INTELLIGENT TUTORING SYSTEM

Drawbacks of CAI (compared with human tutor)

• Inability to conduct dialogues with the student in natural language;

• Inability to understand the subject being taught;• The program cannot accept unanticipated

responses;• Inability to understand the nature of the students’

mistakes or misconceptions;• Inability to profit from experience with students or

to experiment with the teaching strategy.

CAI

ICAI

ITS

Intelligent Computer Assisted Instruction (ICAI)

• concerned with developing computer systems which interact knowledgeably with learners.(Self, 1989)

• The users are able to conduct flexible and adaptive dialogue with the computer through words or graphic interfaces

• They should be allowed to access information in varying forms and from varying viewpoints as they wish

.

What is ITS•Computer-based system that can

simulate the human tutor by putting their knowledge and inference mechanisms into computer systems.

communicate with the users intelligentlymake inferences about students’ knowledge

based on what they have entereddeduce the learner’s knowledge from his int

eractions with the system as he tries to handle the educational tasks posed to him (Mandl & Lesgold, 1989)

Intelligent Tutoring System (ITS)

• A step beyond ICAI• leading to new classes of problems and approaches

and where learning is at least as important as teaching.

• Involves artificial intelligence concepts including:– Knowledge representation and communication– problem-solving approaches– dynamic student modeling– human cognition– intelligent user interfaces– intelligent help systems– use of strategies

• capture the knowledge that allows experts to compose an instructional interaction

• knowledge is explicitly represented and can be used in the system

• the program is responsible to compose instructional interactions dynamically, making decisions by reference to the knowledge provided.

識知理論建構主義

ICAI,ITS

智能電腦教學軟件 , 教學系統

自由探索 操練必須熟習部份

CAI電腦輔助軟件

Agent代理人

人工智能與資訊科技教學

人工智能

智能教學軟件與識知心理學

教師如何教授 教師如何輔助 , 改錯

理想中的電腦教師理想中的電腦教師

因應學生的程度 ,興趣 ,將內容以適當的節奏 ,方式傳授給學生 .

如學生學習過程中產生錯誤 ,能夠指出所犯錯誤,並協助改正 .

為何犯錯 如何改正

智能教學軟件

An

alyz

ing

What can I do for you?

知識如何學習 , 貯藏 , 提取教學能力是什麼 ?

學生犯錯的原因

Intelligent Tutoring System 智能教學軟件

識知心理學

電腦科學

科目知識

ITS

Existing ICAISystem Subject Matter Developed by

Scholar Geography Carbonell, 1970

BIP Programming in BASIC Barr et al., 1976

Buggy Subtraction Brown & Burton, 1978

West Arithmetic Expression Burton & Brown, 1979

Excheck Logic and Set Theory Suppes, 1981

Streamer Steamship Propulsion Willaims et al., 1981

SophieElectronic Trouble-shooting

Brown et al., 1982

Guidon Infectious Diseases Clancey, 1982

Wusor Logical Reasoning Goldstein, 1982

Integrate Symbolic integration Kimball, 1982

Existing ICAI (contd.)

Knezek G.A. (1988) The computer teacher

Spade Programming in Logo Miller, 1982

Quadratic Quadratic Equations O'Shea, 1982

LMS Algebraic Procedures Sleeman, 1982

Why Causes of Rainfall Stevens et al., 1982

Algebra Applied Algebra Lantz et al., 1983

Proust Programming in Pascal Soloway et al., 1983

Neomycin Medical Diagnosis Hasling et al., 1984

Meno Pascal ProgrammingWoolf & McDonald,

1984

Lisp Tutor Lisp programmingAnderson & Reiser,

1985

Geometry Tutor Geometry Proofs Anderson et al., 1985

Tutor Highway Code Davies et al., 1985

FGA French Grammar Brachan et al., 1986.

Architecture of an ITS

Domain ExpertDomain Expert

InstructionalExpert

InstructionalExpert

Student ModelStudent Model

IntelligentInterfaceIntelligentInterface

user

Components of an ITS• The expertise module which contains the expert's

knowledge on the domain (subject knowledge).• The student module which contains the students'

knowledge, whether correct or incorrect, about the subject domain.

• The tutorial module which contains the tutorial knowledge (knowledge about how to teach).

• The interface module which is responsible for the communication between the computer system and the student,containing Communication knowledge -- knowledge about how to communicate with the learner through the computer.

EXPERTISE MODULE

Domain ContentKB

Problem Solver:

Criterion Performance Model

INTERFACE MODULE

Display/LanguageGenerator

Input Interpreter

TUTORIAL MODULE

InstructionalStrategy KB

Diagnostic Rules

Prescriptive Rules

STUDENT MODEL MODULE

Errors/Deficiencies &Learning Needs KB

Knowledge State &Performance History

Individual Differences Variables

Fig. 1. A Schematic Representation of ICAI System (from Park, O., 1991)

KB: Knowledge Base

Interface Module –Communication Knowledge

• The conversation between the student and the computer system

• Management of student-computer interactions– Facilities for teaching– Problems, or exercises– Investigating, exploring and stimulating

• Interface design– Interaction device (hardware)– Interaction design (menus, icons, …)

Expertise Module —Domain Knowledge

• Like a human expert, domain expert has knowledge about a particular domain or content knowledge.

• Factual and procedural, and is maintained in databases by an expert system– A factual database stores pieces of information about the

problem domain, – A procedural database contains knowledge of procedures and

rules that an expert uses to solve problems within that domain• Modeling provides for a closer simulation of the human

expert's reasoning process (criterion-performance model)

• The Expert Model may employ cognitive modeling by using structured knowledge and human-like inference mechanisms –The criterion-performance model is a computer-based expert

that solves the same problem given to the student so that the system can evaluate the student's performance.

P1: IF the equation to be solved contains asubexpression of the form num(term1 + term2)

THEN set as a subgoal to distribute num overterm1 and term2

P2: IF the goal is to distribute num over term1 andterm2

THEN set the subgoal to multiply num times term1

AND set the subgoal to multiply num times term2

AND set the subgoal to combine the previousresults with +

P3: IF the goal is to multiply num times term

THEN write the product of num and term

P4: IF the goal is to combine term1 and term2 witha +

THEN write term1 + term2

Rules employed in Teacher's Apprentice

If: 1) The infection which requires therapy ismeningitis,

2) Only circumstantial evidence is available forthis case,

3) The type of the infection is bacterial,

4) The age of the patient is greater than 17 years,

5) The patient is an alcoholic,

Then: There is evidence that the organisms which might becausing the infection are diplococcus-pneumoniae (.3) and e.coli (.2).

GUIDON: an intelligent tutoring system attempted to use the expert system MYCIN (Shortliffe, 1976) to advise non-experts in the selection of antibiotic therapy for infectious diseases.

English The negative X-component of force is aportion of the x-component of force thatpoints to left.

Physics tutor: (deframe x-neg-force

(element-of x-force)

(line-of-action to-the-left))

English: The X-component of force is along the x-axis and it has a quantitativemeasurement.

Physics tutor: (deframe x-force

(element-of force)

(direction-of x-axis)

(measurement nil))

Representation of Concepts

Student Model Module Self (1988)

• P: procedural knowledge, • C: conceptual knowledge, • T: individual traits, typically a set of labels, e.g.

introvert, blind, bored, etc., describing the student.

• H: history, a transcript of the interactive session, summarized and interpreted to describe significant events.

Types of student models• Quantitative models • Qualitative models: describe objects and processes in terms of

spatial, temporal, and causal relations

– Overlay model: Student's performance is compared to that of the computer expert; part of the expert's knowledge.

– Bug identification method: The student model contains both domain knowledge as rules and misconceptions and errors (bugs) as variants of rules. In this case, the student model includes something that the expert does not have.

Expert Expert Bug

Pupil Pupil

Overlay Model Buf-identification Model

Fig. 5 Expert-based Modeling Method (Elsom-cook, 1988)

Functions of Student Models

1. corrective: to help eradicate bugs in student's model;2. elaborative: to help extend what is described in the

system (which may be considered 'correct' but 'incomplete')

3. strategic: to help initiate more significant changes in tutorial strategy than the tactical decisions of (1) and (2) above.

4. diagnostic: to help resolve the contents in student model

5. predictive: to help determine the student's likely response to tutorial actions.

6. evaluative: to help assess the student or ICAI system.

Self 1988

Limits of Student Model• Combinatorial explosion: the number of possible

combinations will be too large to be incorporated into a common computer system.

• Lack of global view: constraining the student to the smallest and analyzable step, leads to a situation that the overall understanding of the students will not be possible.

• Students' prior knowledge: Students' decision-making processes may depend on a lot of prior knowledge which may be something not directly related to the learning materials.

• Immediate learning context: Kolodner (1983) showed students attempting physics problem draw surface analogies with immediately preceding problems. This means the ITS needs to maintain an episodic memory in order better to provoke productive analogies and to understand the source of mistaken analogies (Self, 1988).

• Personal learning preferences, styles and strategies

Suggestions (Self, 1988)

• Avoiding guessing: e.g.: providing the alternative ways for students to choose.

• Don't diagnose what you can't treat: factors such cognitive styles, motivation should be added?

• Empathize with the student's beliefs, don't label them as bugs, since: – inconsistencies of mal-rules among populations and – the unsystematic nature of mal-rules. Besides, – the development of mal-rules depends much on the developers'

decisions on the levels of abstraction made to the errors. – the breakdown of the old belief that "once a bug has been accurately

diagnosed, an instructional prescription follows naturally" – It is therefore better to represent student's beliefs and that students

are provoked to consider the justifications and implications of their own beliefs.

• Don't feign omniscience -- adopt a "fallible collaborator" role: machine learning techniques should be employed to infer concepts from examples observed from the students in psychologically way that a student model describing the student's beliefs can be made. The student's model in this case is judgment free and is used for both the ITS and student to refine the student's beliefs. The ITS now becomes a collaborator instead of a tutor because instead of teaching, its role is now helping the student to elaborate those beliefs.

Teaching Knowledge

• Student Model can be thought of as containing an advanced profile of the student– infer details about the student's understanding of the

problem domain• Monitors a student's progress and provides coaching

when the student requests assistance• Instructional Environment that provides the student with

tools for proceeding through a tutorial session and obtaining help when needed

• Methods (AI) to generate answers and explanation– Intelligent computer-assisted instruction (ICAI)

TUTORIAL MODULE

• how to present the materials to be taught,

• how to discover the students' errors and how to correct them.

Knowledge in the System

• The didactic rules: The tutor's knowledge of how to teach effectively. These include the sequencing of materials to be taught; when and where in the tutoring process that the materials are to be disclosed,...

• The diagnostic rules: The knowledge of how to find out the errors of the students.

• The prescriptive rules: The knowledge of how to correct students' errors.

INTRODUCE TUTOR HACK COMPLETE

IntroduceContinuing Topic

Teach ProposeMisconception

RepairMisconception

IntroduceDown

ExploreCompetence

DescribeDomain

VerifyMisconception

CompleteTopic

PEDAGOGIC STATES

STRATEGIC STATES

Data

specific intro

generalintro

exploratoryquestion

role explora-tory question

teach specificknowledge

teach generalknowledge

teach dependentknowledge

questiondependency

explicit correctacknowledgement

implicit correctacknowledgement

emphatic correctacknowledgement

explicit incorrectacknowledgement

implicit incorrectacknowledgement

evaluateinput

is correct andelaborate

questionreflexive factor

discuss generalknowledge

discuss specificknowledge

discuss dependentknowledge

proposeanalogy

suggestexample

proposemisconception

verify

correctionclaim of truth

misconception

suggest newtopic

completetopic

repair misconception

TACTICAL STATES

Fig. 6 Discourse Management Network (DMN)for Meno-tutor

P P P

S S S S S S S

S

T T T T TT T TT TT T

TP

meta-rule

meta-rule

Pedagogic state Tactical state Strategic state

Default path Preeemption path

Fig. 7 A Possible Control Mechanism

S1-EXPLORE -- A Strategic Meta-rule

From: teach-data

To: explore-competency

Description: Moves the tutor to question the studentabout a variety of topics.

Activation: The present topic is complete, but thetutor has little confidence in its assessment ofthe student's knowledge.

Behavior: Generates an expository shift fromdetailed examination of a single topic, to ashallow examination of a number of topics.

Conclusion

• most such systems focus on domain areas such as mathematics and computer programming

• instructions are mainly on procedural knowledge and

• the domain knowledge is more restricted.

Criticisms

• seen as superficial by those who emphasizes understanding, metacognition, and personal growth as educational goals; and

• the skills that are most amenable to being rendered redundant by the advent of computer-based tools.

例子 : 電子家課

• 電腦補習老師 : 以協助學生解決家課困難 .

• 教師助手 : 協助教師收集 , 評改及紀錄學生的家課 .

電子家課

電腦知識 人類知識

教師如何協助學生改錯的知識

學生的可能錯誤

電腦知識與人類知識互換

人機介面 : 的設計

正確的學科知識

學生功課錯誤 ,紀錄 , 統計 , 分

析如何分辨練習題目的深淺

教師輸入家課題目

電子家課的使用過程

貯入磁碟分派給學生

HomeworkElectronicHomeworkElectronicHomework

Homework

ElectronicHomework

ElectronicHomework Homework Electronic

Homework Electroni cHomework

ElectronicHomework

學生在家裏在電腦導師的指導下做功課

你剛才輸入的這一段 , 由於…

的關係 , 所以有點偏差 , 請參考… ., 下面是一個例子 :

Electronic HomeworkStudent A

Electronic HomeworkStudent B

Electronic HomeworkStudent C

Electronic HomeworkStudent D

Electronic HomeworkStudent E

Electronic HomeworkStudent F

教師用電腦檢核學生家課

學生這次的表現一般還不錯 ,但在這裏稍為差一點 , 我是不是應該在下一課加強一下 .

學生家課記錄

No. NameNo. of Qs.

correctErrors

1 Student 1 7 1

2 Student 2 4 2

3 Student 3 5 1

4 Student 4 6 2

Errors Ss made this error

log(A+B)=log A + log B 1;3

No.

1

2 log(AB)=(log A)*B 2;4

學生犯錯報告

電子家課如何協助教師• 節省評分及改卷的工作• 即時產生的學生表現記錄及錯誤記錄幫

助教師了解個別學生及整體的表現 , 從而改善教學工作 .

電子家課如何協助學生• 將練習題編排 , 由淺入深 , 引發學生的學

習動機 .

• 學生個別練習時可以得到即時的診斷及回饋

• 當需要時 , 電腦可即時提供協助 .

• 電腦可按學生表現而提供適當的練習• 學生稍有犯錯 , 電腦即時作出提示 . 因此

學生可以避免學到錯誤的概念 .

電子家課背後的研究• 學生錯誤分析

– 測驗 , 面談 , 整理成為 549 誤則 (Mal-rule), 例如 log(2+3)->log2+log3

– 分類歸納成為 5 原誤則 (Primary Mal-rule), 探究原誤則形成的原因

• 協助學生改錯的研究– 根據原誤則的成因 , 加上經驗教師的意見 , 形成一方

法 (Conceptual Dissonance),– 實驗結果證明比較其他方法較優 ,

• 排序 : 實驗找出影響題目深淺的原因 , 輸入電腦系統 , 預測結果與實驗結果相若 .

解題模塊

家課評分模塊

學生錯誤模塊

專家模組題目排列模塊即時提示模塊診斷模塊

輔導模塊

人機介面

電腦導師教師助手

學生模組教導模組

傳意模組

EH

電子家課的組成部份

所用電腦語言

EH InterfaceVisual Basic:Html, JAVA

InferenceAmzi Prolog:

遇到的困難• 當前學習理論未能充份解釋學生犯錯的

原因及如何協助學生改錯• 需要大量人力物力 .

• 學習系統是否有效 ,受很多因素影響 , 要證明系統是否有效 ,並不容易 .

未來發展• 加入聲音 ,圖畫以提高部份學生的學習興趣 .

• 加深學習理論 , 錯誤發生的研究 , 以使改錯部份更有效率 .

• 包含更多的學科領域• 網絡教學

將來的網上教學 :

•電子導師•電子同學

例子

Expert System: Bird

ITS: Electronic Homework

Amzi! Internet Site Guide http://www.amzi.com/cgi-bin/amzisite

Some More Examples

• The PACT Group http://www-2.cs.cmu.edu/~pact/

• Carnegie Learning http://www.carnegielearning.com/

• REM Reflective Evolutionary Mind http://www.cc.gatech.edu/grads/m/Bill.Murdock/rem/

Bird

• Download Amzi prolog from http://www.amzi.com/download/freedist.htm

• Unzip and install :Windows: Amzi Prolog +Logic Server 5.0

• Dowload “bird.pro” and test it.

例子

771.44771.0771.4

4771.0

602.09542.0602.0

9542.0

301.02

4771.02