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Chapter 56: Technology-Based Instructional Design –
Evolution and Major Trends
Gilbert Paquette
CICÉ Research Chair, LICEF Research Center,
Télé-université, Montreal, Canada
gilbert.paquette@licef.ca
www.licef.ca/gp
Abstract
This chapter surveys ICT-based tools and methods that support instructional designers in
planning the delivery of learning systems. This field has evolved since the seventies
through several paradigms: authoring tools, expert systems and intelligent tutoring
systems, automated and guided instructional design, knowledge-based design methods,
eLearning standards and social/cognitive Web environments. Examples will be given to
illustrate each paradigm and the major trends will be uncovered. ICT has evolved rapidly,
enabling new approaches to emerge, helping more people to design learning
environments and building learning design repositories. More and more people are
learning on the Web, using learning portals, information pages and interacting with other
people, but still with insufficient educational support. New challenges make this field an
exciting and blooming research area that has a bright future.
Keywords
Instructional Design, Instructional Engineering, Knowledge-based Design, Educational
Modeling, eLearning Standards, Web-based Learning Environments.
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Chapter 56: Technology-Based Instructional Design –
Evolution and Major Trends
Introduction – Defining the field
Some authors trace the origin of Instructional Design to John Dewey, who, a century ago,
“called for the development of a linking science between learning theory and educational
practice” (Reigeluth 1983, p. 5; Dewey 1900). Others (Dick 1987) situate the beginning
of ID after World War II. But it is really at the beginning of the Sixties, that we see the
beginning of the new discipline, mainly under the influence of the work of B.F. Skinner
on programmed instruction, Jerome Bruner on the cognitivist approach and David
Ausubel (Reigeluth, 1983). In the Seventies and Eighties, research on instructional
theories blossomed: the development of a cybernetic approach (Landa, 1976), the
exposure of learning conditions (Gagné, 1985), the identification of instructional
strategies based on structural learning theories (Scandura, 1973), the development of a
cognitive teaching theory based on enquiries (Collins and Stevens, 1983), the analysis of
instructional strategy components (Merrill, 1994).
Based on these various research efforts, Instructional Design is today a collection of
theories and models helping to understand and apply instructional methods that favor
learning. Instructional Design as a method or a process helps produce plans and models
describing the organization of learning and teaching activities, resources and actors’
involvement that compose an Instructional System or a Learning Environment.
Compared to the theories developed in educational psychology, instructional design can
be seen as a form of engineering aiming to improve educational practice. Its link with
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educational science is analogous to the link between engineering methods and the
physical sciences, or between medicine and life sciences.
The life cycle of a Learning Environment is presented on figure 1. This figure shows four
main processes going from creation or design, production of a learning environment, and
then to its delivery. Finally, a maintenance and revision process serves to detect
deficiencies revealed by the delivery of the learning system, leading to improvements
proposed to the instructional designers, closing up the loop and starting a new cycle.
Figure 1. The basic life cycle of a learning environment
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Figure 1 also shows the products of each process and the main actors that produce them.
While there is a sequential progression between these main processes, it is best to picture
the global process with sub-processes more or less parallel, sharing information between
them with frequent interaction between the actors. In this chapter, we will focus on the
Instructional Design process, methods and support tools, but in some case, we will
identify the interaction of “pure” ID with the other three processes, in particular with the
production process.
Using this general picture of an instructional system, the following sections will present
the main paradigms that propose ways to use information and communication
technologies (ICT) to support the instructional design process. These paradigms are
authoring tools and languages, knowledge modeling of instructional design methods,
automated and guided instructional design, eLearning standards and social/semantic
Web environments. Finally, in the last section, we identify the major trends and issues,
synthesizing the evolution of Technology-based Instructional Design.
56.1 Authoring Tools and Languages
The use of computers in education started fifty years ago, at the beginning of the 1960s.
The first applications were influenced mainly by programmed instruction strategies
(Skinner 1954; Crowder 1959). Most authoring tools and languages for Computer-
assisted Instruction were limited to present information, ask a question and branch to
another unit. Two early authoring systems attempted to go beyond such simple templates,
in order to provide more complete learning strategies.
One specialized programming languages, TUTOR, was developed starting in 1965 for
use on the PLATO system at the University of Illinois at Urbana-Champaign. TUTOR
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had powerful answer parsing and answer judging commands, and it had features to
simplify student records by instructors. TUTOR's flexibility, in combination with
PLATO's computational power (running on what was considered a supercomputer in
1972), also made it suitable for the creation of games and simulations that could be used
for learner-centered education. Later on, templates were developed to ease the
programming part of courseware creation. For example, (Schulz 1975) presents
MONIFORMS, a set of partially completed coding formats in the TUTOR language that
could be adapted by instructional designers in order to implement instructional tactics.
The TICCIT system (Merrill et al., 1980) attempted to provide built-in complex
instructional templates in the mid 1970’s. The student had access to a set of learner-
controlled keys: Rule, Example, Practice, Objective, Help, Advice, Easy, Hard and Map.
The author provided information accessible behind these keys, to be displayed to the
student studying some the rules and concepts for which the information provided. The
system also provided a map or hierarchy diagram from which the student could choose
the next content to study, but with some help from the system.
With the advent of multimedia and Internet technologies, there has been and explosion of
the number of authoring tools. Widely used commercial tools have included
Macromedia’s Authorware, IconAuthor and Click2Learn’s ToolBook. More recent
Learning Content Management System (LCMS), such as BlackBoard, Learning Space,
TopClass, WebCT and Moodle, are totally oriented towards building Web-based courses.
There has been also a proliferation of authoring tools providing templates. However, not
many of them offer multiple instructional strategies (Liao et al., 2003).
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Moreover, while LCMSs, authoring tools or templates help produce resources for
delivery environments based on the more or less limited set of strategies they support,
they are essentially helping in the production process. They do not provide much support
for instructional designers to analyze learning needs, structure target knowledge and
competencies, integrate resources in learning scenarios or plan the production of resource
and delivery environment. In particular, they provide no help to select teaching/learning
strategies before deciding which authoring tools or templates should be used.
56.2 Modeling Instructional Design and Job Aids
With the evolution of technology-based learning, the instructional designer must make a
larger set of interrelated decisions. What kind of delivery model shall we use: classroom,
Web-based, blended? What kind of learning activities do we need for this course? Should
it be predefined, offer multiple learning paths or be learner-constructed? Which actors
will interact at delivery time, what are their roles, what resources do they need? What
kind of interactivity or collaboration should be included? What materials can be reused,
adapted or built anew? How distributed resources are to be managed on the networks?
What kind of eLearning standards will be used? How can we support interoperability and
scalability of the learning system? How can we promote their reusability, sustainability
and affordability? To cope with all these decisions and others, an instructional design
methodology and a tool set are needed more than ever.
The MISA instructional systems engineering method (Paquette et al, 1994, 1999, 2004) is
a long-term effort to address these new needs of the instructional designers. It has
provided a mature methodology at the turn of the century that continues to evolve. As
shown on figure 2, MISA is structured into six phases and four axes under which the
main 35 design tasks and their subtasks are distributed. The four axes are deployed from
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phase 2 to 6. Each axis is based on one or more visual model built with the MOT
language and editor (Paquette 2002, 2010). The other tasks either prepare the
construction of the model or document its properties.
Figure 2. Overview of the MISA instructional system design method.
The MISA method is the result of applying knowledge engineering to the instructional
design domain. Using the MOT language and editors, the products, the task and the
principles of instructional design have been modeled and their interactions identified. The
relationship between tasks is represented using a process graph for each of the phases and
each of the axes. The design documents produced by each of the 35 main tasks are
modeled as concept objects with a certain number of attributes that have well-defined
values. The knowledge model describing MISA ensures the consistency of the method. It
also help guide the navigation of the designer through the method. Contextual help or
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intelligent advice can be given by a supervisor or a software agent for each design task,
based on the relationships between it and the other tasks in the method and also on the
consistency of values for the different attributes in a design document.
The complete model of the MISA method enabled the production of computerized Job
aids or design tools. The first one was AGD, a standalone performance support system
for ID (Paquette et al., 1994). Later on, an improved version of MISA enabled the
construction of Job aids as a set of Word and Excel templates, supplementing the MOT
visual knowledge editor. In 2001, a WEB tool, ADISA was built and will be presented in
the next section. More recently, MISA/ADISA design scenarios can be edited and
processed by the ontology-driven TELOS system (Paquette and Magnan, 2008).
56.3 Expert Systems and Automated/Guided ID
Beginning also in the 1990s, expert systems and Artificial Intelligence techniques started
to be applied to the field of Instructional Design (ID) to provide methodological support
and intelligent help (Winkels 1992) to instructional designers. Many expert systems were
built for focused ID tasks where they have had generally more success than more general
applications (Locatis and Park, 1992). A second category of systems is concerned with
helping designers construct Intelligent Tutoring Systems (Wenger 1987); the Generic
Tutoring Environment (GTE), is a good representative of that category of system (Elen,
1998). We will here focus on a third category of Expert System applications that aim to
support the general Instructional Design process. We present here three of them:
• ID Expert (Merrill, 1998), an expert system for designing courseware, which
evolved into a commercial system called Electronic Trainer;
• GAIDA/GUIDE (Spector et al., 1993) provides a guided approach to ID
Advising;
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• ADISA (Paquette et al. 2001) that supports the operation of the MISA
Instructional System Engineering method (presented above) with linked models
and templates, and the intervention of an intelligent advisor.
The purpose of ID Expert and Electronic Trainer is to provide a consultation system that
could be used by inexperienced instructional designers to assist in instructional design
decision-making, prior to the programming stage. The expert system gathers information
from the user/designer and makes recommendations on the goal of instruction, the
content structure that corresponds to the goal, the elaboration of the content structure, the
modules that are necessary for teaching the content, the instructional transactions that are
best for each module and guidance for elaborating and instantiating each transaction. The
output of the consultation is a design specification that provides a skeleton from which
instructional materials can be built. The domain of the first ID Expert was limited to
goals involving concept classification with a kind-of taxonomies content structure and
goals involving procedures for device operation with a path algorithm content structure.
ID expert 2.0 extended the initial set of goals and provided a delivery interface. The
commercial Electronic Trainer linked the ID expert to authoring capabilities that
produced the corresponding learning material. Unlike many expert systems, which are
directed toward a single main decision, the ID expert makes recommendations on a series
of decision and allows the designer to confirm each recommendation as the reasoning
proceeds.
The GAIDA advisory system was developed to support lesson design as part of the
Advanced Instructional Design Advisor project at Armstrong Laboratory (Spector et al.,
1993). The system uses completely developed sample cases to help less experienced
instructional designers construct their lesson plans. GAIDA is designed explicitly around
the nine events of instruction (Gagné, 1985). It allows users to view a completely worked
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example, shown from the learner’s point of view (see Figure 3). The user can shift from
this learner view to a designer view that provides an elaboration of why specific learner
activities were designed as they were.
Figure 3. A screen from GAIDA/GUIDE.
ADISA is the successor of the AGD system. It is a Web-based system developed to
enhance the performance level of instructional designers, in particular to assist teams who
create Web-based distance learning courses. It embeds a large set of educational
knowledge including 17 typologies of educational concepts from the MISA 4.0 method,
each offering a set of options for the designer to choose from. It provides an editing part
for 35 documentation elements (DE), either forms or graphic models to be produced by
tasks of the MISA method. An important feature is the data propagation from one DE
form or model to another, based on the MISA 4.0 process models.
What can be learned from the research on automated or semi-automated ID systems?
First, productivity improvements have been observed due to performance support
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functionalities and/or automated ID knowledge (Merrill, 1998; Spector et al., 1993).
While results vary, using design support tools can achieve an order of magnitude
improvement in the productivity of a design team. Second, learning can result for
designers using such systems. GAIDA has been evaluated in numerous settings with both
novice and expert designers (Gettman, McNelly & Muraida, 1999). Findings suggest that
expert designers found little use for GAIDA, whereas novice designers made extensive
use of it for about 6 months and then no longer felt a need to use it. MISA/ADISA has
been used by novices and experienced designers for a variety of domains ranging from
well structured to ill-structured knowledge domains (e.g., training lawyers). Paquette and
colleagues (2004, 2010) found consistent improvements in both productivity and
consistency of the ID products. But probably the most important result gained from these
systems is the deeper understanding of ID concepts, processes and principles. To build
these systems, operational expertise in ID must be uncovered, implemented, validated
and again improved in successive versions of a system through its use in various
knowledge domains.
56.4 eLearning Standards for ID
As the number of ICT-based learning platforms or authoring tools increases during the
years, reusability has become more important. The goal is to enable the reuse of learning
objects (or resources) in new educational contexts across a variety of e-learning delivery
systems. This goal requires standard ways to describe and store learning objects or
educational resources. The elaboration of international standards for learning resources
has been initiated by organizations such as IMS global, IEEE-LTSC, AICC and ISO.
Duval & Robson (2001) presented a review of the earlier phases in this evolution of
standards including the Dublin Core metadata initiative up to the publication of the
Learning Object Metadata (LOM) standard by IEEE in 2002. Since then a host of other
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specifications have been published by IMS Global
1
. ISO has started publishing at the end
of 2010 the first documents of its new Metadata for Learning Resource (ISO-MLR 2012)
standard, based on the W3C (2004) Resource Description Framework (RDF)
2
.
The work on Educational Modeling Languages (Koper 2001), and the subsequent
publication of the IMS Learning Design Specification (IMS-LD 2003; Koper and
Tattersall 2004, Griffiths et al., 2005), is the most important initiative to date, to integrate
Instructional Design modeling into the international standards movement. This
specification is a formal way to represent the structure of a Unit of Learning and the
concept of a pedagogical method. A Basic Learning Design involves three kinds of
entities with relations between them: actor’s roles, activities and environments grouping
learning resources and services. Activities, performed by actors are organized in a tree
structure called a method, decomposed into alternative plays, each decomposed into a
series of acts, further decomposed into activity structures down to terminal learning or
support activities.
IMS-LD embeds and generalizes other IMS specifications such as MD (metadata), SS
(simple sequencing), CP (content packaging), RDCEO (learning objectives and
prerequisites), QTI (questionnaires and tests), LIP (learner information profile) and
others. SCORM, the Sharable Content Object Reusable Model supported by the ADL
Technical Team (2004), can be seen as a specialization of IMS-LD to single-user simpler
hierarchical activity structures. IMS-LD expands SCORM specifications in many ways:
• IMS-LD describes methods as multi-actor workflow processes;
1
All available at http://www.imsglobal.org/specifications.html
2
http://www.w3.org/2004/01/sws-pressrelease.html.fr
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• IMS-LD can provide alternative plays adapted to different target populations;
• IMS-LD integrates the description of collaboration services;
• IMS-LD integrates (at Level B and C) some user modelling and cross-users
notifications;
• Most important, IMS-LD favours instructional strategies like collaborative
learning, problem solving, project-based learning, communities of practices, and
multi-facilitators support as found in more advanced learning strategies.
With regard to the tool set, a form-based tool, RELOAD (2004), was an improvement
from previously used XML editors, but it imposes too many constraints on the design
process. Visual representation techniques and tools aim to free instructional designers
from these constraints. Although well suited for software engineering purposes, UML
graphs and diagrams, as proposed by the Best Practice and Implementation Guide (IMS-
LD 2003), pose many difficulties for instructional design. There exists more user-
friendly instructional visual design software like LAMS (Dalziel 2005), or the first MOT
knowledge editors. These are useful in an inception phase, but cannot produce compliant
IMS-LD executable files. This has led the construction of new visual design tools like
the MOT+LD specialized editor (Paquette et al, 2005) and, more recently, the G-MOT
scenario editor, the central aggregation tool in TELOS (Paquette, 2010).
Besides their strong influence on the standardization and interoperability of authoring
tools, IMS-LD and other eLearning standards have also helped stress the importance of
Instructional Design. IMS-LD is just a reusability format, but it has opened the spectrum
of possible learning strategies that can be supported by standardized authoring tools. So
the need becomes more evident for front-end methods and tools to support designers in
producing high quality Learning Designs. Furthermore, the Learning Object Paradigm
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has move the focus towards aggregating resources and interactions, instead of producing
more text, multimedia or web-based document. In this new approach to ID, the learners
and the facilitators as resources themselves, interacting together within activities using
and producing learning resources, a more cognitive and constructivist process than
simple information transmission.
56.5 Social/Semantic Web Environments
In the last decade, the now ubiquitous Web has evolved through overlapping generations
that are most of the time called the Information Web, the Social Web (Web 2.0) and the
Semantic Web (Web 3.0). Web 2.0 technologies are there to stay because they make the
use of Internet a brand new social experience, just as the first Internet browser did fifteen
years ago with information access. Semantic Web technologies have the same potential to
dramatically improve Web 2.0 activities that are often limited to superficial chats or
simple information transmission. The new Web 2.0 and Web 3.0 technologies have an
enormous potential if they are blended to support knowledge-intensive social processes.
This is now a very active research area internationally that corresponds to individuals’
and organizations’ needs. Here are a few research orientations that will orient the future
of Web 2.0/3.0 learning environments and learning design:
1. Modeling knowledge-Intensive social processes. Both for work and educational
scenarios, much attention is given today to multi-actor workflows, but leaving aside
the crucial issue of knowledge and competency acquisition that occur during these
processes. On the contrary, Knowledge and competency models must be at the
forefront of the new learning environments to enable a transfer of competency from
content experts to learners or to novice workers through collaborative knowledge
exchanges. Unexplored research problems occur when the scenario or workflow is
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built while collaborating, in an emergent way such as in project-based learning where
the learners becomes their own designer.
2. Taking in account knowledge contexts of use, privacy and trust issues in collaborative
learning processes. A huge amount of information is available for learning but it is
locked from potentials users due to security and privacy concerns. These problems
must be solved especially for the mobile learners whose location, device limitations,
and task at hand change all the type. Context model must be linked to task models and
knowledge/competency models.
3. Personalizing learning environments and creating more intelligent tools. Nowadays,
the abundance and popularity of Web applications, such as blogs, discussion forums,
social and professional networks pose a great challenge. Web personalization and
recommender systems are two important areas that attempt to cope with such
information overload problems. Web personalization systems organize the Web
environments based on the users personal interests and preferences. Recommender
systems suggest information, products or peer-to-peer communication in accordance
with the user’s personal demands and properties.
4. Building Semantic Media User Interface. The continued growth and importance of the
Social Web has resulted in information taking many forms, including text, images,
video and more recently augmented or virtual reality environments such as Second
Life. Furthermore, this information is accessible through desktop and laptop
computers, and through “intelligent” mobile phones or tablets that bring unique
constraints in terms of computing resources and user interfaces. The vast amounts of
data coming out of the Social and Semantic Web entails a need for more intelligent
human interfaces and visualization capabilities.
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5. Aggregating Social-Semantic tools into Learning Environments. Data “Mashups”
have been identified by the Horizon study (2008)
3
as one of the leading trends for
2010-2011. Using social environments like Facebook or Wikipedia, users become
Web designers, assembling text, picture, sound according to their needs. The quality
for learning issuses then comes at the forefront, while the impact of these new
situation on ID methods and tools must be investigated.
The Social and Semantic Web shapes the new learning environments, posing new
challenges to Instructional Designers, fostering the need for new advances in the ID
methodology and tool set. One interesting approach is to see Instructional Design as a
knowledge-intensive collaborative multi-actor process where the actors interact within a
Web 2.0/3.0 environment to assemble actors, activities and resources for learning or
knowledge management.
In such a setting, personalized assistance must be given both to designers and to the user
of the learning environments they produce based semantic Web techniques, an area part
of the Adaptive Semantic Web (Dolog and al 2003) that we call Ontology-based
Assistance Systems. Recent research on assistance systems at LICEF (Paquette and
Marino, 2011) proposes that advisor agents be grafted on environments/scenarios, built in
the context of the TELOS system (Paquette, Rosca et al, 2006; Paquette and Magnan,
2008). TELOS is a service-oriented, ontology-driven system that helps build on-line
environments for learning or for work. Its basic principle is the aggregation of resources
into visual activity scenarios. In TELOS, the task model (the scenario) may represent
multi-actor processes or workflows integrating a variety of control patterns between tasks
3
http://www.nmc.org/pdf/2008-Horizon-Report.pdf
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or activities such as splits and joins. These scenarios can be intended for any kind of
actors: for engineers who aim to extend the services given by the system, for
technologists who build designers’ platforms, for designers who built courses or work
scenarios and for the final users who interact in these scenarios.
Figure 4 presents the upper graph of a design process (build by an educational
technologist) to help designers produce IMS-LD compliant designs: in the first activity, a
designer produces the upper structure of a learning scenario (i.e. a method); in the second
one, each Act in a Method is identified and defined; in the third one, a scenario model is
build of each act as well as a knowledge/competency model and the association between
the two structures. This third activity has a complex sub-model not shown on the figure
where knowledge and competencies are associated to actors, activities and resources,
Figure 4. A multi-actor design scenario.
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When such a scenario is executed by TELOS, a Web environment is produced for the
members of a design team to help them produce a learning environment model intended
for learners and facilitators, to be run in the same way by the TELOS system.
56.6 Trends and ID Issues
As a conclusion, I present here four trends in methods and tools for Instructional Design
with a set of corresponding issues that present today a challenge to the field.
From Tutoring to Open Learning Design. As shown in section 1, at the advent of ICT
in learning, it seemed natural to use ICT for the creation of learning programs. The terms
CAI (Computer Aided Instruction) and CBT (Computer-Based Training) put the focus on
instruction instead of learning. In this paradigm, the computer program was the teacher or
a teacher aid, displaying information, asking questions and presenting more information
depending on the learner’s answers to previous question. Respecting the learners’ pace
and adapting to its answers was advocated in support for this approach. But soon, ICT in
education evolved towards a more learner-oriented focus. Typically, learners would
interact with computerized simulations and games, solve problems by programming the
computer, search for relevant information or realize projects using software tools like
text/graphic editors, database or spreadsheets. Nowadays, even though there are many
programmed instruction coursewares, useful in limited cases, the trend is clearly towards
more open environments where the learner uses the computer as a tool instead as a static
and rigid teacher. Typically, a set of ordered activities, a scenario, is provided on the
Web, where the learner is invited to find useful information on the Web, to use computer
tools or to program the computer to address some question. Supporting this trend, the
Web acts as a universal encyclopedia, provides a highly interactive communication
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system between learners and teachers, presents aggregation functions for the end user to
assemble it own environment and e-portfolios.
This evolution brings to light some provocative ID issues. The first one is the challenge
made to instructional design as a process distinct from delivery, some proponents even
advocating the end of ID. On the contrary, others pretend that the new possibilities
offered by the Web must be planned even more carefully if we want open environments
to provide quality learning. Just like software engineering has brought quality that could
not result from hasty coding, shouldn’t instructional engineering provide support to cope
with complexity, with the larger set of decisions that face designers? But the emphasis in
ID now has to shift from simply organizing information to designing activity scenarios
and communication between learners and facilitators based on sound and well-proven
instructional strategies and methods. A second important issue is the quality of the
information available for learning, whether the learner or teacher selects it. We are in an
expanding context of billions of pages available on the Web, some providing unreliable
information. On the Web, we find the good, the bad and the ugly. One solution that has
been proposed is the use of learning object repositories composed of high-quality
educational resources, available using metadata standardized descriptions. But this
solution still has a long way to go to become mainstream. A third issue is the support of
learners in their Web-based activities. Too many times, teachers or designers will
propose Web-based activities without any support, relying on the younger generation’s
abilities to use the Internet. Young or adult learners need support to find useful and
reliable information, to learn how to communicate within the social Web, to understand
the possibilities and limit of technology and their own meta-competencies in using it.
Instructional designers must be supported in providing guidance on these questions, even
more if the learning environment that they are planning is open and learner-centric.
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From Automating to Supporting Instructional Design. Most persons designing
instruction are not trained in Instructional Design. To address this problem, a number of
researchers started building systems that could be used by inexperienced designers in
their instructional design decision-making process, prior to the production stage. The
general idea in the systems presented in section 3 was to have a designer interact with an
expert system enhanced with ID knowledge that could recommend design components to
be used for the definition or production of a learning environment. So the term
“automated design” seems a bit exaggerated. In fact, the design was the result an
interaction between the designer and the system acting as a companion or as a tool. So
the process was semi-automated. As mentioned earlier, these semi-automated systems
have been used in a number of organizations where they have increased the productivity
of designers and helped train new designers. Their main achievement was the production
of a considerable amount of ID knowledge, but they were only marginally successful,
mainly because of their complexity and their lack of flexibility and adaptivity.
These issues can be addressed by building support environments for designers in the form
of mash-ups produced using workflow or scenario editors. Such editors produce
executable sets of design tasks linked to tools and documents from various sources,
operated by the actor(s) that perform the tasks. These scenarios can be limited in
complexity, adapted to individual or team work, range from a single task to larger series
of design tasks, adapted to the needs of a designer, a design team or an organization.
From time to time, tasks can be reordered in the design scenario, support documents and
tools can be replaced, participating actors can be added, deleted or tasks can be
redistributed among actors, thus providing the needed flexibility for adaptation to a
design context.
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From Individual to Distributed and Collaborative ID. The first generation of
Instructional Design tools and methods were intended for individual teachers at the
design phase or in the production phase of a learning environment. Typically, an
individual would sit in front of a single computer and interacts with a single software,
building a design model and/or producing a CBT courseware. In more recent distance
learning systems or Learning Content Management Systems (LCMS), the focus is also on
individual designers but the design software is Web based and can integrate resources
available anywhere on the Web in addition to the tools provided by the LCMS. Still, the
most widely used design/production environments like WebCT or Moodle do not support
teamwork. They do not integrate an ID method. In fact, they provide generally a single
set of design tasks aiming at the rapid production of a Web-based environment.
Methods like MISA and the IMS-LD specification presented above integrate a multi-
actor design process, taking in account the fact that in distance education and company
training, the learning environments are usually designed and built by a team with
members playing different roles. This links well with Web 2.0 software such as
Wikipedia or GoogleDocs where documents can be built collaboratively. Flickr and
YouTube offer repositories of pictures or videos to be populated by a design team.
Facebook can provide some collaborative support to a design team. These social software
tools must of course be integrated into design scenarios implementing parts of an
Instructional Design method to produce, for example, SCORM or IMS-LD interoperable
learning environments. Bringing all these elements together can provide a stimulating
distributed and collaborative ID environment.
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From Information-based to Knowledge Model-based ID. If we go back in history,
preparing instruction has been mainly based on information processing. A scholar would
read extensively, think a lot and synthesize large amounts of information into content
documents or lectures that could be communicated to learners and novices, hopefully in a
pedagogical way. Preparing lectures has been done and is still being done by most
professors in much the same way, except that now the Internet provides a web of
information sources. But we are now in the knowledge age where the exponential growth
of available information is the rule. The use of an ever larger set of components makes
the task of designing instruction much more difficult.
There are many reasons for Instructional Design to evolve towards ontology-based
educational modeling (Paquette 2010). First, within the Semantic Web framework,
resources on the Internet can be described by the knowledge they support using domain
ontology models. Moreover, learning environments must have a structured executable
representation of the knowledge to be processed in order to help users based on their
present and expected state of knowledge and competency. A third reason is that the
learning process or scenario is also the result of a knowledge modeling activity using an
educational modeling language. Knowledge-based ID focuses on the interaction between
two models: a knowledge model of a domain (usually an ontology) that is the subject of
learning and instruction, and a process model (generally a multi-actor workflow or
scenario) of the learning and teaching activities grouping tasks, resources used and
produced by actors in the scenario. These scenario components are referenced by
knowledge and competencies described in domain ontologies. Such model-based ID is
necessary to cope with the inherent complexity of instructional design today, while
providing flexibility and adaptability.
Paquette
Conclusion
We have underlined some of the difficulties of instructional engineering, taking into
account the great number of factors the designer must consider, and the constraints he
must work with. Beyond the possible improvements mentioned above, it is important to
develop various means of adaptive assistance for instructional engineering and to
integrate them to computerized tools that support designers. This assistance cannot rest
only on templates and model libraries. The implementation context must also be taken
into account.
It is not easy to implement any method in an organization. It suffices to consider the time
it took to convince programmers and their customers to adopt software engineering
methods. The increasingly complex and vital character of information processing
systems, however, provides strong arguments in favor of the adoption of such methods,
making gradually anachronistic the spontaneous programming approach that marked the
first decades of software production. In the field of instructional engineering, we haven’t
reached this point yet, although we can already see that during the next years, the same
type of evolution will be increasingly necessary due to the demands of the knowledge
economy. Still, ICT-based instructional engineering has a promising future for practical
use in organizations. It remains also a challenging and rewarding research field.
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Paquette
Author Information
Gilbert Paquette,
CICE Research Chair, LICEF Research Center
Télé-université du Québec
100 Sherbrooke St., West
Montreal, Quebec, Canada
Email : gilbert.paquette@licef .ca
Web sites : www.licef.ca/gp and www.licef.ca/cice
Phone : 1-514-840-2747 ext. 2818
Gilbert Paquette holds a Ph.D from the Université du Maine (France) in Artificial
Intelligence and Education. Researcher at the LICEF research center he has founded in
1992, he holds a Canada Research Chair in Instructional and Cognitive Engineering
(CICE), has acted as the Scientific Director of the LORNET Canadian research network
(2004-2009) and is a full professor at Télé-université du Québec in Montreal since 1986.
In 2007, he received an Honoris Causa Doctorate from the University Pierre et Marie
Curie (Paris VI) for pioneering strategic projects in the field of knowledge-based
systems, instructional Design and distance education, and also for his political
involvement as Minister for Science and Technology in the Quebec Government. Recent
publications include four books on Instructional Design and Knowledge Modeling. He
has given invited conferences in many parts of the world and sits on the advisory
committee for six Journals, three in France, one in the US and two in Canada. He
represents Canada on the Globe consortium for learning objects repositories. He has also
participated in advisory committees for two European networks: TENCompetence and
Share-TEC.
