)

ERM-Tutor (2003)

COLLECT-UML (2005)

ASPIRE, VIPER

J-LATTE

Thermo-Tutor

CAPIT

LBITS – elementary vocabulary

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Current work

ASPIRE, VIPER

Supporting meta-cognitive skills (self-explanation, self-assessment …)

Affective modeling and pedagogical agents

Supporting multiple teaching strategies

New ITSs