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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.
>B
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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
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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
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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.
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)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$o
al rendimiento (ill, '334.
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*s difcil resistir la comparaciUn de estos e%entos con e%entos en la historia de la
tecnologa de fabricaciUn de acero. )uando :rederic# Ta$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