TRADUCIR Objeto de Aprendizaje Gibbons

8/18/2019 TRADUCIR Objeto de Aprendizaje Gibbons

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The Nature and Origin

of Instructional Objects1

Andrew S. Gibbons

Jon Nelson

Utah State University

Robert Richards

Idaho National Engineering and Environmental Laboratory

Introduction

This chapter examines the nature and origin of a construct we term the instructional

object . Rather than being a single definable object, it is a complex and multifaceted

emerging technological construct!one piece of a larger technological pu""le. The

general outlines of the pu""le piece are ta#ing shape concurrentl$ in the se%eral

disciplines from which the practices of instructional technolog$ are deri%ed!computer

science, information technolog$, intelligent tutoring s$stems, and instructional

ps$cholog$. The terminolog$ used to describe this new idea reflects its multiple origins,

its di%erse moti%ations, and its newness. &n the literature what we will refer to as the

' This chapter describes research on the instructional design process carried out under the auspices of the

(umanS$stem Simulations )enter at the &daho National *n%ironmental and *ngineering +aborator$

-epartment of *nerg$.


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speciali"ed producti%it$ tools. 8$ doing so, we are hoping to lin# the practice of

instructional designers with new design constructs implied b$ current %iews of instruction

that are shifting toward studentcentered, situated, problembased, and modelcentered

experiences!ones that are also shaped b$ the demands of scaling and production

efficienc$.

6e belie%e that this discussion is timel$. *%en as the instructional use of the 6orld 6ide

6eb is being promoted with increasing urgenc$, there are serious 9uestions concerning

whether it is full$ pro%ided with design concepts, architectures, and tools that fit it forser%ice as a channel for instructing rather than merel$ informing :airweather 2

Gibbons, 7;;;. At the same time, instructional design theorists are 9uestioning the

assumptions underl$ing existing design methodologies that are pro%ing brittle in the face

of challenges posed b$ the newer instructional modes Gordon 2 = Rowland, '33?. The

instructional object has been proposed within different specialt$ fields for its producti%it$

benefits, for its standardi"ation benefits, and as a means of ma#ing design accessible to a

growing arm$ of untrained de%elopers. As the design process e%ol%es a theoretic base, we

feel it important to as# how that theor$ base can be related to instructional objects.

Standards and CBI Technology

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The industr$ that focuses on the design, de%elopment, and deli%er$ of computeri"ed

instruction is currentl$ undergoing a period of standard setting focused on the distribution

of instructional experiences o%er the &nternet and 6orld 6ide 6eb. The instructional

object!indexed b$ metadata!has great potential as a common building bloc# for a

di%erse range of technolog$based instructional products. 5assi%e efforts in%ol%ing

hundreds of practitioners, suppliers, and consumers are contributing to object standards

that will allow this building bloc# to become the basic unit of commerce in instruction

and performance support (ill, '334.

&t is hard to resist comparing these e%ents with e%ents in the histor$ of the steelma#ing

technolog$. 6hen :rederic# Ta$lor showed in the opening $ears of the 7;th centur$ that

reliable recipes for steel could be placed into the hands of relati%el$ untrained furnace

operators 5isa, '33@, an arm$ of new and lesstrained but full$ competent furnace

operators began to ta#e o%er the mills. Greater 9uantities of steel industrial scale could

be produced at more precisel$ controlled le%els of 9ualit$. Three #e$ e%ents in the

expansion of steel ma#ing in%ol%ed epochs of standard setting carried out b$ three

different standards coalitions. 1%er se%eral decades, these coalitions arbitrated the

measures of product 9ualit$ for rail steel, structural steel, and automoti%e steel

respecti%el$. 6ith each new standard, the industr$ progressed and expanded. This in turn

led to e%en more rapid expansion and di%ersification of the use of steel in other products.

Steel standards pa%ed the wa$ for ' the achie%ement of more precise and predictable

control o%er steel manufacturing processes, 7 a standardbased product that could be

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tailored to the needs of the user, and ? the abilit$ to scale production to industrial

proportions using the new processes 5isa, '33@. 6ithout these de%elopments, steel

9ualit$ would still be highl$ %ariable, steel products would ha%e a much narrower range,

and steel ma#ing would still be essentiall$ an idios$ncratic craft practiced b$ highl$

trained and apprenticed furnace operators.

The Nature of Instructional Objects

6e define instructional objects in a later section of this chapter b$ relating them to anarchitecture for modelcentered instructional products. As we use the term in this chapter,

instructional objects refer to an$ element of that architecture that can be independentl$

drawn into a momentar$ assembl$ in order to create an instructional e%ent. &nstructional

objects can include problem en%ironments, interacti%e models, instructional problems or

problem sets, instructional function modules, modular routines for instructional

augmentation coaching, feedbac#, etc., instructional message elements, modular

routines for representation of information, or logic modules related to instructional

purposes management, recording, selecting, etc..

The literature in a number of disciplines that contribute to instructional technolog$

describes objects that perform some subset of the functions re9uired of the different #inds

of instructional object

• 1bjects in%ol%ed in database structuring

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• 1bjects for the storage of expert s$stem #nowledge

• 1bjects for document format control

• 1bjects used for de%elopment process control

• 5odular, portable expert tutors

• 1bjects representing computer logic modules for use b$ nonprogrammers

• 1bjects for machine disco%er$ of #nowledge

• 1bjects for instructional design

•

1bjects containing informational or message content

• 1bjects for #nowledge capture

• 1bjects that support decision ma#ing

• 1bjects for data management

All of these t$pes of object and more are needed to implement instruction through the

realtime assembl$ of objects. Gerard '3B3 in a surprisingl$ %isionar$ statement earl$

in the histor$ of computerbased instruction describes how /curricular units can be made

smaller and combined, li#e standardi"ed 5eccano Cmechanical building setD parts, into a

great %ariet$ of particular programs custommade for each learner0 p. 73?;. Thirt$

$ears later, the %alue and practicalit$ of this idea is becoming apparent.

Basic Issues

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To set the stage for the discussion of instructional object origins, it is essential to touch

briefl$ on two issues related generall$ to the design and de%elopment of technolog$

based instruction

• The goals of computeri"ed instruction adapti%it$, generati%it$, and scalabilit$

• The structure of the technological design space

The Goals of Computeried Instruction! Adapti"ity# Generati"ity# and Scalability

:rom the earliest da$s of computerbased instruction as a technolog$, the goal has clearl$

been creating instruction that was ' adaptive to the indi%idual, 7 generative rather

than precomposed, and ? scalable to industrial production le%els without proportional

increases in cost.

Nowhere are these ideals more clearl$ stated than in Computer-Assisted Instruction: A

oo! o" #eadings '3B3a, a groundbrea#ing and in man$ wa$s still current %olume

edited b$ At#inson and 6ilson. Eirtuall$ all of the chapters selected for the boo# build on

the three themes adapti%it$, generati%it$, and scalabilit$.

Adaptivity: At#inson and 6ilson credit the rapid rate of growth before '3B3 in )A& in

part /to the rich and intriguing potential of computerassisted instruction for answering

toda$Fs most pressing need in education!the indi%iduali"ation of instruction0 At#inson


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2 6ilson, '3B3b, p. ?. The$ distinguish )A& that is adapti%e from that which is not,

attributing the difference to /response sensiti%e strateg$.0 Suppes '3B3 foresees /a #ind

of indi%iduali"ed instruction once possible onl$ for a few members of the aristocrac$0

that can /be made a%ailable to all students at all le%els of abilities0 p. >'. This durable

argument is being used currentl$ to promote instructional object standards Gra%es,

'33>.

Suppes '3B3 describes how computers will /free students from the drudger$ of doing

exactl$ similar tas#s unadjusted and untailored to their indi%idual needs.0 p. >.Stolurow '3B3, describing models of teaching, explains

Hmust be c$bernetic, or responsesensiti%e, if it is adapti%e. A model for

adapti%e, or personali"ed, instruction specifies a set of responsedependent rules

to be used b$ a teacher, or a teaching s$stem, in ma#ing decisions about the nature

of the subse9uent e%ents to be used in teaching a student. p. B3;

(e introduces an /ideographic0 instructional model that designs for /possibilities0 rather

than plans for specific paths /we need wa$s to describe the alternati%es and we need to

identif$ useful %ariables0 p. 4. Stolurow ma#es the important distinction /between

branching and contingenc$ or responseproduced organi"ation Cof instructionD0 p. 3.

These and man$ other things that could be cited from the At#inson and 6ilson %olume

ma#e it clear that adapti%it$ was a closel$held earl$ goal of computerbased instruction.

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&ncidentall$, these and other statements in the boo# ma#e it clear that )A& was not

en%isioned b$ these pioneers as simpl$ computeri"ed programmed instruction.

$enerativity: Generati%it$ refers to the abilit$ of computeri"ed instruction to create

instructional messages and interactions b$ combining primiti%e message and interaction

elements rather than b$ storing precomposed messages and interaction logics. The

contributors to At#inson and 6ilson describe mainl$ precomposed instructional forms

because in the earl$ da$s of )A& there were no tools to support generati%it$, but man$

At#inson and 6ilson paper authors emphasi"e future tooling for generati%it$.

Suppes '3B3, who later produced math problem generation tools himself, describes

three le%els of interaction between students and instructional programs, all of them

subject to some degree of generati%it$ ' indi%iduali"ed drillandpractice, 7 tutorial

s$stems that /approximate the interaction a patient tutor would ha%e with an indi%idual

student,0 and ? dialogue s$stems /permitting the student to conduct a genuine dialogue

with the computer0 p. >7>>.

Silberman '3B3 describes the use of the computer to generate practice exercises p. @?.

Stolurow, describing the instructional rules of an adapti%e s$stem said

These rules Cfor controlling presentation of information, posing of a

problem, acceptance of a response, judging the response, and gi%ing feedbac#D

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also can be called organi"ing rules= the$ are the rules of an instructional grammar.

*%entuall$ we should de%elop generati%e grammars for instruction. p. B

Scalability: The authors of the At#inson and 6ilson %olume were sensiti%e to the then

highl$ %isible costs of computerassisted instruction. Their solutions to scalabilit$ were

projections of lower computer costs, expectations for larger multiterminal s$stems, and

calculations of product cost spread o%er large numbers of users. The connecti%e and

distributi%e technolog$ of the da$ was the timeshared monolithic centrali"ed mainframe

s$stem and then highcost and low9ualit$ telephone lines.

The goals of adaptivity, generativity, and scalability that pre%ailed in '3B3 are still #e$

targets. These goals were adopted b$ researchers in intelligent tutoring s$stems, and the$

are clearl$ e%ident in the writings of that group of researchers, especiall$ in the

occasional summaries of the field and its e%ol%ing theor$ and method 6enger, '34=

Isot#a, 5asse$, 2 5utter, '344= Ioulson 2 Richardson, '344= 8urns, Iarlett, 2

Redfield, '33'= Noor, '333.

8urns and Iarlett '33' tell us to, /5a#e no mista#e. &TSs are tr$ing to achie%e oneon

one instruction, and therein lies the complexit$ and the necessar$ flexibilit$ of an$

potentiall$ honest &TS design.0

Toda$ the tutorial s$stems and dialogue s$stems described b$ Suppes still represent

cutting edge goals for intelligent tutoring s$stems. Generati%it$ is still clearl$ a part of

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the basic game plan. This is e%ident in the goals of the -epartment of -efense Ad%anced

-istributed +earning S$stem &nitiati%e Ad%anced -istributed +earning &nitiati%e, no

date. As 8urns and Iarlett '33' explain,

&TS designers ha%e set up their own hol$ grail. The grail is, as $ou might

ha%e guessed, the capabilit$ for a largescale, multiuser #nowledge base to

generate coherent definitions and explanations. &t goes without sa$ing that if a

student has a reasonable 9uestion, then an &TS should ha%e an answer. p. B

The personal computer, the networ#, and rapidl$ proliferating communications

connecti%it$ ha%e become the standard. 8ecause of this, our focus on scalabilit$ has

shifted from deli%er$ costs to de%elopment costs. 1ne of the forces behind the

instructional objects phenomenon is the prospect of lowering product costs through a

number of mechanisms reusabilit$, standardi"ed connecti%it$, modularit$ to optimi"e

transmission from central stores, and standardi"ed manufacture.

The Structure of the Technological $esign Space! The Con"ergence %one

Technologies often de%elop first as ad hoc s$stems of practice that later must be

grounded in technological theor$ and form a mutuall$ contributor$ exchange with

scientific theor$. &nstructional technolog$ is see#ing its theoretical foundations more

%igorousl$ now than e%er before 5errill, '33>= Reigeluth, '333= (annafin, et al., '33.

6e belie%e that se%eral clues to de%eloping a more robust theoretical basis for

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instructional technolog$ can come from stud$ing technolog$ as a t$pe of #nowledge

see#ing acti%it$ and from stud$ing the technological process.

Technolog$ consists of the human wor# accomplished within a con%ergence "one

where conceptual artifacts designed structures, construct architectures are gi%en specific

form with materials, information, and forceinformation transfer mechanisms. &n this

con%ergence "one, conceptual artifacts are lin#ed with material or e%ent artifacts that

express a specific intention. &n a discussion of the 6orld 6ide 6eb and 5odel)entered

&nstruction, Gibbons and his associates Gibbons, et al., in press describe thiscon%ergence "one in terms of conceptual instructional constructs being reali"ed using the

programming constructs of a particular software tool.

This is the place where the designerFs abstract instructional constructs and

the concrete logic constructs supplied b$ the de%elopment tool come together to

produce an actual product. At this point, the abstract e%ent constructs are gi%en

expression!if possible!b$ the constructs supplied b$ the de%elopment tool.

8urns and Iarlett '33' pro%ide a glimpse of this boundar$ world

Iroposed architectures for representing teaching #nowledge in &TSs can be

described in terms of how #nowledge is understood b$ experts and how it can be

represented b$ programmers in sets of domainindependent tutoring strategies. p.

@B

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(erbert Simon, in Sciences o" the Arti"icial , describes this con%ergence "one between the

abstract world and the concrete world as a #e$ to understanding technological acti%it$ in

general

& ha%e shown that a science of artificial phenomena is alwa$s in imminent

danger of dissol%ing and %anishing. The peculiar properties of the artifact lie on

the thin interface between the natural laws within and the natural laws without.

6hat can we sa$ about itK 6hat is there to stud$ besides the boundar$ sciences! those that go%ern the means and the tas# en%ironmentK

The artificial world is centered precisel$ on this interface between the

outer and inner en%ironments= it is concerned with attaining goals b$ adapting the

former to the latter. The proper stud$ of those who are concerned with the

artificial is the wa$ in which that adaptation of means to en%ironments is brought

about!and central to that is the process of design itself. The professional schools

will reassume their professional responsibilities just to the degree that the$ can

disco%er a science of design, a bod$ of intellectuall$ tough, anal$tic, partl$

formali"able, partl$ empirical, teachable doctrine about the design process. p.

'?'7

Simon emphasi"es the fragilit$ of the connections across the interface between

conceptual and real the interface is difficult to imagine in the abstract, and it is not

surprising that man$ designers!especiall$ no%ice ones!focus their attention mainl$ on

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the material result of designing rather than on its conceptual precursors. &n fact, as we

explain in a later section of this chapter, the focus of designers on a particular set of

design constructs allows classification of designers into a number of broad classes.

$imensions of the $esign Space

Technologists who succeed in %isuali"ing this conceptualmaterial boundar$ can be

baffled b$ its complexit$. -esigns are ne%er the simple, unitar$ conceptions that we

describe in textboo# terms. &nstead, the$ are multila$ered constructions of mechanismand functionalit$ whose interconnections re9uire se%eral transformational lin#s to reach

across the conceptualmaterial boundar$. +in#s and la$ers both must articulate in designs

such that interference between la$ers is minimi"ed and the future adaptabilit$ of the

artifact to changing conditions is maximi"ed!the factor that gi%es the artifact

sur%i%abilit$. Automated design s$stems pro%ide principled guidance for those decisions

that cannot be automated and default %alues for those that can.

8rand '33> describes the principle of la$ering in designs b$ describing the la$ered

design of building!in what he calls the /BS0 se9uence

• S&T* L This is the geographical setting, the urban location, and the legall$

defined lot, whose boundaries and context outlast generations of ephemeral

buildings. /Site is eternal, / -uff$ agrees.

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• STRM)TMR* L The foundation and loadbearing elements are perilous and

expensi%e to change, so people donFt. These are the building. Structural life

ranges from ?; to ?;; $ears but few buildings ma#e it past B;, for other

reasons.

• S&N L *xterior surfaces now change e%er$ 7; $ears or so, to #eep with

fashion and technolog$, or for wholesale repair. Recent focus on energ$ costs

has led to reengineered S#ins that are airtight and better insulated.

• S*RE&)*S L These are the wor#ing guts of a building communications

wiring, electrical wiring, plumbing, sprin#ler s$stem, (EA) heating,

%entilating, air conditioning, and mo%ing parts li#e ele%ators and escalators.

The$ wear out or obsolesce e%er$ to '@ $ears. 5an$ buildings are

demolished earl$ if their outdated s$stems are too deepl$ embedded to replace

easil$.

• SIA)* I+AN L The interior la$out!where walls, ceilings, floors, and doors

go. Turbulent commercial space can change e%er$ ? $ears or so= exceptionall$

9uiet homes might wait ?; $ears.

• STM:: L )hairs, des#s, phones, pictures, #itchen appliances, lamps, hair

brushes= all the things that twitch around dail$ to monthl$. :urniture is called

mobilia in &talian for good reason. p. '?

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7 The progressi%e se9uence of integrations or constructtoconstruct lin#s the

hori"ontal dimension of the figure through which the original conception of a

design emerges into an actual artifact

? The interconnections angled lines between the la$ers of a design show that

each la$er can be articulated with e%er$ other la$er.

:igure '. 5ultistaging and multila$ering of an instructional design space.

As a design progresses from the conceptual stage to the real artifact stage, the

integration of the la$ers increases to the point where abstract design and concrete product

la$ers can barel$ be distinguished. Thus the structure and ser%ice la$ers of a building

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disappear behind co%ering walls and exterior s#in= thus the model and medialogic la$ers

of an instructional artifact disappear behind the strateg$ and surface representation la$ers.

Since the tangible surface la$ers of a design are what we experience, it is not surprising

that new designers fail to see the multiple la$ers of structure that are actuall$ designed.

This is t$pical with building designs, and it is especiall$ t$pical with instructional

designs.

&nstructional designers can be classified generall$ in terms of the constructs the$ en%ision

within a design!the constructs therefore that the$ are most liable to use to create thecentral structures of their designs

• %ediacentric designers tend to concentrate on mediarelated constructs and

their arrangement e.g., manuals, pages, cuts, transitions, s$nchroni"ations,

etc.

• %essagecentric designers tend to constructs related to /telling0 the

instructional message in a wa$ that supports its rapid upta#e and integration

with prior #nowledge e.g., analog$, ad%ance organi"er, use of conceptual

figures, dramati"ation, etc.

• Strategycentric designers prefer to place structures and se9uences of strategic

elements at the center of their designs e.g., message componenti"ation,

interaction patterns, interaction t$pes, etc.

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• %odel centric designers tend to build their designs around central, interacti%e

models of en%ironments, causeeffect s$stems, and performance expertise and

supplement them with focusing problems and instructional augmentations

-esigners tend to mo%e through these /centrisms0 as personal experience accumulates

and the %alue of new, less %isible, subtler constructs becomes apparent to them. 6ith each

mo%e to a new %iewpoint the designer gains the use of the new design constructs without

gi%ing up the old ones, so this change results in the accumulation of fundamental design

building bloc#s.

6hen instructional objects are used in design, the$ are constructs within SimonFs design

space. The$ can theoreticall$ be media, message, strateg$, or model objects or an$

combination of these interacting across se%eral la$ers. The$ can represent a functional

instructional product ha%ing a man$la$ered design or a single element that can be

integrated at the time of instruction into products to suppl$ some modular functionalit$ in

a cooperati%e wa$.

The Origin of Instructional Objects

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Irior to the notion of instructional objects, descriptions of the instructional design process

ha%e been couched in the terminolog$ of other #inds of constructs considered to be

produced at some point during design.

:igure 7 depicts the traditional &S- process in relation to SimonFs technolog$ interface.

-esign is t$picall$ seen as deri%ing from each other, in succession, structural elements

that permit re9uirements tracing of design elements bac# to a foundation of anal$sis

elements. &n :igure 7 this chain of anal$sis and design constructs begins with tas#s

obtained through tas# anal$sis that are used as a base for deri%ing objecti%es, which are in

turn used as a base for deri%ing wor# models including instructional e%ents, see

Gibbons, 8underson, 1lsen, 2 Robertson, '33@.

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:igure 7. Generation of instructional design constructs within the abstract side of the

design space, showing the preconditioning of constructs b$ instructional assumptions.

The /Ts0 on the diagram indicate ruleguided transformations using the base

construct to obtain a resultant construct. +in#s not mar#ed with a /T0 consist of attaching

9ualities or properties to an alread$existing construct. The diagram could be more

detailed, but in its present form it illustrates how a progression of anal$sis constructs

tas#s, objecti%es e%entuall$ lin#s forward to design constructs wor# models,

instructional e%ents which constitute the design. At this point designers bridge acrossSimonFs gap b$ lin#ing the constructs that ma#e up the design with media and tool

constructs logic structures, media structures, representations, concrete objects.

)onceptions of the design process are idios$ncratic to designers. -ifferent designers lin#

different constructs through different deri%ational chains. The goal of :igure 7 is to show

how a t$pical designer %iew can be related to se%eral generations of abstract constructs on

one side of SimonFs gap that lin# from the abstract realm into a concrete realm whose

constructs are traceable. A different %ersion of the design process would produce a

diagram similar to :igure 7 that lin#ed different elements. All of :igure 7 fits within the

leftmost third of :igure ', so all of the structures shown in :igure 7 are abstract.

-e%elopment steps that build the bridge to tool and media constructs ma$ gi%e rise to

directl$ corresponding media and tool objects through a process called /alignment0 see

-uffin 2 Gibbons, in preparation.

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Regardless of the specific constructs used b$ a designer, their mapping across SimonFs

technolog$ gap can be accomplished in the same manner. Thus, SimonFs description of

this interface describes an underl$ing design space, and this allows design methodologies

to be compared!on the basis of the constructual elements used in lin#ages on both sides

of the gap. &nstructional objects ha%e constructual existence on both sides the$ represent

a particular alignment of abstract design, abstract media, and concrete tool constructs.

The Influence of Instructional &ie's on $esign Constructs

:igure 7 also depicts how the designerFs preconceptions regarding instructional methods

precondition the choice of anal$sis and design constructs deri%ed on the left side of the

gap. A designer subscribing to beha%ioral principles will deri%e anal$sis elements

consisting of operant chains and indi%idual operant units. These will lin# forward to

produce a traceable lineage of compatible deri%ed elements. 1ne inclined toward

structured strategic approaches to instruction will deri%e elements that correspond to the

taxonom$ underl$ing the particular strategic %iewpoint. A Gagne ad%ocate will produce

tas#s and objecti%es that correspond with GagneFs learning t$pes= a 8loom ad%ocate will

produce anal$sis units that correspond with 8loomFs. 5errillFs transaction t$pes 5errill,

et al., '33B ser%e a similar function. 5an$ designers or design teams, rather than

adhering to the constructs of a particular theorist, construct their own categori"ation

schemes. 1ften these are conditioned b$ the subject matter being instructed and consist of

blends of both theoretic and practicall$moti%ated classes of constructs. These pre

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condition the anal$sis constructs deri%ed and subse9uentl$ the chain of constructs that

result.

The designerFs instructional assumptions thus exercise a subtle but real influence in wa$s

not alwa$s full$ recogni"ed b$ e%er$da$ designers. The strategic %iewpoint acts as a

-NAli#e pattern, and if it is applied consistentl$ throughout anal$sis and design can

afford the designer product consistenc$ and de%elopment efficienc$. &f the designer is not

influenced b$ a particular strategic %iewpoint, the anal$sis and design constructs lin#ed

b$ deri%ation can consist of messagedeli%er$ constructs or media deli%er$ constructs. &nthis wa$ :igure 7 can also be related to the four /centrisms0 described earlier.

The process of mapping of constructs first within the abstract side of the technological

design space and then across the gap to the concrete side is robust to an enormous %ariet$

of personall$held instructional design models. &t is possible to identif$, e%en in the wor#

of designers who den$ ha%ing a consistent single approach to design, a pattern of

constructs and deri%ati%e relationships that bridge the abstractionconcretion gap. 6e

propose that this t$pe of designer is most common because most designers encounter a

broad range of design problem t$pes, and construct output from anal$sis can differ from

project to project. &t follows logicall$ that this would in%ol%e at least some %ariation in

the form and deri%ation lin#s for those output constructs as well.

&n the face of calls for design models adapted specificall$ to the needs of educators or

industrial designers, the %iew of design we are outlining pro%ides a %ehicle for

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understanding differences. This applies as well to the notion of tailored design processes,

partial or local design processes, and process descriptions adapted to the needs of a

particular project. &t is also possible to see how in iterati%e designde%elopment processes

one of the things that can e%ol%e throughout the project is the nature of the design and

anal$sis constructs themsel%es.

Implications for Instructional Objects

The constructs used in a design space and their deri%ati%e relationships are the #e$ tounderstanding the origins and structures of an$ design. The instructional object enters this

design space as a potentiall$ powerful construct that must find its place within a fabric of

deri%ati%e relationships with other constructs. The problem of instructional objects, then,

as well as being one of defining the object construct and its internals, in%ol%es placing the

instructional object within the context of the design process.

:or this reason we are interested in predesign anal$sis. :or the remainder of this chapter,

we will outline a modelcentered anal$sis process in terms of its creation of constructs

within the design space. 6e will also show how the anal$sis product lin#s within the

design space and e%entuall$ to media and tool constructs. Irior to a discussion of anal$sis

and design constructs, it is necessar$ to describe the strategic %iewpoint of model

centered instruction that preconditions the selection and relation of anal$sis constructs in

this chapter.

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(odel)Centered Instruction

5odelcentered instruction Gibbons, '334= in press is a design theor$ based on the

following principles

• Experience: +earners should be gi%en opportunit$ to interact withmodels of three t$pes en%ironment, causeeffect s$stem, and expert performance.

• Problem solving: &nteraction with models should be focused throughcarefull$ selected problems, expressed in terms of the model, withsolutions being performed b$ the learner, b$ a peer, or b$ an expert.

• Denaturing: 5odels are denatured b$ the medium used to expressthem. -esigners must select the le%el of denaturing that matches thelearnerFs existing #nowledge le%el.

• Sequence: Iroblems should be arranged in a carefull$ constructedse9uence.

• Goal orientation: Iroblems should be appropriate for the attainmentof specific instructional goals.

• Resourcing: The learner should be gi%en problemsol%ing informationresources, materials, and tools within a solution en%ironment.

• Instructional augmentation: The learner should be gi%en supportduring problem sol%ing in the form of d$namic, speciali"ed, designedinstructional features.

The theor$ is described in more detail in se%eral sources Gibbons, '334= Gibbons 2

:airweather, '334, in press= Gibbons, :airweather, Anderson, 2 5errill, '33= Gibbons,

+awless, Anderson, 2 -uffin, 7;;;.

A current general trend toward modelcentered designs is t$pified b$ 5ontague '344

The primar$ idea is that the instructional en%ironment must represent to

the learner the context of the en%ironment in which what is learned will be or

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could be used. nowledge learned will then be appropriate for use and students

learn to thin# and act in appropriate wa$s. Transfer should be direct and strong.

The design of the learning en%ironments thus ma$ include cle%er

combinations of %arious means for representing tas#s and information to students,

for eliciting appropriate thought and planning to carr$ out actions, for assessing

errors in thought and planning and correcting them. & ta#e the %iew that the tas# of

the designer of instruction is to pro%ide the student with the necessar$ tools and

conditions for learning. That is to sa$, the student needs to learn the appropriate

language and concepts to use to understand situations in which what is learned isused and how to operate in them. She or he needs to #now a multitude of proper

facts and when and how to use them. Then, the student needs to learn how to put

the information, facts, situations, and performances#ill together in appropriate

contexts. This performance or useorientation is meant to contrast with formal,

topicoriented teaching that focuses on formal, general #nowledge and s#ills

abstracted from their uses and taught as isolated topics. Ierformance or use

orientation in teaching embeds the #nowledge and s#ills to be learned in

functional context of their use. This is not a tri%ial distinction. &t has serious

implications for the #ind of learning that ta#es place, and how to ma#e it happen.

p. '7@B

&n the modelcentric %iew of instruction, the /model0 and the /instructional problem0 are

assumed as central constructs of design. These modelcentered constructs can be lin#ed

directl$ to media and tool constructs. The$ are identified through a method of predesign

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anal$sis that we call the 5odel)entered Anal$sis Irocess 5)AI that captures both

anal$sis and design constructs at the same time, lin#ing them in a closel$ aligned

relationship. The modelcentered anal$sis generates an output lin#able directl$ to

instructional objects. The 5)AI was defined on the basis of a thorough re%iew of the

predesign anal$sis literature b$ Gibbons, Nelson, and Richards 7;;;a, 7;;;b.

The anal$sis method is intended to be generall$ useful b$ all instructional creators

instructors, designers regardless of the specific instructional medium used. 6e ha%e

deliberatel$ structured the anal$sis process so that the anal$sis method applies to the fullrange of instructional applications. This includes classroom instructors teaching

indi%idual lessons, multimedia designers creating shortcourse products, and intelligent

tutoring s$stem designers, particularl$ those situating their training in realistic

performance settings using problems as a structuring principle.

Theory# Artifacts# and *re)$esign Analysis

The prescripti%e nature of technological theor$ re9uires that a designer #now the desired

goal state and in%ites the designer to emplo$ consistent structuring techni9ues as a means

of reaching it. 1ur re%iew of predesign anal$sis literature compared examples of existing

anal$sis methods in terms of ' input constructs, 7 transformation rules, and ? output

constructs.

:igure ? shows the anal$sis process deri%ing from a bod$ of expertise an artifact

representing some e%ent or content structure& This artifact bears the structural imprint of

the expertise and acts as a #ind of information store. &t in turn can transmit its structure

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and information to other design artifacts. &n the same wa$ a chain of chemical

intermediaries during cell metabolism stores and transfers information or energ$ for later

use in forms that cannot be directl$ metaboli"ed themsel%es.

:igure ?. A technological theor$ of anal$sis.

At some point in this forward motion of tramsmittal, the structure is impressed on an

instructional artifact to create a form that can be /metaboli"ed0. 6e show this

transformation in :igure ? as design process trans"ormations' and we ha%e labeled the

resulting artifact as the arti"act o" intervention& 1ne assumption of :igure ? is that

inter%ention can ta#e place at an intervention point that has been measured as an

appropriate, perhaps optimal, point for the application of that artifactual inter%ention.

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6e describe a methodolog$ that produces artifacts containing problem e%ent structures

that can be transformed into a %ariet$ of artifacts capable of expression in a %ariet$ of

forms in a %ariet$ of media, through a %ariet$ of media constructs. 6hen these media

constructs are brought into contact with the learning processes of a student, the course of

learning is influenced. The chain of deri%ing these structures is short, and contrar$ to past

formal %iews of anal$sis and design, the order of creation of lin#ed artifacts is re%ersible

and 7wa$.

The +esonant Structure

:igure > shows that the output of the 5)AI methodolog$ is a design element!the

problem structure!and that this element is related to three classes of anal$tic element

en%ironment elements, causeeffect s$stem elements, and elements of expert

performance. The arrows in :igure > show relationships that create a propert$ we call

resonance& The principle of resonance is that an$ t$pe element of the anal$sis ma$ be

used as an entr$ point for the s$stematic deri%ation of the remaining elements of the other

t$pes. :or instance, the identification of an en%ironment element leads directl$ to the

identification of s$stem process elements, related expert performance elements, and

e%entuall$ to problems that in%ol%e all of these. +i#ewise, the identification of a problem

allows the designer to wor# bac#ward to define the en%ironment, s$stem, and expert

performance re9uirements necessar$ to stage that problem for students. The basic unit of

5)AI anal$sis is this resonant structure.

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:igure >. The resonant structure of modelcentered anal$sis.

This resonant relationship exists for all four of the :igure > elements in all of the

directions indicated b$ arrows. The implication is that anal$sis does not necessaril$

proceed in a topdown manner as is true in most anal$sis methodologies but that the

anal$st ma$ mo%e laterall$ among design elements in a pattern more compatible with a

subjectmatter expertFs stream of thought. 6e belie%e that e%en traditional forms of

anal$sis proceed more or less in this fashion, e%en during anal$ses that are putati%el$

/topdown0. The anal$sis begins at some initial anchor point and wor#s outward in all

directions, sometimes wor#ing upward to a new anchor.

:igure @ shows that each of the element t$pes from :igure > participates in a hierarch$ of

elements of its own #ind. These hierarchies can be projected, as it were, on the %iews of

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a modeling language. This modeling language, which we ha%e termed an Anal$sis

5odeling +anguage A5+, is patterned after the Mnified 5odeling +anguage M5+

used b$ programmers to design complex object s$stems 8ooch, Rumbaugh, 2 Jacobsen,

'333.

:igure @. An Anal$sis 5odeling +anguage pro%iding multiple %iews into a bod$ of

expertise.

This modeling language offers four projected %iews of a bod$ of expertise a %iew of

performance en%ironments, a %iew of causeeffect s$stems hosted within the

en%ironments, and expert performances performed on the causeeffect s$stems within the

en%ironments. The fourth %iew into the bod$ of expertise consists of situated problem

structures from e%er$da$ settings that can be used for instructional design purposes.

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Iroblems in the problem %iew are lin#ed with the elements from the other %iews in

resonant relationships.

The benefit of representing the anal$sis as a set of %iews lin#ed internall$ is that the

relationships between elements within a %iew are preser%ed and can be used to further the

anal$sis. The principle of resonance allows the anal$st to mo%e between %iews, filling in

the hierarch$ on each of the %iews. The anal$st is also enabled to wor# within a single

%iew, generating upward and downward from indi%idual elements according to the logic

of that indi%idual hierarch$.

:or instance, an anal$st, ha%ing defined a s$stem process, ma$ brea# the process into its

subprocesses showing them hierarchicall$ on the same %iew and then mo%e to a different

%iew, sa$ the expert performance %iew, to identif$ tas#s related to the control or use of

the subprocesses that were identified in the first %iew. This ma$ in turn suggest

appropriate training problems to the anal$st, so the anal$st ma$ mo%e to the problem

%iew and record these problems.

The Organiation of the &ie's

The hierarchies of each %iew differ according to a logic uni9ue to that %iew

• The environment %iew hierarch$ brea#s the en%ironment into locations that can be

na%igated b$ paths. *n%ironment locations are normall$ nested within each other, and

diagrams are often the best representation of their interrelation. (owe%er, a simple

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outline form can capture this relationship also. Iaths between locations must be

captured in a supplemental form when an outline is used&

• The system vie( contains three hierarchies under a single head ' a raw component

hierarch$, 7 a functional subs$stems hierarch$, and ? a s$stem process hierarch$.

*xamples of these relationships include ' the di%ision of an automobile engine into

ph$sical components determined b$ proximit$ or juxtaposition, 7 the di%ision of an

automobile engine into sometimes ph$sicall$ isolated parts that form functional

subs$stems, such as fuel s$stem, and cooling s$stem, and ? a separate hierarch$

describing processes carried out as forces and information operate and are

transformed within the s$stem defined b$ ' and 7. The s$stem %iew in most cases

will also include a %iew of the product produced b$ expert performance andOor the

tools used to produce the product.

• The e)pert per"ormance vie( decomposes complex, multistep performances into

progressi%el$ simpler performance units according to a partsof or %arietiesof

principle. Se%eral s$stems for cogniti%e tas# anal$sis ha%e been de%eloped that

perform this #ind of brea#down. 5oreo%er, traditional tas# anal$sis accomplishes

this t$pe of a brea#down but to a lesser degree of detail and without including #e$

decisionma#ing steps. The expert performance %iew also decomposes goals that

represent states of the s$stems being acted upon.

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• The problem structure vie( contains a hierarch$ of problem structures s$stematicall$

deri%able from the contents of the other %iews using the parameteri"ed semantic

string as a generating de%ice see description below. This %iew arranges problems in

a multidimensional space according to field %alues in the string structure. As strings

ta#e on more specific modifiers the$ mo%e downward in the hierarch$.

The en%ironment, s$stem, and expert performance %iews are composed of anal$tic

elements. The problem structure %iew is composed of design s$nthesi"ed elements that

ha%e an anal$tic function, hence the connection of the problem %iew to the other three.

This ma#es the set of %iews, ta#en together, a bridge between anal$sis and design.

,ntering Analysis from (ultiple *oints

The principle of resonance allows for multiple entr$ points into the anal$sis. The anal$st

can begin b$ collecting en%ironment elements, s$stem elements, elements of expert

performance, or problem structure elements and organi"ing them into %iews, and once

information is gathered for one anal$sis %iew, resonance automaticall$ leads the designer

to 9uestions that populate each of the other %iews.

*roblem Structures: Anal$sis can begin with a set of constructs normall$ considered to be

on the design side of the anal$sisdesign watershed. This %iew of anal$sis means that as

anal$sts we can begin b$ as#ing the S5* what the$ thin# are appropriate performance

problems job situations, common crises, use cases, etc. for instruction as a means of

mo%ing anal$sis ahead, using constructs from the subjectmatter expert S5*Fs world

that are alread$ familiar. As a S5* begins to generate examples of performance

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problems, the instructional designer must translate the statements into a semantic string

form, either at the time of anal$sis or in a followup documentation period. The

instructional designer must also use the resonant relationships principle to identif$

elements of performance, s$stems, and en%ironment implicit within problem statements

and record them in their respecti%e %iews. Additional problems can be generated from

initial problems b$ formali"ing problem statements into semantic string form and

s$stematicall$ %ar$ing string slot contents to create new problem forms.

E)pert *er"ormance Structures: )urrentl$ there exist a number of tools for bothelicitation and recording of expert performance. This area has been the special focus of

anal$sis in the past for both traditional tas# anal$sis TTA and cogniti%e tas# anal$sis

)TA. TTA has tended to proceed b$ fragmenting a higherle%el tas# into lowerle%el

components. )TA has tended to loo# for se9uences of tas#s, including reasoning and

decisionma#ing steps!especiall$ those related to specific characteristics of the operated

s$stem. Ierformance anal$sis in 5)AI incorporates both of these principles, with

emphasis on the hierarchical arrangement of tas#s because of the generati%e principle it

establishes for continuing anal$sis using existing tas#s to generate new ones.

To expedite anal$sis with the S5*, a use case approach is appropriate for identif$ing

both tas# fragments and the decisions that join them into longer se9uences of

performance. A sufficient number of use cases gathered 9uic#l$ can pro%ide the anal$st

with a great deal of anal$sis detail, and in cases of restricted de%elopment time can

pro%ide a rapid anal$sis alternati%e because use cases constitute a basis for problem sets.

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Environment Structures: An en%ironment is a s$stem that is not within the immediate

scope of instruction. &n instruction that uses progressions of models as a method 6hite

2 :rederi#sen, '33;, what is initiall$ en%ironment e%entuall$ emerges into the details of

the s$stems being instructed. Therefore, en%ironment is a relati%e and d$namic construct.

&f a particular s$stem is not at the forefront of instruction, in the context of a specific

problem, it can be considered the en%ironment or the bac#ground for the problem.

*n%ironment pro%ides both setting elements and pathing elements for the processes

described in the s$stem %iew of 5)AI. An en%ironment description can be 9uitedetailed, and most S5*s tend to accept this as a standard. (owe%er, +esgold '333 and

ieras '344 ha%e recommended that both en%ironment and s$stem definitions need to

be limited to useful definitions from the studentFs point of %iew to a%oid including

irrele%ant, unusable information in instruction.

A good starting point for eliciting elements of the en%ironment is to as# the S5* for all

of the settings where s$stems exist or performances are re9uired. 1ne wa$ of capturing

the en%ironment is as a diagram using A5+. Representing an en%ironment graphicall$

helps both S5* and instructional designer ensure completeness in the en%ironment %iew

and to use the en%ironment %iew to extend other %iews b$ path tracing.

System structures: Mnderstanding the processes within a s$stem is a prere9uisite to

explaining beha%ior and outcomes with respect to that s$stem. A significant source of

operator error is the lac# of a complete and accurate s$stem model in the learner. &t is

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clear that good s$stem models are the basis for effecti%e expert performance and that as

expertise grows the nature of the expertFs s$stem models changes correspondingl$ )hi,

Glaser, 2 :arr, '344= Isot#a, 5asse$, 2 5utter, '344. :rom our re%iew of the literature

we found a number of instructional products that did not succeed as well as the$ could

ha%e because the$ lac#ed s$stem process that could be separatel$ articulated. 5P)&N

)lance$, '34>, for instance, could not gi%e explanations of expert s$stems decisions

without s$stem models. &nstruction that can con%e$ to the learner a complete model the

processes that occur within the scope of instruction can pro%ide the learner with a

complete explanation of wh$ certain phenomena were obser%ed.

&n s$stem process anal$sis three things must be identified initiating e%ents, internal

processes, and terminating indications. *%ents that initiate a s$stem process consist of a

user action or another process acting from without. &nternal processes are represented in a

number of wa$s as se9uential steps, as flow diagrams, or as principles rules that control

the flow of e%ents.

S$stem structures are captured in the form of ' a hierarch$ of s$stem components, 7 a

hierarch$ of functional units made up of indi%idual components, and ? a tracing of the

processes on the face of ' and 7 on top of the en%ironment description. Irocess

tracings form a multidimensional hierarchical form but are best captured as indi%idual

tracings, normall$ related to expert performance elements.

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The Semantic String as a Construct for *roblem Structure ,-pression

6e feel the modelcentered architecture and the modelcentered anal$sis process to be

highl$ rele%ant to a discussion of instructional objects and their nature and origin because

an$ element of the architecture and an$ element identified during the anal$sis ma$ be

treated as a t$pe of instructional object. This is consonant with the wide range of objects

of different #inds i.e., instructional, #nowledge, learning, etc. mentioned earl$ in this

chapter. 5oreo%er, we feel the problem to be a #e$ structuring object t$pe that allows the

designer to connect anal$sis directl$ with design and designs directl$ with tool

constructs.

The output of 5)AI is a set of problem structures with their resonant en%ironment,

s$stem, and expert performance primiti%es that can be used to build an instructional

curriculum se9uence. A problem structure is a complete and detailed tas# description

expressing a performance to be used during instruction, either as an occasion for

modeling expert beha%ior or as a performance challenge to the learner.

The 5)AI problem structure is a data structure. A repeating data structure of some #ind

is common to all anal$sis methodologies. This is most e%ident in traditional tas# anal$sis

in the repeating nature of tas#s at different le%els of the hierarch$ and in cogniti%e tas#

anal$sis in the IAR& unit (all, Gott, 2 Io#orn$, '33@, the regularit$ of AndersonQs rule

forms Anderson, '33?, and the regular anal$sis structures b$ the -NA and S5ART

Shute, in press s$stems. &t is li#el$ that the regularit$ of these anal$sis units is closel$

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related to a conceptual unit defined b$ 5iller, Galanter, and Iribram 5iller, Galanter, 2

Iribram, '3B; called the T1T* Test1perateTest*%aluate unit.

The 5)AI problem structure is expressed as a semantic string!created b$ merging data

fields from the other three anal$sis %iews ' en%ironment, 7 causeeffect s$stems, and

? expert performance. The semantic string expresses a generic problem structure.

-uring instruction a problem structure is gi%en specific instantiating %alues. The

semantic string does not ha%e an absolute structure and can therefore be adapted to the

characteristics of tas#s related to indi%idual projects and to trajectories of student progress. (owe%er, we belie%e the string to be conditioned b$ a general pattern of

relationships found in e%er$da$ e%entscript or schematic situations Schan# et al., '33>

in which actors act upon patient s$stems and materials using tools to create artifacts. 6e

belie%e this dramatic structure to be related to Schan#Fs Schan# 2 :ano, '337 list of

indices.

A general expression of the semantic string consists of the following

In +environment, one or more +actor, e)ecutes +per"ormance,

using +tool, a""ecting +system process, to produce +arti"act, having

+ualities,&

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to demonstrate that the$ ha%e achie%ed some degree of co%erage of some bod$ of subject

matter with their instruction.

Accountabilit$ re9uirements ha%e traditionall$ led to forms of instruction that fill

administrati%e re9uirements but ha%e little impact on performance. This is especiall$ true

when training is regulated and mandated a%iation, nuclear, power distribution, ha"ardous

waste. Accountabilit$ in these cases has been e9uated with %erbal co%erage, and a

formulaic %ariet$ of %erbal training has become standard in these situations /$uidelines

"or Evaluation o" Nuclear 1acility 2raining *rograms, '33>.

&nstructional objecti%es are normall$ used as the accountabilit$ tool in forming this t$pe

of instruction, and in some cases traditional tas# anal$sis methods are used as a means of

grounding the objecti%es in a s$stematic process to certif$ soundness and completeness.

Accountabilit$ in this atmosphere is difficult, and sometimes tas# anal$sis principles

ha%e to be stretched in order to ma#e the accountabilit$ connection.

Acceptance of problem sol%ing as appropriate form of instruction and assessment ma#es

the accountabilit$ problem harder. &t creates new problems for accountabilit$, because

the basic construct of accountabilit$ changes from the %erbal chec#off to the real and

d$namic competenc$. &nstructional designers lac# the abilit$ to express d$namic

competenc$ and also lac# a theor$ of performance measurement that would generate

appropriate performance assessments.

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The semantic string mechanism supplies a method for the description of d$namic

competenc$. 6hen the string is instantiated with specific %alues or with a range of

%alues, it expresses a specific problem or range of problems. Eariations of string %alues

ma#e this an expression of a range of performance capabilit$.

Generating *roblems and .sing /eighting To 0ocus *roblem Sets

&nstructional problems are generated computationall$ using the semantic string b$

defining a range of %alues for each field in the string and then s$stematicall$ substituting

%alues in specific string positions. Generation of problems using the semantic string ta#es

place in two steps ' insertion of %alues from the hierarchicall$organi"ed %iews into the

string to create a problem, and 7 selection of specific initial %alues that instantiate the

problem. This results in a geometric proliferation of possible problems, so mechanisms

capable of narrowing and focusing problem sets into se9uences are important.

This is accomplished b$ selecting string %alues depending on the principle the designer is

tr$ing to maximi"e within a problem se9uence. A few possible se9uence principles are

gi%en here as examples

• %a)imum coverage in limited time !String %alues will be selected with the minimum

of redundanc$. *ach problem will contain as man$ new elements in string positions

as possible.

• Cognitive load management3 String %alues will be selected in terms of their addition

to the current cogniti%e load . &ncreases ma$ be due to increased memor$

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re9uirement, coordination of conflicting sensor$ demands, integration of parallel

decision processes, or a large number of other possibilities. *ach string element is

judged according to its contribution to load.

• Integration o" comple)es o" prior learning !String %alues are selected as

combinations of elements from each of the %iew hierarchies that practice alread$

mastered areas of the hierarchies in new combinations.

• 4econte)tuali5ation o" s!ills3 String %alues are selected so that the$ %ar$

s$stematicall$, preser%ing expert performance elements but %ar$ing en%ironment and

s$stem elements as widel$ as possible. )ore performances are retained in the string

but to them are added as wide a %ariet$ as possible of nonrelated performances.

• *ractice to automaticity3 String %alues are #ept as unchanged as possible with the

exception of the conditions in the en%ironment, which change in terms of timing

factors where possible.

• 2rans"er3 String %alues for expert performance change along a dimension in which

performances in the se9uence contain similar elements. *n%ironment and s$stem

string elements are made to %ar$ widel$.

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• #is! a(areness3 String %alues are selected on the basis of weightings attached to

performances, s$stem processes, and en%ironmental configurations that ha%e

historicall$ posed or ha%e the potential for posing ris#s.

6hen string %alues ha%e been selected, indi%idual problems are instantiated b$ the

designer b$ specif$ing data that situates the problem. This data includes

• Environment con"iguration data !-ata that describes the specific en%ironment in

which the problem will be presented to the learner.

• Environment initiali5ation data !-ata that describes %ariable %alues of the

en%ironment at problem initiation.

• System con"iguration data !-ata that describes the configuration of s$stems that the

student will interact with or obser%e.

• System initiali5ation data3 -ata that describes %ariable %alues of the s$stems at the

beginning of the problem.

• *roblem history3 -ata that describes the histor$ of e%ents that has brought the

problem to its present state.

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• *roblem end state data3 -ata that describes the states of s$stem and en%ironment at

the end of the successfull$ concluded problem.

+elation to Instructional Objects

&nstructional objects, under their se%eral names, are often referred to in the literature as if

the$ were a welldefined, unitar$ element. (owe%er, the$ must be seen in terms of their

place in an architectural hierarch$ capable of finding, comparing, and selecting them and

then joining them together to perform an orchestrated instructional function that re9uires

more than a single object can accomplish unless it is a selfcontained instructional

product. An architectural superstructure capable of emplo$ing objects in this wa$ is

described in the +earning Technolog$ S$stems Architecture +TSA :arance 2 Ton#el,

'333.

This architecture will re9uire a %ariet$ of object t$pes, some of them merel$ content

bearing, but some of them consisting of functional instructional subunits of man$ #inds,

including in man$ cases interacti%e models and related sets of problems defined with

respect to the models.

Ieters, for instance, describes how /H#nowledge objectsF enabled b$ CanD emergentclass of digital libraries will be much more li#e experiencesF than the$ will be li#e

thingsF, much more li#e programsF than documentsF, and readers will ha%e uni9ue

experiences with these objects in an e%en more profound wa$ than is alread$ the case

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with boo#s, periodicals, etc.0 Ieters, '33@. This in turn suggests the need for model

components that can be brought together in %arious combinations to create the

en%ironments and s$stems for progressions of problems.

:or example, a telephone switch manufacturer, in order to align training objects with

training content and to reuse the same objects in multiple training contexts, might create

models of three different I8s switches and two different telephone sets, a des# set and

a handset, that can be connected in different configurations. The same models could be

used for numerous training problems for the installer, the maintainer, and the operator.The independent problem sets themsel%es a t$pe of instructional object would consist of

the list of models to be connected together for a particular problem initial %alues,

terminal solution %alues, and instructional data to be used b$ independent instructional

functionalities coaches, feedbac# gi%ers, didactic gi%ers in conjunction with the

problems. The instructional agents, of course, would be a %ariet$ of instructional object

as well.

5)AI pro%ides a shared methodolog$ basis for deri%ing suites of model objects

interoperable not onl$ with themsel%es but with instructional agents that share the model

centered instructional %iewpoint. The important idea here is not that modelcentered

principles are right for all occasions but that the creation and use of instructional objects

benefits from a process capable of coordinating instructional assumptions with object

outlines and connections.

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This is the principle we ha%e been exploring with 5)AI. 6e ha%e found it useful to

describe a modelcentered instructional product architecture that aligns with se%eral

la$ers of design see :igure ' instructional models, instructional strategies, instructional

problems, instructional message elements, representation, and medialogic. At the same

time it allows these le%els of design to be integrated into running products, it allows them

maximum portabilit$ and reusabilit$ in a number of modes. &nstructional functions can be

added independentl$ of model function. The modelcentered architecture is illustrated in

:igure B below and includes

• A problem solving environment that contains e%er$thing

• A problem environment that contains informationbearing locations

• The paths for na%igating between locations

• Cause-e""ect or event models in%isible to the %iewer

•

Controls and indicators within locations, connected to the models

• 1ne or more problems to be sol%ed within the problem en%ironment

• 5odels of e)pert per"ormance that can be obser%ed

• #esources that suppl$ information for use in problem solution

• 2ools that can be used to generate information or to record information

• Instructional augmentations that offer coaching, feedbac#, interpretations,

explanations, or other helps to problem sol%ing

>


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+elation to the Goals of CBI! Adapti"ity# Generati"ity# and Scalability

5ost importantl$, lin#ing the origin of instructional objects to the design process!

through anal$sis and design constructs!appears to change the anal$sis and design

process itself in a wa$ that produces primiti%es that can be used to meet the )8& goals of

adapti%it$, generati%it$, and scalabilit$.

Adaptivity is obtained as independent instructional objects are assembled and

implemented in response to current learner states. The granularit$ of adapti%it$ in objectarchitectures will correspond to the granularit$ of the objects themsel%es and the

instructional rules that can be generated to control the operations of objects. 5)AI is

flexible with respect to granularit$ because it represents elements of en%ironments,

s$stems, and expert performance at high le%els of consolidation or at %er$ detailed and

fragmented le%els. The granularit$ of object identification can be adjusted to an$ le%el

between these extremes. This is one of the characteristics that allows 5)AI to pro%ide

useful anal$sis and design functionalit$ to both smallscale and lowbudget de%elopment

projects that will use instructors and o%erhead projectors as well as the largescale,

wellfinanced ones that ma$ at the high end %enture into intelligent tutoring methods.

$enerativity is also fa%ored b$ an anal$sis that identifies at a high le%el of granularit$ the

terms that might enter into the instructional dialogue at an$ le%el. Generati%it$ is not a

single propert$ of computerbased instructional s$stems but rather refers to the abilit$ of

the s$stem to combine an$ of se%eral instructional constructs with tool and material

>4


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constructs on the fl$ instructional model suites, instructional problems and problem

se9uences, instructional strategies, instructional messages, instructional representations,

and e%en instructional medialogic. The semantic string method of problem expression!

though computationall$ impractical without ade9uate data to guide the generation of

problems!can, if pro%ided with that data, lead designers to the generation of progressi%e

problem sets and can ma#e possible for computerbased s$stems the generation of

problem se9uences.

:igure B. 5odelcentered instructional architecture.

Scalability in%ol%es production of 9uantit$ at specified le%els of 9ualit$ within specified

time and resource constraints. &t also re9uires an increase in producti%it$ without a

proportional increase in production cost. &nstructional object technolog$ cannot now

pro%ide scalabilit$ because the infrastructure of s#ills, tools, and processes is not

a%ailable that would support this. Scalabilit$, howe%er, is one of the main arguments

pro%ided in promoting instructional object economies Spohrer, Sumner 2 Shum '334,

>3

Iroblem Sol%ing*n%ironment

5odel

*n%ironment

+

+

+

S$stem

Resources

ools

Augmentation

*xperts

*xpert

Ierformer

Iroblems


50/114

and ade9uate technologies for designing and using objects will modif$ instructional

de%elopment costs in the same wa$ that roads, gas stations, and mechanic shops modified

automobile costs.

Conclusion

1ur purpose has been to set instructional objects within a design process context.

Standardi"ation efforts related to object properties and indexing will open the floodgates

for object manufacture and sharing, but without attention to design process,interoperabilit$ among all the necessar$ %arieties of instructional objects and the

fa%orable economics needed to sustain their use will not materiali"e.

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a naturalea y el origen de los Objetos de Instrucci2n C'D

Andrew S. Gibbons

Jon Nelson

Universidad del Estado de Utah

Robert Richards

Idaho National Engineering Laboratory y del %edio Ambiente

Introducci2n

*n este captulo se examina la naturale"a $ el origen de una construcciUn 9ue llamamos

el objeto de instrucci@n& *n lugar de ser un Vnico objeto definible, se trata de un complejo

$ de mVltiples facetas emergentes tecnolUgica constructo de una pie"a de un

rompecabe"as tecnolUgica mWs grande. +as lneas maestras de la pie"a del rompecabe"as

estWn tomando forma simultWnea en las di%ersas disciplinas de las 9ue las prWcticas de la

tecnologa educati%a son la ciencia por ordenador deri%ada, tecnologa de la informaciUn,

sistemas tutoriales inteligentes, $ psicologa de la instrucciUn. +a terminologa utili"ada para describir esta nue%a idea refleja sus mVltiples orgenes, sus moti%aciones di%ersas, $

su no%edad. *n la literatura lo 9ue nos referiremos como el objeto de instrucciUn se

denomina di%ersamente objeto de instrucciUn, objeto educati%o, objeto de

aprendi"aje, objeto de conocimiento, objeto inteligente $ objeto de datos. Nuestro

@3

https://translate.googleusercontent.com/translate_f#_ftn1https://translate.googleusercontent.com/translate_f#_ftn1


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trabajo estW mWs fuertemente influenciada por el trabajo de Spohrer $ sus asociados en las

economas de objetos educati%os Spohrer, Sumner $ Shum, '334.

Se ha escrito mucho acerca de los objetos de instrucciUn, pero poco acerca de cUmo se

originan los objetos. *ste captulo examina los objetos de instrucciUn en el contexto de un

espacio complejo diseXo de la instrucciUn. Iroponemos las dimensiones de este espacio $

usar eso como un fondo para relacionar juntas las mVltiples definiciones del objeto de

instrucciUn. +uego tratamos de situar la nue%a construcciUn en un contexto de acti%idades

de diseXo 9ue difiere de la %isiUn tradicional proceso de diseXo. Terminamos con la

descripciUn de los criterios $ directrices metodolUgicas para la generaciUn de objetos.

)omo el objeto de instrucciUn sigue asumiendo definiciUn $ proporciones, $ como el

trabajo en muchos campos con%erge, creemos 9ue los objetos de instrucciUn en alguna

forma se con%ertirWn en un factor importante en el crecimiento $ proliferaciUn de

instrucciUn basada en la tecnologa informWtica $ de apo$o al rendimiento.

An3lisis y objetos de Instrucci2n

*l objeti%o a largo pla"o de esta in%estigaciUn es la consolidaciUn de una teora de diseXo

de instrucciUn 9ue utili"a el modelo como un constructo central de diseXo. Mna base, tal

apo$arW futuras in%estigaciones sistemWticas sobre las %ariedades de productos,

ar9uitecturas de productos, la eficiencia de producciUn $ herramientas de producti%idad

especiali"ados. Al hacerlo, tenemos la esperan"a de %incular la prWctica de los

diseXadores de instrucciUn con nue%as construcciones de diseXo 9ue implica la %isiUn

actual de la instrucciUn 9ue estWn cambiando hacia centrado en el estudiante, situada,

basado en problemas, $ centrado en el modelo experienciasones 9ue tambiYn estWn

conformadas por las exigencias de escala $ eficiencia de la producciUn.

B;


61/114

)reemos 9ue esta discusiUn es oportuna. A pesar de 9ue el uso de instrucciUn de la 6orld

6ide 6eb se estW promo%iendo cada %e" mWs urgente, existen serias dudas acerca de si

estW totalmente pro%ista de conceptos de diseXo, ar9uitecturas $ herramientas 9ue se

adapten a ella para el ser%icio como un canal para instruir en lugar de limitarse a informar

:airweather $ Gibbons, 7;;;. Al mismo tiempo, los teUricos del diseXo de instrucciUn

estWn cuestionando las suposiciones sub$acentes metodologas de diseXo existentes 9ue

estWn demostrando frWgiles en %ista de los desafos planteados por los modos de

instrucciUn mWs recientes Gordon 2 = Rowland, '33?. *l objeto de instrucciUn se ha propuesto dentro de los

diferentes campos de especialidad para sus beneficios de producti%idad, por sus %entajas

de la normali"aciUn, $ como un medio de hacer un diseXo accesible a un creciente

ejYrcito de desarrolladores no entrenados. A medida 9ue e%oluciona el proceso de diseXo

de una base teUrica, creemos 9ue es importante preguntar cUmo esa base de la teora

puede estar relacionada con los objetos de instrucciUn.

,st3ndares y Tecnolog4a CBI

+a industria 9ue se centra en el diseXo, desarrollo $ entrega de instrucciUn computari"ada

estW actualmente en un perodo de establecimiento de normas se centrU en la distribuciUn

de experiencias de enseXan"a a tra%Ys de &nternet $ la 6orld 6ide 6eb. *l objeto

indexados por instrucciUn de metadatos tiene un gran potencial como un blo9ue de

construcciUn comVn para una amplia gama de productos de instrucciUn basados en la

tecnologa. *sfuer"os masi%os 9ue afectan a cientos de profesionales, pro%eedores $

consumidores estWn contribu$endo a oponerse normas 9ue permitirWn este blo9ue de

construcciUn para con%ertirse en la unidad bWsica del comercio en la instrucciUn $ apo$o

al rendimiento (ill, '334.

B'


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*s difcil resistir la comparaciUn de estos e%entos con e%entos en la historia de la

tecnologa de fabricaciUn de acero. )uando :rederic# Ta$lor demostrU en los primeros

aXos del siglo 9ue las recetas fiables para el acero se colocaran en las manos de los

operadores de hornos relati%amente inexperto 5&SA, '33@, un ejYrcito de nue%os $

menos entrenados, pero plenamente competentes operadores de los hornos comen"U a

hacerse cargo de los molinos. 5a$ores cantidades de acero escala industrial podran

producirse a ni%eles controlados de forma mWs precisa la calidad. Tres e%entos cla%e en la

expansiUn de la fabricaciUn de acero Ypocas in%olucradas de establecimiento de normas

lle%ada a cabo por tres diferentes coaliciones de normas. -urante %arias d

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