Intelligent Tutorial Systems

Book: HYPERMEDIA LEARNING SYSTEMSTHEORY – DIDACTICS – DESIGN

Prof. Dr. Rolf Schulmeister

Talk by:Martin Homik

For ActiveMath Lesegruppe:08.06.2005

Overview

ITS

Components Expert Systems Adaptivity

Knowledge ModelExpert Model

Learner ModelDiagnose Model

Tutor ModelPedagogical Model

Interface

Intelligent TS:• Agent Theory: perceive, reason, act• Goal: minimize gap between expert and

learner• Flexible and adaptive

Components[Barr/Feigenbaum]

Knowledge ModelExpert Model

Learner ModelDiagnosis Model

Tutor ModelPedagogical Model

Interface

ITS

Knowledge Domain(Expert Model)

Knowledge Domain

Declarative Procedural Heuristic

KnowledgeDesignModels

Black Box Glass BoxSemantic

Nets

Knowledge Domain(Expert Model)

• Declarative knowledge defines:– Terms from knowledge domain by their attributes– Relationship of the terms (by frames/inheritance)

• Procedural knowledge consists of:– Arguments/rules which help in solving problems

• Heuristic knowledge consists of:– Experience/problem-solving knowledge of experts– … not confined to particular contents

[Winograd (1975)]

Knowledge Domain Design Models

• Black box:– No reproduction of human intelligence– Example: SOPHIE I [Brown, Burton (1974)]

• Glass box [Goldstein/Papert (1977)] :– KD: modelled in the form of an expert system– Transparent: reproduces the problem-solving

behaviour of human experts

• Semantic Nets [Jonassen (1992)]:– Nodes with patterns– Typed relations

Student Model

Declarative Procedural

Knowledge Models

Subset(Overlay)

DeviationBuggy/Perturbation

Functions

Corrective Elaborating

Strategic Diagnostic

Predictive Evaluative

Student Model(Learner Model, Diagnosis Model)

Kinds of student model

• Subset model (overlay model):– The parts of the expert knowledge which the

student has done are ticked off • Deviation model

– Analyse student’s answers, conclude by way of inference what has been understood,

– … not »explaining« his learning behaviour.

Functions of SMs [Self (1988)]

• Corrective function:– find/correct student’s mistakes– follow the learner’s train of thought step by

step • Elaborating function:

– intervene if learner’s knowledge correct but incomplete

– compare the expert model with the learner’s current state of knowledge and suggest actions

• Strategic function:– change of the methodical level– provision of other learning strategies

Functions of SMs [Self (1988)]

• Diagnostic function:– find out the learner’s ideas– the system analyses the SM by itself

• Predictive function:– simulate the learner – … make predictions about behaviour

• Evaluative function:– reconstruct the learner’s learning process

Criticism on Student Models

“These approaches both imply a very simplistic model of the learning process (not far removed from rote learning), which takes no account of the rich range of learning styles and capabilities for which there is psychological evidence.“ [Elsom-Cook]

Criticism: Diagnosis Function (Models)

• Mostly: in the sense of bug recognition– Provide fix set of bugs– Register bugs in the course of the program and

then machine learn [Ohlsson and Langley (1988)]

– Problem: compound bugs, noise [see also Hennecke]

• “Neither the bug library technique nor the machine learning approach is currently used extensively in instructional computing systems” [Ohlsson (1993)].

Criticism: Learning Behaviour Gaps

• Wish: evaluate the psychological plausibility of a solution or mistake, but “there are disappointingly few psychologocal principles that can be used for that purpose.” [Ohlson/Langley (1988)]

• Individual learning styles and strategies researched by psychology play a minor role

Tutor Model(Pedagogical Model)

• Presentation of learning materials:– “What, when, how?”

• Simulates the decision behaviour of a teacher– Referring to pedagogical intervention– Generates appropriate instructions – Basis: difference between expert and student

model

Tutorial Strategies

• Socratic dialogue– Questions encourage analysis of learner’s

mistakes

• Coaching– Problems and activities for exercising skills– Trying out solutions to problems– Feedback provision

• In summary:– Tutor model follows rather the instructional

approach than the concept of discovery learning or the cognitive tool

Tutorial Gaps

• Everyday reasoning of the teacher• … his assumptions about the learning

process of the pupil or student, • .. his knowledge of the situation structure

and rules of interaction• Passive student:

– “the assumption of a given task and given expertise puts students in a passive role with respect to finding their own problems and developing their own expertise” [Bredo (1993)]

– Solution: Assembly Tool

Interface Interactions

• Socratic Dialogue– Ask questions and reason on answers

• Coaching– Analyse help requests

• Learning by Doing– System demands tasks; difference reasoning

• Learning while Doing– Tutor stays in the background– Provides occasionally help

Criticism

• Current TS are either directive or non-directive

• … but not both yet. [Elsom-Cook, 1988]– … it is by no means always the case that the

dialogue is truly Socratic”

• [Mandl/Horn] distinguish between:– Guided learning or instruction as aim

([Anderson/Reiser (1985)])– Microworld concept, with discovery learning as

aim ([Shute/Glaser et al (1989)])

Smithtown

LISP Tutor

Interface Types [Kearsley (1987)]

• Socratic dialogue• Coaching• Debugging• Microworld• Explanatory expert systems

complete control freedom of learning.

SocraticDialogue Microworld

Elsom-Cook Continuum

Natural Language Behaviour

• Simulates teacher [Mandl/Hron (1990)] • “…approach a natural language dialogue”

[Mandl/Hron (1990)] • Necessary feature of a tutorial system

[Spada/Opwis (1985)]• Linguistic Research today is much more

advanced, but “effective communication requires looking beyond the words that are spoken and determining what the tutor and student should be communicating about” [Woolf’s (1987)]

DiBi

KAVIS SUOMO

Systems or Prototypes?

• “…most systems focus on the development of only a single component of what would constitute a fully usable system” [Kearsley (1987)].

=> Systems are rather prototypes

Operationalisation of Concepts?

• Learning bases on a concept of behaviour– domain model: model of concepts (behavioural objectives) – student model: model of the student’s behavioural sequences

• In contrast to behaviourism: ITS attempts to define cognitive concepts for the domain. – Cannot avoid an operationalisation of these concepts as

behavioural objectives, if a comparison of student model and knowledge domain are to be possible

• Psychological theories [Pask, Saljö, Martin, Entwistle]:– help the educator to design and understand – cannot be operationalised in the sense of ITS– resist any reduction to if-when rules

Operationalisation of Concepts?

Problems:• Concept of understanding• Cognitive concepts [Dillenbourg/Self (1992)]

– True cognitive concepts do not exist as yet– “most of the work on learner modelling has been

concentrated on the […] behavior <–> behavioral knowledge mapping, with a relative neglect of the conceptual knowledge component”

• works of Resnick, Chabay, Larkin, Merrill, Ohlsson and others: Cognition is used in the sense of »cognitive science«

• “The major problems facing ITS design at present stem from a lack of applicable models of human learning” [Tompsett (1992), 98]

Lack of Success?

• Commercial failure [McCalla (1992a)]• Prototypes bound: particular knowledge domain

– Change of domain: effort of developing [Schulm.]– … CBT had more success than ITS [Duchastel (1992c)]

• Theoretical problems of ITS [Woolf (1987)] • Clancey (1989):

– His programs are not being used– “The effect is that our technological goals–exploring the

space of what computers can do for instruction–dominated over our educational goals.”

– … constructivist paradigm of »situated cognition«: “researchers must participate in the community they wish to influence”

GUIDON

GEO Tutor

Expert Systems

Expert Systems

Knowledge Base Inference Application

Facts

Rules

Strategies

Forward

Backward

Ask

Interpreter

Explanatory Component

Tutorial Decisions

ZEERA STAT-EXPERTGUIDON

Expert Systems

• Knowledge base (facts, rules, strategies)– Expert knowledge– Usually in logical notation– If-then rules

• Inference (forward/backward reasoning, ask)

• Applications:– Interpreter– Explanatory component – Tutorial decision about didactic strategies

Expert System vs. ITS

• … do not strive to simulate human reasoning or problem-solving

• … one cannot learn anything from expert systems, since expert systems merely acquire the necessary data by asking the users for information, and then draw their conclusions from them independently and ‘invisibly’.

• Clancey ( → ):– “… it cannot explain why a particular rule is correct, and

it cannot explain the strategy behind the design of its goal structure […] At a certain level, MYCIN is aphasic – able to perform, but unable to talk about what it knows”.

MYCIN GUIDON

Adaptivity

• Adaptive tutorial strategies:– Precond.: student model with diagnostic

functions– Determine the learner’s current level and history– Transmit this information to tutor

• Problem: Adapt to something that has not yet been fully researched by science– Bastien: concentrate on IUI– Presuppose a mental or cognitive model of user

thought processes

Adaption and Hypermedia Systems

• “Hypermedia is a non-pedagogical technology […] which must count on the student’s own intelligence for learning guidance.” [Duchastel]

• “Didactics […] are essentially goal-directed processes.” [Duchastel]

• Hypermedia ITS “provoke the student into browsing” [Duchastel]

• Schulmeister: – ITS supporting self-guided learning are still valuable

pedagogical tools– … better than giving expository instruction – … didactics should support open, exploratory,

constructive learning situations

Planned Adaptivity

• Instructional adaptivity [Duchastel]:– Not individuum-oriented– Adaption ≈ pedagogical knowledge– Adaptation to pre-imagined types of learners

invested into the program design by the designer

• Hermeneutic adaptivity [Schulmeister]:– Individuum-oriented– Learner furnishes the interpretative and the

“subject gives way“ – ITS pedagogics cannot do other than plan

adaptivity.

Planned Adaptivity

Good adaptivity → (different) learner parameters

Problems:• Combinatoric explosion• Logical consistency (if too many

parameters)• Internal consistency (parameters overlap)

Microadaption

• Adaptation to student models by different strategies in the instructional system

• Example [VanLehn (1991)]:– Explanation-based learning

• assumes complete mastery of the domain • presupposes stored knowledge can be accessed/applied

– Similarity-based learning→ Able to change the level of explanation

Problems:• Inadequate reproduction of learning processes• Cannot react to individual problems (are not

recognized by the diagnostic component)

Sierra

Adaptivity through Teaching Methods

• Not as teachers do …• … multitude of teaching methods/analyse a

multitude of learner variables:– Drill & practice – Tutorials with exercises– Interactive construction– Socratic dialogues– Exploratory learning environments

• Limits to the modification of didactic strategies: – Restricted to the knowledge domain– Restricted to observable learner behaviour

Summary

ITS

Components Expert Systems Adaptivity

Knowledge ModelExpert Model

Learner ModelDiagnose Model

Tutor ModelPedagogical Model

Interface

Understanding

• Simon and Hayes (1976):– Solving the logic of a problem, e.g. understanding the

operative structure of fractions.

• Greeno and Riley (1987):– Exclusively grasping concepts of natural science

• Why self-limitation to simple cognition levels?– “The major problems facing ITS design at present stem

from a lack of applicable models of human learning” [Tompsett (1992), 98]

• … starting point is the observation that students approach scientific problems in different ways than experts, and whose aim it is to approximate the student’s knowledge model to that of the expert.

ITS

Expert Systems and ITS

• ECAL: Elsom-Cook, O’Malley(1990)– CAL <-> ITS

• BIOMEC: Giardana (1992)– Allow discovery learning and apprenticeship– Dynamic: links between expert and student knowledge

• Physics Tutor: Jonassen, Wang– ITS with an expert system and a hypertext– Explore the practicability and generalizability of the ITS

concept

Authoring Systems

Simulators Constructive Learning Situations

Adaptivity and Control

• … control over learner?