1 Adaptive Educational Environments for Cognitive Skills Acquisition Ashok Patel Director, CAL...

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Adaptive Educational Environments for Cognitive

Skills AcquisitionAshok PatelDirector, CAL ResearchDe Montfort University

Leicester, United KingdomTel/Fax: +44 116 257 7193 [email protected]

KinshukInformation Systems Dept, Massey University

Palmerston North, New ZealandTel: +64 6 350 5799 Ext 2090 Fax: +64 6 350 5725

[email protected]

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Introduction

• Adaptive educational environments for cognitive skills in applied domains

• Accommodation of both the ‘instuction’ and ‘construction’ of knowledge

• Tutorial aims to provide better understanding of design principles for development of such environments

• Design based on informed educational methodologies

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Introduction

Who are we?

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Aspects of learning facilitated by adaptive

educational environments

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Major use of adaptive educational environments

Life long learning

Life long learners

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Life-long “learning”

• Continuous non-formal and informal learning activities (during and beyond student life!)

• Motivation to learn is typically problem focused

• Observation and practice are key elements.

• Starts from birth!!

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Life-long “learners”

• Includes practitioners who have to learn continuously due to rapid changes in the technology employed for their work (just-in-time, learning-on-demand)

• Learners generally do not have direct contact with subject experts

• Learners traditionally pursued distance learning avenues.

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Life-long learning and computers

• Absence of human experts requires some kind of ‘filling the gap’

• A range of contemporary technological hardware, software and systems are sought to impart the domain competence to the learners

What is domain competence!

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Domain competence

=

Domain Knowledge

+

Skills (Cognitive + Physical)

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Constituents of Domain Competence

Know-whyKnow-how

Know-how-not Know-why-not

Know-when

Know-when-not

Know-what

logical processes

Know-about

Easier tolearn from mistakes

An example of the know-how aspect of know-when is the temporal context required for an appropriate sequence of operation

An example of the know-why aspect of know-when is the environmental and behavioural contexts required for making a decision

Action oriented and experiential

Reflection oriented and abstract

Difficult tolearn from mistakes

Trial and error

Context oriented and both experiential and abstract

Awareness oriented

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Know-how It has an operational orientation. It is mainly action-driven and hence pre-

dominantly experiential. It is difficult to inherit it from someone

else’s experience.

Know-how-not Learning from mistakes.

Examples : Computer simulation and virtual reality

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Know-why It has a causal orientation. It is mainly reflection-driven and therefore

based on abstraction. It can be inherited from someone else’s line

of reasoning.

Know-why-not Logical processes. Needs deeper reflection.

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Know-when (and -where)

It has a contextual orientation.

It provides the temporal and spatial context

for both the know-how and know-why. It is

thus both action and/or reflection driven.

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Know-about

It has an awareness orientation.

It includes above three types of knowledge in

terms of know-what.

It also contains information about the

environmental context of this knowledge.

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How about computers?

Discussion

Which constituent of knowledge can be successfully facilitated through computers?

Know-how Know-whyKnow-when Know-about

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Know-how (skill based) is easier to learn from making mistakes. It requires less reflection and hence most suitable for

computer based learning.

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Ideally, an instructional system, designed for novice users, teach all knowledge constituents.

But, know-why is difficult to handle mainly for two reasons:1. It needs natural language interaction.2. It needs use of metaphors, which are difficult to

understand for a novice user.

Know-how, on the other hand, is operational, and can be conveyed to the user more easily, even with symbolic representations.

Instruction in knowledge context

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Traditional hypermedia based ITSs approach, in general, has been to teach the know-why aspect of knowledge with the help of explanations.

The links provide stimulus to the user to know more about a particular topic.

System works more as a friendly librarian and learning depends on the initiative of a student.

Instruction in knowledge context

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TLTP Byzantium has used symbolic representations to explain the concepts by taking advantages of precise and concise mathematical notations.

Various features of the approach are: Bottom-up approach No centralised knowledge Each Intelligent Tutoring Tool (ITT) is free

standing

TLTP Byzantium approach

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The Byzantium principles use computers and human for what they are

(currently) good at

employ useful software tools in the overall learning environment consisting of human teachers and education technologies

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The Byzantium principles

add applied intelligence to the software tools to provide a degree of support to students, enabling them to work by themselves

understand the economics of the learning environment and be concerned with assessment and course management as they consume substantial human resource

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Knowledge levels

1. Introductory application level, where the formation of a mental map of interrelated conceptual atoms takes place and the students learn how to use the basic tools of a discipline.

2. Advanced application level, where the vertical and horizontal integration takes place.

3. Actual application approximation level, where an attempt is made to stimulate the real world situation and students learn to understand the behavioural and environmental contexts of the procedural knowledge.

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Mixed-initiative systems All the knowledge is stored in two-fold

knowledge base Not intended to replace the human tutor, but to

supplement To be mixed and matched with other technologies

(e.g. video) Suitable for various configurations such as

classroom, open and distance learning

Intelligent Tutoring Tools (ITTs)

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A network of inter-related variables where the whole network remains constant.

Example, partial network of 7 out of a total of 14 variables in marginal costing.

Structure of an ITT

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Marginal costing relationships

R

VT CT

VU

Q

CU

R = VT + CTR = Q * P

P

CT = R - VTCT = Q * CU

Q = VT / VUQ = CT / CUQ = R / P

CU = CT / QCU = P - VU

VU = VT / QVU = P - CU

VT = R - CTVT = Q * VU

P = R / QP =VU + CU

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Student-ITT Interaction No fixed sequence of filling in the

variables.

Any value can be entered in any variable, provided the whole network remains consistent.

Correct values are accepted even if the intermediate values are missing.

On entry of a wrong value and missing intermediate values, the student is advised to carry out necessary intermediate step(s).

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Granularity Negotiation Provision of intermediate variables in

the interface.

Multiple screen interface where one screen carries forward the aggregation of the details in the previous screen.

Functional interface that zoom-in to show the constituents of a complex value presented as a single variable on the main interface e.g. formula in the Capital Investment Appraisal ITT.

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Structure of an ITT

Inference Engine

Context based link to textual

description

User Interfacemodule

FileManagement

Input (student answer, position)Feedback

(four levels)

Knowledge Base1. Variables2. Relationships3. Tolerances

Modes- Student- Lecturer- Administrator

RandomQuestionGenerator

DynamicMessaging

System

TutoringModule

Expert Model1. Correct values2. Derivation procedure(Local expert model)

Student Model1. Student input2. Value status (filled or blank)3. Derivation procedure4. Interface preferences

Add-ons1. Calculator2. Table Interface3. Formula Interface

}Application specific

MarkerLecturer’s model answer to

any lecturer generated narrative questions

(Remote Expert Model)

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Tutoring Strategy of an ITT

Introduction of complexity in phased manner

Corrective, elaborative and evaluative aspects of student model are used for tutoring.

Learning process is broken down to very small steps through suitable interfaces.

‘Road to London’ paradigm is adopted to eliminate the need for diagnostic, predictive and strategic aspects.

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Feedback from tutoring module

If the student input is correct If the student input is incorrect:

If the value can be derived directly from the work done by the student and/or from the given information

If enough information isn’t available to directly derive the value

Context sensitive messages are generated to improve semantics and to prevent monotony. There are several possibilities:

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Evaluation Independent evaluation of

Byzantium and another CAL package at University of Glasgow over three years period (Stoner & Harvey, 1999)

Student performance improved significantly since introduction of CAL

Improvement appeared to be mainly reflected in the students’ ability to complete numeric questions.

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Evaluation: Student Feedback

“Byzantium was useful because you could go over bits you were unsure about. It was better than a book because it was interactive. With the interactive questions you tend to pay more attention than you would to a book.”

“Byzantium offers instant feedback, is more involving and you can do as many questions as you like.”

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Evaluation: Student Feedback

71% students showed a preference for Byzantium material while 8% indicated no particular preference.

The students wanted more tutoring systems like Byzantium, observing that it was good to use CAL if the tutoring software was good.

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Future: Integrating the ITTsThe ITTs can be integrated in two directions:

Vertical integration will allow holding and comparing results of different instances of an ITT, e.g. comparing four different springs.

Horizontal integration will allow use of multiple ITTs to solve a given problem, e.g. design of a typical safety valve using spring design, screw design and other ITTs.

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Integrating the ITTsAn individual ITT is, thus, an autonomous entity, connected in a decentralised network to make more sophisticated tutoring system.

The integration can be achieved either:

using an intelligent advanced application level interface

or

by software agents who would approach the ITTs searching for a solution of the problem in hand.

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How this approach helps in cognitive skills?

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Domain Skills

Senso-motoric skills are externally visible. They can be easily acquired by visualising the processes and learn through observations.

Cognitive skills run inside human mind. Their non-visible nature demands a more sophisticated learning process.

Life long learners have more difficulty in acquiring cognitive skills!

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What is the best suitable theoretical framework for

facilitation of cognitive skills?

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Cognitive apprenticeship framework

Modelling: Learners study the task pattern of experts to develop own cognitive model

Coaching: Learners solve tasks by consulting a tutorial component of the environment

Fading: Tutorial activity is gradually reduced in line with learners’ improving performance and problem solving competence

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Phases of Cognitive apprenticeship

1. World knowledge (initial requirement)

2. Observation of interactions among masters and peers

3. Assisting in completion of tasks done by master

4. Trying out on own by imitating

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5. Getting feedback from master

6. Getting advise for new things on the basis of results of imitation, comparing given solution with alternatives

7. Reflection by student, resulting from master’s advice

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8. Repetition of process from 2 to 7 Fading out guidance and feedback Active participation, exploration

and innovation come in

9. Assessment of generalisation of the tasks and concepts learnt during repetition process

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Cognitive apprenticeship

Vs

Guided discovery(guidance/instruction; observation of

demonstrations to understand the concepts, exploration within limited boundaries, guidance/feedback …)

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Example systems

Cognitive apprenticeship based learning environment (CABLE)

Interactive simulations based learning environment (InterSim)

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CABLE

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Environment should facilitate:

acquisition of basic domain knowledge;

application of the basic domain knowledge in non-contextual and contextual scenarios to get skills of the discipline; and

generalisation of the domain knowledge to get competence of applying it in real world situations.

CABLE objectives

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CABLE architecture Observation - for acquisition of the concepts

Simple imitation - skills acquisition through articulation of the concepts

Advanced imitation - for generalisation and abstraction of already acquired concepts and for acquisition of skills of applying concepts in different contexts

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CABLE architecture Contextual observation - for deeper learning after imitation process results into the identification of gaps in learner’s current understanding of the domain knowledge

Interpretation of real life problems - for acquiring competence in such narrative problems as encountered in real life situations

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CABLE architecture

Mastery in skills - for repetitive training

Assessment - for measurement of overall progress

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CABLETeacher generated

contextual problemsfor generalised

learning & testing

Teacher generatedcontextual problems for

strongly situatedlearning & testing

System generatedproblems - random

selection of variables

Teacher generated richnarrative problems with model

answers to simulate real lifeconditions

Descriptive text,illustrations andsolved examples

Use offine-grained

interfaces

Fine-graineddynamicfeedback

“Why ?” explanation for the system

recommended solution

“What did I do ?”diagnosticfeedback

Tools of the Trade

Assessment

Intelligent Tutoring Tools

Listen/ Observe Domain’s

concepts andtheir purpose

Interactive Learning Rehearsing/repairingmisconceptions and

missing concepts

Testing Abstract

orSingle context

Testing Multiple contexts

and/orRich narrative

Extending Greater complexityBuilding skills inthe use of tools

Learning by syntactic mapping of interfaceobjects is possible

Ensures generalisation and far transfer ofknowledge

Instruction as themain source

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Observation phase

CABLE

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Interactive messages: dialogue,

feedback and guidance

Problem Space:Students have to fill in the remaining

values

Icon showing Interactive

learning mode

Control Panel Icon showingsystem generated problem mode:

generated values given in grey

Interactive learning and testing phase

CABLE

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Fine grain interface and contextual help

CABLE

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Context based problem creation facility

CABLE

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CABLE Demo

Future work on mental process modelling

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InterSim

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Analytical disciplines focus on domain knowledge while task oriented disciplines emphasise more on skills.

Task oriented medical profession requires core knowledge of medical concepts, senso-motoric skills in physical tasks (such as surgery), and cognitive skills in diagnosis of diseases and other decision making tasks.

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InterSim learning environment is an exploratory learning environment with individual intelligent assistance geared towards facilitating core knowledge and cognitive skills.

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Educational framework

InterSim environment adopts Cognitive Apprenticeship framework (Collins, Brown & Newman, 1989) to support domain knowledge while focusing on skills acquisition. Accordingly, the environment supports following activities:

• Modelling• Coaching• Fading

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Characteristics of InterSim environmentThe InterSim environment supports:

• learning in and outside educational institutions where the constraints of space, time or resources limit the possibility of regular institutional access

• different configurations of learning such as classroom based, open and distance learning.

• acquisition of cognitive competence in both knowledge and task oriented performance.

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InterSim system for Ear domainAims: Competence acquisition by the

learner in basic and advanced knowledge of the domain and associated cognitive skills.

Target audience: Medical students and doctors in continuing medical education.

Additional features: Knowledge sharing among doctors by adding and documenting real cases of the domain within the system.

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Main Functional States in InterSim

Immediate (dynamic) feedback on user actions with a view to facilitate active learning.

Coarse grained instruction dominated learning: Basic knowledge of subject matter and overview of the domain with the help of tutoring and guided tours.

Fine grained knowledge construction: Advanced understanding of specific areas with the help of intelligent interactive simulations.

Learning Assessment Case Authoring

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Main Functional States in InterSim

Cognitive skills development: In a constructivist manner through repetitive learning.

Application of the acquired knowledge and skills: Identifying and correcting any misconceptions acquired or gaps left in earlier learning, with the help of domain problems, including notoriously difficult cases recorded in the field, for the learner to attempt diagnosis and solution.

Learning Assessment Case Authoring

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Main Functional States in InterSimLearning Assessment Case Authoring

• Assesses the knowledge and skills acquired within the Learning State without providing any assistance.

• Assessment of acquisition and retention of the knowledge and skills

• Assessment by comparing learner’s solution with master solution.

• Delayed (static) feedback

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Main Functional States in InterSimLearning Assessment Case Authoring

Authoring facilities for doctors/teachers to add real life cases.

These cases can be accessed for learning and assessment purposes.

Teachers can employ the documented cases to provide case-based teaching away from the system, if so desired.

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Intelligent assistance

Intelligent assistance and adopts adaptive behaviour according to learners’ needs.

The system adopts exploration-space-control (ESC) methodology to facilitate the exploration of learning space with reduced cognitive overload.

Within the Assessment State, ESC assists in selecting suitable problems to match the individual learner’s level of learning.

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Dynamic messagesDynamic messages adopted to the situation and learners’ current needs.

Handling messages:Context adaptive navigational aids to help learners identify various interactions available in the system. Increases amount of ‘useful exploration’.

Learning messages:Subject domain related messages, driven by the learner interactions, based on learner’s state of knowledge and competence.

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InterSim Demo

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What exactly we mean by

Adaptivity

in

Adaptive Educational Systems?

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“Intelligence”/adaptivity

Increased user efficiency, effectiveness and satisfaction

by

Improved correspondence between learner, goal and system

characteristics

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Need for “Intelligence”/adaptivity

Users generally work on their own without external support.

System is used by variety of users from all over the world.

Customised system behaviour reduces meta-learning overhead for the user and allows focus on completion of actual task.

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Adaptable SystemsSystems that allow the user to change certain system parameters and adapt the

system behaviour accordingly.

Adaptive SystemsSystems that adapt to the users automatically based on system’s assumptions

about user needs.

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How does adaptivity work? System monitors user’s action

patterns with various components of system’s interface.

Some systems support the user in the learning phase by introducing them to system operation.

Some systems draw user’s attention to unfamiliar tools.

User errors are primary candidate for automatic adaptation.

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Levels of adaptation Simple: “hard-wired”

Self-regulating: monitors the effects of adaptation and changes behaviour accordingly

Self-mediating: Monitors the effects of adaptation on model before putting into practice

Self-modifying: Capable of chaging representations by reasoning about the interactions

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Problems in adaptation

User is observed by the system, actions are recorded, giving rise to data and privacy protection issues.

Social monitoring becomes possibility.

User feels being controlled by the system.

User is exposed to adaptation concept favoured by the designer of the system.

User may be distracted from the task by sudden automatic modifications.

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Recommendation for adaptive systems Means for user to (de)activate

adaptation

Offering adaptation in the form of proposal

User may define specific parameters used in adaptation

Giving user information about effects of adaptation hence preventing surprises

Editable user model

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Content based adaptation

Multiple Representation Approach

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Multiple Representation Approach

Adaptation for multimedia based systems

Presents multimedia objects (such as audio, pictures, animations) into a multimedia interface world where the relationships of the objects to the world are governed by the educational framework.

Tasks in cognitive skills acquisition and related

multimedia objects

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Components of MRA

Multimedia object selection

Navigational object selection

Integration of multimedia objects

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Task specificity and learner’s competence Different multimedia objects are suitable

for different tasksAudio is good to stimulate

imaginationVideo clips for action informationText to convey detailsDiagrams are good to convey ideas


Components of Multiple Representation Approach

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Task specificity and learner’s competence Level of learner’s domain competence in

the current situation should be considered

Curriculum should follow a granular structure to allow assessment on individual units

This will ensure context based selection of multimedia objects



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Task specificity and learner’s competence Granularity in domain content in two

dimensions:Advancement in curriculum (e.g.

initially an abstract concept using animation of a concrete instantiation of the concept, followed by more complex abstract representation)

Details of the content (.g. static diagram at novice level, and VR with full complexity at advanced)



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Task specificity and learner’s competence

Multimedia object selectionComponents of Multiple Representation

Approach

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ExpectationsExpectations of learner and of domain

about representation of the tasks should be considered

If they don’t match, possibly provide presentations in more than one form to suit all expectations (e.g. learner wants overview by graphic, but domain requires textual details, give both)

Multimedia object selectionComponents of Multiple Representation

Approach

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Reference & revisits of already learned domain content

“revisiting the same material, at different times, in re-arranged contexts, for different purposes, and from different conceptual perspectives is essential for attaining the goals of advanced knowledge acquisition”.

(Spiro et at., 1991)



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Reference & revisits of already learned domain contentMRA favours revisiting the same domain

content in different contexts.Use of similar multimedia objects is

favoured since it puts less cognitive overload on the user.



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Reference & revisits of already learned domain contenta. enforces links between current concept

and the referred oneb. enhances the mental model of previous

concept and its generalisation in multiple situated scenarios

c. ease in learning current concept by making familiarisation with past learning experiences



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Use of multi-sensory channelsAdequate use the visual, aural and tactile

senses of the learner.Chances of getting distraction due to the

unused channel are high.Reception enhances if the representation

of domain content involves various sensory channels.



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Context based selection of multimedia objects If multiple multimedia objects are

available for same task/concept, presentation should use the most suitable object in the context (e.g. interaction possibility by simulation to beginner, whereas review of concept in textual form by experienced learner

Demand of domain should determine which multimedia object is required for which task and in which context.



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Authenticity of multimedia objectsThe learner should be aware of the

authenticity of the multimedia objects (Laurel et al., 1992) (e.g. appropriate messages to the learner while showing schematic diagrams and animations of the processes that do not show the real objects)



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Navigation in typical educational systems is via links

A link does not say what happens to the screen when the user activates the link (Rada, 1995)

Learner’s expectations of outcome while activating a link should be matched with the presentation of actual resulting interface connected to the link (either static as in traditional systems or adaptive/dynamic as in intelligent systems).



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Type of link should suit to the context and learner’s expectations towards the outcomes.

Links should be used for the tasks for which they suit best and do not put cognitive overload on the learner.

Selection of links should not deviate learner’s attention from the main task of learning.

The existence of link should be as transparent as possible.



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MR approach favours the use of both, interaction objects (e. g. push buttons, radio buttons, check boxes) and interactive objects (e.g. text, pictures) (Bodart & Vanderdonckt, 1994)

Interaction objects provide transition from one part of the system to another on learner’s explicit initiative

Interactive objects facilitate a contextual transfer recommended by the system



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Types of navigational links:

Direct successorleading to the successive domain unit in knowledge hierarchy.

Such transfer should arise from current context such as link in text or message after fulfilling learning criteria of current domain unit.



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Parallel concept linkleading to the analogous domain unit for comparative learningorto the unit related to another aspect of currently being learnt domain content.

These transfers should be explicit (using interaction object).



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Fine grained unit linkleading to the fine details of the domain content once some missing or mis-conceptions are identified

These transfers are very contextual and it is necessary to maintain the context during transfer.

Interactive objects such as image maps should be used.



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Glossary linkleading to a pop-up “spring loaded” module (Nielsen, 1996) in exploration process

Available only till learner is interested in it and explicit action required (such as pressing the mouse button).

These links provide a referential summary of the terms, hence should be initiated from the terms themselves.



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Excursion linkleading to a learning unit outside the current knowledge hierarchy, within current context

A way back to main learning unit should be possible

The context for excursion links should be broad enough to cover the essence of current unit



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Problem linkleading to the problems related to current conceptual unit.

Transfers to problems should result from system’s inference of learning criteria fulfilment of a conceptual unit.



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Navigational object selectionComponents of Multiple Representation

Approach

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No more than one active multimedia object at a time on the screen (except comparative studies)

Integration of multimedia objects should be complimentary and synchronised

Same material should not be repeated using different multimedia objects at the same time

Integration of multimedia objectComponents of Multiple Representation

Approach

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Integration of decision intensive objects is not recommended due to their high cognitive load demand

To avoid confusion, different multimedia objects not initially distinguishable should not be put together

Integration of dynamic and static observation objects should be such that both objects should not use same sensory channel at the same time


Approach

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Approach

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Exploration Space Control

User exploration based adaptation

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Exploration is a self-initiated learning activity.

Learning by exploration is an effective technique for task oriented disciplines such as computer science and medicine.

It provides not only skills of the domain but also the understanding of the embedded concepts.

Exploration

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Hypermedia systems, simulation systems and other similar systems provide learners with exploration environments where they can explore various paths to solve problems.

Learning actually takes place by accessing various information resources such as hypertext, demonstrations, simulations, and so on.

Learning by exploration

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Exploration activity: searching these information resources to comprehend the information and to acquire domain concepts/knowledge.

Comprehension and acquisition involve mutually integrating the information from different resources, and integrating new information into existing knowledge.

Learning by exploration

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Extent of the exploration activity

=

Extent of the information resources (including the domain concepts/knowledge)

+

Exploration operations (such as search, selection, apply, integration etc.)

This is called exploration space.

Exploration space

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Problems in educational explorationLearners should be free to explore to “construct”

their learning (Carroll et al.,1985).However, learners may not know what to and

how to explore.Excessive mental efforts to search and integrate

the information from different information resources may cause cognitive overload.

Exploration space may also be quite wide so they may lose their ways.

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The extent and amount of complexity inherited in existing exploration techniques should depend on the learner’s current level of competence and their current capacity to cope with cognitive load in such explorations.

Need for adaptation in exploration

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Examples in exploration adaptation

Adaptation of navigation the exploration paths that learners should follow

Tailoring the information to be presented to the learners

(making it easier for the learners to search and comprehend domain concepts and knowledge)

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Examples in exploration adaptation

Various parameters are restricted in simulation-based learning systems to make it easier to interpret the results.

Some systems sequence the problems in such ways which focus learners’ attention on specific parts of the domain. This allows an easier understanding of the domain in gradual manner.

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Supporting learning by exploration

The exploration space needs to be limited for the novice learner, and restrictions should gradually be removed as the learner progresses in the learning process.

Exploration Space Control (ESC) is the over arching phenomenon encompassing these adaptive mechanisms.

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Exploration Space Control (ESC)

Information Resource-3



Information Resource-n

Exploration Operations

Exploration SpaceSelect

Trace

Apply

Information Space

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Exploration space control (ESC)

ESC controls the extent of exploration space according to domain complexity and to the learners’ competence, understanding levels, experiences, characteristics, etc.

It is also a technology that integrates current technologies for exploration.

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Exploration space control (ESC)ESC facilitates proper learning environment for

all types of learners.

Several tools are provided through suitable user interface to explore the exploration space.

Exploration tools and information is restricted according to the learners’ attributes.

However, these restrictions would be as less as possible, and would be reduced with learners’ progress of subject matter understanding.

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Purposes of ESCTo facilitate active learning:Suitable for learners with higher learning competence. Achieved by reducing cognitive load as less as possible.

To facilitate step-by-step learning:Suitable for learners with lower learning competence. Achieved by reducing cognitive load as much as possible.

A combination of ‘active learning’ and ‘step-by-step learning’ covers whole

learner spectrum, and therefore ESC is applicable for all kinds of learners.

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ESC Control Levels Embedding information: This facilitates the

creation of information space and involves scaffolding.

Limiting information resources:

Limiting number of information resources

Selecting types of information resources appropriate for looking into current domain material

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ESC Control Levels Limiting exploration paths:

Limiting the number of feasible exploration paths to be looked into

Limiting the exploration paths which are non-feasible or are unrelated to the current domain material

Limiting information to be presented:

Limiting the amount of information.

Adapting the contents of information to each learner

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ESC and Current Technologies

Control Levels

Embedding information

Limiting information resources & exploration paths

Limiting exploration paths

Limiting presented information

Limiting exploration paths & presented information

Current Technologies

Scaffolding

Navigation

Problem ordering (courseware)

Information tailoring

Simulation setting

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Designing systems with ESC

1. Identification of learning goals to be accomplished by the learners

2. Selection of scaffolding methodsa. Selecting various information

resources to accomplish each learning goal (e.g. hypertext, simulation, demonstration)

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2. Selection of scaffolding methodsb. Developing the information

resources (decision about the information to be presented) based on:• Amount of the information• Contents of the information

(such as abstract/concrete, detail, and theory/example)

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2. Selection of scaffolding methodsc. Selecting various exploration

operations to be used in and between each information resource (such as Select, Trace, Apply, Integrate, and Interpret).

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Designing systems with ESC3. Deciding levels of control to be

applied to different information resources:a) Deciding the major purpose of ESC

(active or step-by-step learning)

Helps designers to decide on the ways of how to control exploration space

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b) Deciding control levels

Exploration Operations

Control Levels

Exploration paths

Presented information

Information resources Select, Integrate

Select, Trace, Apply

Interpret, Understand

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Designing systems with ESCc. Deciding application of control levels

according to learner and domain models

Learner model factors: Preferences Knowledge Levels Experiences Competence Exploration Process Cognitive Load (Mental Efforts)Domain model factors: Type of knowledge (know-how, know-why

…) Degree of detail (Granularity) Depth (Deep or Shallow)

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Pedagogical issues in the development of adaptive

educational systems

Contextsleading to

Human Teacher Model

(not just for cognitive skills based systems)

Research in Context

• Knowledge management• Reasoning• Natural language processing

Intelligent systems:

Intelligent tutoring systems:• Knowledge representation• Discourse management• Other architectural aspects to

improve student computer interaction and to provide effective tutoring strategies

Current implementation of context is only within the systems.There are various contexts outside the system that have an

important bearing on the success of an ITS!

Contexts of intelligent tutoring systems

Besides the inter-actional context, the environmental and objectival contexts are

important for any educational system.

Student

EnvironmentalContexts

ITS

InteractionalContexts

Teacher

Teachingprocess

Assessmentprocess

ObjectivalContexts

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Inter-actional Contexts• Employed within the system to

provide an improved human-computer interaction.

• They facilitate more intelligent feedback by the system.

Plan recognitionKnowledge structuringKnowledge representationReasoningDiscourse management

Environmental ContextsThe major constituents of environmental contexts:

Student (capabilities, preferences and motivation)

Teacher (preferences and outlook) Discipline (nature) Knowledge (characteristics) Medium (capabilities of computer hardware

and software) Social and technological environment

Environmental Contexts

Environmental contexts

An innovative use of technology may require uncomfortable scrutiny of educational purpose and teaching method.

- Hammond & Trapp

Discipline

Contexts of adaptive edu. systemsHuman

PsychologyHuman

PsychologyCyber

Psychology

Teaching styles

Model of ITS

Natural Abilities

Learning styles

Motivation

Model of ITS

Inheritedteaching styles and

capabilities forintelligentbehaviour

Teaching styles

Model of ITS

Model of TeachingCollaborators


Traditional teacher-student interaction is a complex phenomenon, affected by personality, background, motivation and host of other factors and the same is also true for the peer-to-peer interaction.

An ITS that intervenes in this rich environment needs to demonstrate intelligent behaviour, not only in its interactions with a student but also in its interactions with a teacher.

ITS in a joint cognitive system


“... don’t try to mimic the way people communicate, just try to design the system so it complements human communicative skills”

- Devlin

The role of the teacher stands out as a partner within the 'joint-educational system'. An ITS should understand this role and help a teacher rather than prematurely attempt to act as a replacement.

The role of a teacher


High workloads, surface level assessment demands and lack of freedom in learning environment force students to use reproductive approaches. Does a teacher’s teaching style also get affected similarly?

If a teacher is forced to adopt a superficial teaching style due to external factors, the situation can be improved by harnessing ITS in a supportive role to free up some of the pressure.

Teaching styles and students


While it is worthwhile within the design laboratories to integrate advanced pedagogical strategies of modelling, coaching, reflection, articulation, scaffolding and fading as well as exploration within an ITS, it is also practically worthwhile for an ITS to support a teacher in adopting these strategies in a limited way but with a degree of ‘intelligence.’

Support for a teacher within an ITS


We suggest that a human teacher model should formally be incorporated in the design of an ITS to recognise the different teaching styles, to record the teaching styles adopted in the design and preferably to enable adaptation to suit the implementing teacher.

Human teacher model


An explicit explanation of the teaching style will not only enable an implementing teacher to understand the designer’s rationale but will also help in dealing with the cognitive dissonance arising from any differences in the teaching styles.

Teaching styles and the discipline


A discipline’s subject matter and the degree to which its practice is regulated contributes to the way it is taught.

Computers currently are good for the disciplines where learning is based on ‘doing’ and ‘observing’ rather than ‘freely conversing’ and ‘arguing’.

Nature of discipline


At the introductory conceptual level, learning benefits more from graphics, animations, audio and video clips as they provide multiple stimulus to improve motivation and facilitate concept acquisition.

Levels of learning a discipline & representations


Procedural learning benefits from direct manipulation of symbols, text and graphical representations for some disciplines.

While others benefit from analysis, exchange of opinions and expert feedback preferably in a synchronous system such as video conferencing and electronic white boards or asynchronously using email or discussion forums.



Theoretical learning benefits from some form of hierarchical representation enabling smooth granular transitions.



Technologies such as hypertext, multimedia, hypermedia and virtual reality offer increasing ease and flexibility in knowledge construction by the learners, however, they are not an unmixed blessing. They are accompanied by an additional cognitive load and a potential for distractions.

Characteristics of the medium


The novice learners should benefit from richer representations as they provide multiple stimulus.

The same learners may most likely get distracted in the absence of directed learning as they may not have developed adequate meta-cognitive skills of setting learning goals, selecting effective learning techniques, monitoring progress towards goals, and adjusting strategies as needed.

Characteristics of the medium

• Instructional methods and not the media cause learning.

• Human brain, product of millions years evolution, is not changing rapidly and can be overloaded by the sensory output that technology is capable of delivering.

- Clark

To prevent such overloading & distraction, amount and richness of contextual information may have to be constrained.

Environmental contextsCharacteristics of the medium

A representation that is efficient from the learning point of view may be inefficient for a performance task and the optimisation of efficiency and expressiveness is often mutually exclusive requiring a trade-off, possibly per domain.

- Dutton & Conroy

System design should be governed by needs of discipline and not by some abstract model or emerging technologies.

Environmental contextsCharacteristics of the medium

“Designing interfaces for culturally diverse users is fundamentally a problem of communicating the intended meaning of representations ... in every culturally determined usability problem a divergence between the target meaning and the interpreted meaning of representations was present.”

- Bourges-Waldegg & Scrivner

Social & cultural effectsEnvironmental

contexts

Their study found, however, that direct intercultural communications between users are less problematic since the users develop jointly a communication space in order to succeed in their task, despite differences in culture and language.

Social & cultural effectsEnvironmental

contexts

“So far in the research literature, little attention has been given to the effect of class or socio-economic differences upon variations in learning style.”

- Anderson

Differences in backgrounds, goals, or outlooks on life can be problematic in communication between two people every bit as much as their not speaking the same language.

Social environmentEnvironmental

contexts

Objectival contextsTeaching and Assessment

Concerned with “teaching-assessment objectives anomaly”

Human teacher attempts to cover whole syllabus, the assessment procedure does not require to perform 100%.

Is the objective of educational system to encourage acquisition of the facts and rules or is it to encourage acquisition of meta-learning abilities?

Contexts of an ITS

The success of an ITS depends on adequate consideration of the various contexts encompassing its design and implementation. While there is an increasing recognition of context in the 'Intelligent' aspect of an ITS, there is a need for recognition that context affects the 'Tutoring' and 'System' aspects as well.

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Modelling human teacher

Human teachers may have:

• different personalities

• different teaching styles (born out of their traditional, progressive or vocational outlook and their own learning style)It is not possible to envisage all the

preferences of implementer teacher at design time.

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Human Teacher ModelWe recommend a re-configurable human teacher model to be incorporated in the design of ITS:

• to recognise the different teaching styles

• to put on record the teaching style(s) adopted in the design, and

• enable manual or automatic adaptation to suit the implementing teacher

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Why explicit record of designer’s teaching style(s)

• better understanding of designer’s rationale by implementing teacher

• help in dealing with the cognitive dissonance arising from any differences in teaching styles

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Why explicit record of designer’s teaching style(s)

• clear rationale behind adopted teaching strategy may also help in the student learning in less adaptive systems

• easier understanding of representations which are difficult due to cultural differences

• if designer’s teaching style is unproductive in a culture, the system may be localised

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Summary: Incremental growth• ITS, in their current stage, cannot

replace all the functions of human teacher.

• Efforts should be on increasing productivity (just like initial word processors for steno-typists).

• ITS designers should treat human teacher as their target user.

• Human Teacher Model is next logical approach in that direction.

Designing cognitive skills based adaptive educational

systems

Multiple representation approach

Exploration space control

MRA in InterSim system

MRA in InterSim systemMultimedia object selection

• Learning process starts with receptive and active observation using:

Text and Normal static pictures for receptive observation

Sensitive pictures for active learning in current domain hierarchy

Semi-sensitive pictures for active learning outside of current domain hierarchy.


• Advanced observation takes place using three types of animations:

Automatic user controlled user initiated.

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• Following observation phase, simulations are used for acquiring skills.

• More realistic learning environment is provided by pictorial virtual reality.

• Even more realistic cases are provided by video.

• Decision making skills are provided by flowcharts.


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Navigation object selection


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Integration of multimedia objects

Examples:•The concept of "appropriate sound energy routing " is presented by two comparative animations.•Structure of ossicles is presented as alternative static picture and pictorial VR to prevent confusion due to similar initial visual states.


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MRA and student modelling

• To act as filter on student model

Content recommended by student model to present to the learner

MRA ValidatorContent

manipulator

validation not ok

MRA Renderer

Interface presentation to student

validated

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MRA and student modelling

Functionality achieved:

• XML Schema for Multiple Representation Approach implementation.

• Stylesheets based on XML Schema to transform XML notation into web pages.

• Formalisation of limited multimedia objects and link types.

Products: 1. Manual validator2. Limited renderer

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MRA and student modellingNext steps

• Customisation interfaces for rules modifications and objects addition/deletion

• Automatic verification compiler stylesheets for dynamic domain content presentation based on student model state

• Content manipulator to resolve validation problems

• Fully functional renderer

Prototype demo

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Designing cognitive skills based adaptive educational

systems

Exploration space control

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ESC in InterSim

1. Determination of learning goals:

Goal a) Understanding the structure and functionality of human ear

Goal b) Acquiring appropriate skills in diagnosing and treating ear diseases.

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ESC in InterSim2. Scaffolding

a) Selection and development of various information resources:Hypertext: description of structure and

functionality of ear (goal a)Demonstration: behaviour of normal ear

(goal a)Simulation: experiment on functionality of

ear (goal a), exploration for diagnosis and treatment of diseases (goal b)Problem ordering: problem sequencing

based on learner competence (goal a), enabling learners to solve (diagnose and treat) problems in order (goal b)

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2. Scaffoldingb) Deciding control levels to restrict exploration operationsHypertext: Select & Trace (for goal a)Demonstration: Interpret (for goal a)Simulation: Apply & Interpret (for goals

a & b)Problem ordering: Trace (for goals a &

b)Design: Select, Apply & Interpret (for

goal b)

ESC in InterSim

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3. Deciding control levelsa) Major purpose of ESC in InterSim system (to provide active learning)b) Selection of control levels- Limiting information resources (for goals

a & b)- Limiting exploration paths (for goals a &

b)- Limiting presented information (for goal

a)- Embedding information (for goal b)c) Deciding how to control various information resources

ESC in InterSim

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Example of Control Levels in InterSim

Concept of main path and excursions

Example:Main learning path: Structure of middle earExcursion: Physics of soundLimitations: Only sound travel through mechanical linkage of ossicles is presented. Information regarding sound transfer from air to water (related to inner ear) and other similar information is not presented to maintain the context.

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- Restriction methods: Restricting the representation of domain material in terms of complexity of representation (for example, static pictures vs virtual reality scenarios)

- Deciding parameters: Exploration experience, Cognitive load


Limiting information resources for understanding the structure and

functionality of ear

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- Restriction methods: Restricting buttons, combo box choices, anchors/ links to be used in explorating hypermedia to limit Select & Trace operations.

- Deciding parameters: Exploration competence, Knowledge level


Limiting exploration paths for understanding the structure and

functionality of ear

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- Restriction methods: Providing scaffolding so as to decrease domain complexity with regard to learner models (for example, first allowing the learner to semi-explore the disease development process in an animation wizard; then adding simulation capabilities to allow the full exploration; then adding extra simulation capabilities for diagnosis)

- Deciding parameters: Exploration competence


Embedding information for acquiring skills diagnosing and treating diseases

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Future of Exploration Space Control

The Implementation of Exploration Space Control has provided various adaptive features towards the learner and thus validated the applicability of the methodology for systems providing learning-by-exploration.

Further research demands implementation of ESC in other system providing learning by exploration to assess the generalisation of the methodology.

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Exploration Space Control ResearchNext steps

Development of framework to enhance student model based on “Exploration Space Control” methodology

ESC Validator ESC Content Manipulator ESC Renderer

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Synopsis

Cognitive skills based adaptive educational systems may prove very effective in rapidly increasing life-long and just-in-time learning process if:

• they are based on appropriate educational framework

• contexts surrounding their design process are considered appropriately

• adaptation is provided both at content and user level

1 Adaptive Educational Environments for Cognitive Skills Acquisition Ashok Patel Director, CAL...

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