The Expert Module

Cs5034

Material preparado por: Dr. Jorge Adolfo Ramírez Uresti

Introduction

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Two places of intelligence in an ITS Knowledge of the subject domain

Pedagogical knowledge

“Humans cannot tutor effectively in a domain they are not expert, and there are also inarticulate experts who make terrible instructors.” – Anderson (1988)

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Introduction ...

3

ITSs typically are incomplete Provide only part of the instruction

Supplemented by human teachers

Ideally should have abundance of knowledge Human expert 10 years of experience

Developing the domain knowledge is: Labour-intensive

Knowledge needs to be discovered and codified

Over 50% of effort in building an ITS

Two types of domain knowledge: Knowledge about the domain itself

Knowledge about how to be proficient in the domain

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Introduction ...

4

Three options to encode knowledge Black box model

Not actually codifying human knowledge

Formulas that produce same results

Expert systems Extract knowledge from a human expert

Devising a way of codifying and applying knowledge

Applying the knowledge does not have to correspond to the way a human applies it

Cognitive models Make the expert system a simulation

Applies knowledge in the same way a human does it

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Introduction ...

5

Pedagogical effectiveness

Implementation effort

more

more

Black box

models

Expert

systems

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Relation of Expert Modules to Expert

Systems

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Expert systems

(methodology defined)

Expert module

of ITS

Black box

models

Cognitive

models

Qualitative

Process

models

Experts systems

(Criterion defined)

Expert system criterion: System that achieves high-quality performance

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Black Box Models

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Generates the correct input-output behaviour over a

range of tasks in the domain

Can be used as a judge of correctness

Internal computations are

Not available to the student

Of no use when teaching

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Black Box Models ...

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Example: SOPHIE SPICE simulator

Helped in troubleshooting circuits

Not possible to explain its decisions

Example: game of chess Brute force methods

Provides good advice on a move

Cannot explain why that is a good move

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Black Box Models ...

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Black Box used in reactive tutors

Better than nothing

Expert systems can be easily converted into tutors

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Black Box Models ...

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Issue-based tutoring

Make patterns defined on the students’ and experts’

behaviour

Attach an instruction to those patterns

Example: WEST

Student no bump and Tutor bump

Tutor interrupts with an explanation of “bumping”

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Black Box Models ...

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INPUT

(e.q., Game

Board)

STUDENT

BLACK

BOXOUTPUT

(e.q., Bump)

OUTPUT

(e.q., Count)

TUTORIAL INTERVENTIONRev. 200811

Black Box Models ...

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Issue-based tutoring...

Useful even for non Black Box models

More economical and efficient to code interventions

Not important to know details

Disadvantages

Students may think a concept should not be applied at all

Cannot explain misconceptions

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Black Box Models ...

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Surface level versus Deep Tutoring

Surface Level a) “The side-angle-side rule requires two congruent

sides and a congruent angle; you have only given

one congruent side and a congruent angle”

b) “Try to prove AB AB”

c) “To apply the side-angle-side postulate you

have to establish AB is congruent to itself. You

cannot simply assume it.”

d) “ Whenever you are trying to prove triangles

congruent it is a good idea to prove that shared

sides are congruent to themselves. This will give

you a pair of corresponding parts

Deep Level

A

C D

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Glass Box Expert Systems

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Great quantity and humanlike nature of knowledge

Knowledge acquisition is time-consuming

Useful for ITSs when the domain expert is also an expert teacher

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Glass Box Expert Systems ...

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Example: GUIDON Based on MYCIN – diagnosis of bacterial infections

450 if-then rules encode probabilistic reasoning for medical diagnosis

Uses t-rules for instruction Based on a differential between the expert’s behaviour and the

student’s behaviour

Rules are defined on the expert’s reasoning processes

Problems: Exhaustive backward search – not like humans reason

Many MYCIN rules are too complex to be taught

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Glass Box Expert Systems ...

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IF

The infection which requires therapy is meningitis

Organisms were not seen in the stain of the culture

The type of infection is bacterial

The patient does not have a head injury defect

The age of the patient is between 15 and 55 years

THEN

The organisms that might have been causing the

Infection are diplococus-pneumoniae(.75) and

neisseria-meningitidis(.74)

Typical MYCIN ruleRev. 200811

Glass Box Expert Systems ...

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IF

The number of factors appearing in the domain

wich need to be asked by the student is zero

The number of subgoals remaining to be determined

before the domain rule can be applied is equal to 1

THEN

Say: subgoal suggestion

Discuss the (sub)goal with the student in a

goal-directed mode

Wrap up the discussion of the domain being considered

GUIDON’s tutorial rulesRev. 200811

Glass Box Expert Systems ...

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Example: NEOMYCIN Different control structure on domain knowledge

Domain independent set of rules about how to use the domain rules

Current active set of hypotheses contained in a new data structure called differential

Designed to reflect characteristics of human short-term memory

Lesson: pay attention to the knowledge in the expert module and in the way it is deployed (same restrictions as humans)

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Cognitive Models

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Develop a simulation of human problem solving in a domain Knowledge is decomposed into meaningful, humanlike

components

Knowledge is deployed in a humanlike manner

Problems: Develop is time-consuming and constrained

Running a cognitive model may be slow

Decide which psychological components are essential for tutoring

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Cognitive Models ...

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Types of knowledge to tutor: Procedural

Knowledge about how to perform a task

Example: calculus, algebra

Declarative Set of facts appropriately organized to reason with them

More general and not specialized for particular use

Example: geography

Causal Allows to reason about the behaviour of a device

Example: troubleshooting

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Procedural Knowledge

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Usually a rule-based system

Production system

If-then rules matched to working memory of facts

Working memory -> short-term human memory limitations

Recognize-act cycle -> basic data-driven character of human cognition

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Procedural Knowledge ...

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Sub{} SatisfactionCondition:TRUE

L1: {}--> (ColSequence RightmostTOPcell

RightmostBottomCell RightmostAnswerCell)

COLSEQUENCE (TC BC AC) Satisfaction Condition: (Blank? (Next TC))

L2: {}--> (SubCol TC BC AC)

L3: {}--> (ColSequence (Next TC)(Next BC) Next AC))

SubCol (TC BC AC) Satisfaction Condition: (NOT (Blank? AC))

L4: {(Blank? BC)}--> (WriteAns TC AC)

L5: {(Less? TC BC)}--> (Borrow TC )

L6: {}--> (Diff TC BC AC)

Multiple column substraction skillRev. 200811

Procedural Knowledge ...

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Use of rule-based representations

Students make errors when trying to repair their procedures at impasses created by missing rules

Eliminating rules -> predicts human errors

Each rule is an independent piece of knowledge

Loss of rules corresponds to human errors

Each rule can be communicated to a student independent of total problem structure

Rules can be used to represent the student’s knowledge state – set of production rules.

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Procedural Knowledge ...

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Model tracing Observe student’s surface behaviour

Try to match student’s actions to rules firing on a rule-based system

Continue interaction based on the SM generated from the tracing

Uses: Provide immediate feedback on errors

Interrogate students about their intentions

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Declarative Knowledge

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Use when student must: Understand basic principles and facts of a domain

Reason with these generally – knows how to justify his actions

Not concerned student becomes facile at any one application of the knowledge

Knowledge base is separate from inference procedures –knowledge and control are separated

Can be combined with procedural knowledge Knowledge and control are together

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Declarative Knowledge ...

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Example: SCHOLAR

Goal: to communicate information about South American geography

Used a semantic net representation

“Close to internal knowledge structure of humans”

Nodes

Representing various concepts

Linked by various relationships – define fundamental inference processes

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STATE

-- -- -- -- --

LATITUDE

-- -- -- -- --

-- -- -- -- --

-- -- -- -- --

CONTINENT

-- -- -- -- --

-- -- -- -- --

-- -- -- -- --

COUNTRY

(SUPERC (State Independent))

(SUPERP Continent)

----------------------------------------

Examples Argentina

Bolivia Brazil ..................

Uruguay U.S. Venezuela)

URUGUAY

(SUPERC Country)

-- -- -- -- --

-- -- -- -- --

SOUTHAMERICA(SUPERC Continent)

(Countries Argentina

-------------------------

Uruguay Venezuela) Rev. 200811

Declarative Knowledge ...

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Schema representation

Contains slots

Knowledge is entered into slots

Erroneous knowledge creates “bugs”

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Declarative Knowledge ...

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Evaporation

Actors

Source: Large-body-of-water

Destination: Air mass

Factors

Temperature(Source)

Temperature(Destination)

Proximity(Source, Destination)

Functional-relationship

Positive(Temperature(Source))

Positive(Temperature(Destination))

Result

Increase(Humidity(Destination))

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Declarative Knowledge ...

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Schema representation ...

Student is supposed to have the general inference

procedures to reason about knowledge

Task: represent knowledge in a form that these procedures can be

invoked by student

Problems

Rule “If the student can use knowledge, he knows it” does

not apply

Schema must “know” how student makes inferences

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Declarative Knowledge ...

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Socratic dialogue

To understand inferences ITS needs to understand dialogues

Give the control to the tutor

Asks questions

Instruction and assessment is done through these questions

Tutor probes the student’s ability to apply knowledge

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WHY: Do you think AMAZON jungle has heavy rainfall or light rainfall?

(Ask for a prediction about a particular case)

STUDENT: Heavy rainfall

WHY: Why does the Amazon have heavy rainfall?

(Ask for prior cause)

STUDENT: Because it´s near the Atlantic

WHY: Yes, the Amazon jungle is near the equatorial Atlantic. How does

that affect rainfall in the Amazon jungle?

(Ask for intermediate cause)

STUDENT: The water in the current is warm.

•

•

•

WHY: Do you think that any place with mountains has heavy rainfall?

(Form a general rule for insufficient cause)

STUDENT: Yes

WHY: Southern California has mountains. Why doesn t Southern California have

heavy rainfall?

(Pick a counter-example for an insuffient cause)

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Declarative Knowledge ...

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IF

The student gives an explanation of one or more

Factors that are not sufficient

THEN

Formulate a general rule for asserting that the factors

given are sufficient

Ask the student if the rule is true

Rule for implementing Socratic method

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Declarative Knowledge ...

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Socratic dialogue...

Conditions of rules refer to underlying knowledge rather than

surface behaviour

Rules involve a mix of knowledge, assessment and instruction

Disadvantage: natural language

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Qualitative Process Models

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Knowledge that underlies our ability to mentally simulate and

reason about a dynamic process

Troubleshooting behaviour – reasoning through the causal structure of a

device

Reasoning about the causal structure of the world

Usually uses equations to model a device

Reasoning about the device involves tracing the constraints among the

equations

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Qualitative Process Models ...

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Used when the goal is to use knowledge for

troubleshooting

Like black boxes but can explain how it reasoned by using

formulas

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Example: LECOBA

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Domain: Binary Boolean Algebra (Basic)

Cognitive model – procedural knowledge Students learn how to simplify Boolean expresions

In order to learn, students must: Read material

See examples of problems being solved by an expert

Solve exercises

Get feedback Well done!

Tell them when they have done something different from an expert solution

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Example: LECOBA…

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A program for simplifying Boolean Expressions was developed

Allowed to simplify in several modes

Novice to expert

Returned a path to see how knowledge was applied (rules)

Allowed to apply knowledge in exactly the same way a student had done it (compare or simulate a student)

The “simplifier” allowed to assess student’s work and to demostrate expert behaviour

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Example: LECOBA …

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Simplifier was unable to explain its knowledge

Used Model Tracing

Knowledge about the domain was coded in several texts linked to the current topic Introduction

Operations with 0 & 1

Basic Theorems

Introduction to heuristics

Basic Laws

Complex heuristics

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Example: LECOBA …

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Topics explained by the tutor were very dull

Student had to read all the material

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Example: LECOBA …

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Exercises and problems were pre-defined and clasiffied in

several groups

Tutor only chose from a set of exercises and decided if

they were to be used as an example or as a problem

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Example: LECOBA …

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Tutor’s and Companion’s dialogs were hardcoded

Decision on how to talk to the student were related to

his global knowledge level

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Example: LECOBA …

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Tutor and Companion knowledge to give a justification

was pre-defined in text files

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