Preparing Instructional Designers: Traditional and Emerging Perspectives

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1 Preparing Instructional Designers: Traditional and Emerging Perspectives 52 Monica W. Tracey and Elizabeth Boling Abstract While of fi cial de fi nitions and textbooks in the fi eld re fl ect a conception of design in which little has changed in decades, there has been a growing awareness since the early 1990s that broader conceptions of design could bene fi t practice in instructional design. Preparations of instructional designers in college programs traditionally include the use of instructional design models and processes incorporating project work. Approaches based on studio design are recently emerging in some programs. Research on design practice and the effectiveness of design pedagogies in the fi eld are called for. Keywords Design Design knowledge Precedent Studio pedagogy To address the question of preparing instructional designers, we fi rst consider what it is they are being prepared to do. In a detailed analysis of the most widely adopted textbooks and of fi cial de fi nitions of the fi eld, Smith and Boling ( 2009 ) establish that conceptions of designing in the fi eld have changed little over the 40 years covered by those materials. Summarized, these conceptions of designing in the fi eld comprise the view that design is a systematic process, represented by models, based on theory and grounded in data while focused on problem solving. As these conceptions are clearly outlined in the most widely adopted textbooks in the fi eld we can presume that these perceptions guide the preparation of instructional designers. The International Board for Training, Performance, and Instruction (IBSTPI) competencies (Richey, Fields, & Foxon, 2001 ) are currently the most widely accepted M.W. Tracey, Ph.D. (*) College of Education, Wayne State University, 383 Education Building, Detroit MI 48202, USA E. Boling Indiana University, 201 N. Rose Avenue, Bloomington, Indiana 47405, USA standards for instructional design practice, although several other organizations including the International Society for Technology in Education (ISTE), the IEEE Technical Committee on Learning and Technology s competency-based perspective on curricula and assessments for Advanced Learning Technology (Hartley, Kinshuk, Koper, Okamoto, & Spector, 2010 ), and the United Nations Educational, Scienti fi c, and Cultural Organizations (UNESCO) have also recommended competencies related to technology and instruction/education (Sims & Koszalka, 2008 ). The intent of the competencies is to provide a guide for professional practice and preparation for that practice. Traditional Methods of Preparing Instructional Designers Major textbooks in the fi eld are organized around process models (Smith, 2008 ), and current preparation in instructional design programs most often begins with an introduction to the instructional design process via one or more models, even when instructors report that they are not sure why they do so (Boling, Easterling, Hardre, Howard, & Roman, 2011 ). As recently as 2009 a new textbook appeared (Branch, 2009 ) centering squarely and solely on ADDIE as J.M. Spector et al. (eds.), Handbook of Research on Educational Communications and Technology, DOI / _52, Springer Science+Business Media New York

2 654 M.W. Tracey and E. Boling the conceptual framework and process model for designing, and presenting the traditional view of designing as the appropriate basis for teaching novices how to design. Novice designers are also taught the foundations of the fi eld, including descriptive and prescriptive theories from multiple domains, as well as methods for analysis, preparing objectives, and other activities within the larger process frame ( Richey, Klein, & Tracey, 2011 ). Although acknowledging that instructional designers adapt their practice to cultural norms in their own countries, Campbell, Kanuka, and Schwier point out their realization during a recent symposium organized speci fi cally to explore such practices that most international instructional designers with graduate preparation have been enculturated with a North American view of ISD ( 2010 ; p. 15) owing to their having studied in U.S. programs or in programs modeled on these. Providing IDT students an opportunity to practice ID in the context of an authentic project has become conventional in many IDT programs (Cennamo & Holmes, 2001 ; Knowles & Suh, 2005 ; Quinn, 1994 ; Tracey, Chatervert, Lake, & Wilson, 2008 ) while still emphasizing traditional process models and the use of generalized principles applied to designing as exempli fi ed by Cennamo and Holmes ( 2001 ). Using an apprenticeship model, Collins, Brown, and Holum ( 1991 ) documented the design and application of a clinical course, which immersed their instructional design students in practice. Results indicated that overall, students needed guidance in extrapolating general design principles from a speci fi c design experience. The authors suggest that the extensive use of design cases, discussions with expert designers, and experiential learning opportunities within graduate programs are important in the effort to prepare students for instructional design practice after graduation. Match Between Formal Preparation and Practice Using established competencies, theories, principles, and process models as a guide to preparing students for professional practice may be fundamentally sound, but research indicates that additional knowledge and skills are needed in practice. In 2000, instructional design students participating in a study on the use of case studies indicated that the instructional design knowledge and skills they needed for actual practice was well beyond what they learned in the classroom (Julian, Kinzie, & Larsen, 2000 ). In 2010 Villachica, Marker, and Taylor analyzed 85 surveys from employers in the fi eld and discovered that the skills they expect new employees to bring to the job are required for 22 activities that fi t into the broad conceptual categories associated with ADDIE, but that graduates trained in this fi eld were unable to perform those activities as expected, or were only able to do so with a lot of assistance (p. 33). While Larson (2005 ), in researching whether alignment exists in instructional design preparation and practice with respect to general instructional design competencies, found that graduates felt well prepared overall by their programs to apply those competencies in practice, a review of actual ID practice reported in classic research studies (Holcomb, Wedman, & Tessmer, 1996 ; Kirschner, Carr, van Merrienboer, & Sloep, 2002 ; Rowland, 1992 ; Tessmer & Wedman, 1992 ; Visscher-Voerman & Gustafson, 2004 ; Wedman & Tessmer, 1993 ; Winer & Vazquez-Abad, 1995 ) identified substantial variation in actual practice using the competencies (Leigh & Tracey, 2010 ). Some of the essential IBSTPI competencies are not frequently used in actual practice by instructional designers because of (a) lack of time and resources, (b) control in decision making, (c) the designer s perception of a task, (d) underlying philosophical beliefs, and (e) designer expertise (Leigh & Tracey, 2010 ). Research focused on identifying what instructional designers actually do indicate a difference in the use of tools used to prepare designers and how designers actually approach design (Kirschner et al., 2002 ; Leigh & Tracey, 2010 ; Wedman & Tessmer, 1993 ; Winer & Vazquez-Abad, 1995 ). Practitioners in the fi eld do not always use the tools that were used to teach them and this seems to have been true for some time (Rowland, 1992 ). It is not necessarily the case that designers in practice are prevented from carrying out their work as they were taught to do it in school, although some do report this view (Boling et al., 2011 ). Kirschner et al. ( 2002 ) speculate that, while ID models often inspire designers, their activities typically don t re fl ect the systematic, step-by-step approach as prescribed in traditional ID models (p. 91), and Visscher-Voerman and Gustafson observe that, although often prepared to approach design in a systematic manner, most designers in practice approach design in an individual, iterative, and context-driven manner (2004 ). Despite this, Smith and Boling ( 2009 ) note that descriptions of how designers impact design are absent in the textbooks and de fi nitions published by the fi eld. Preparation for Design Practice When looking at student preparation in other design professions, including graphic design, engineering, and architecture, it is evident that these designer preparation programs do not solely focus on process. Instructors teaching design in these fi elds discuss teaching technical methods, but focus on teaching designers to be ethical, to de fi ne their own talents, to understand the world, have passion for design, acquire their own voices [the concern is with] transforming students into designers, rather than

3 52 Preparing Instructional Designers teaching students the process of design (Bichelmeyer, Boling, & Gibbons, 2006 ; p. 42). In part this difference in focus stems from the problems and limitations of teaching based on models and prescriptive guidelines for process. Siegel and Stolterman ( 2008 ), working in human computer interface design, address one of the dif fi culties novices encounter as they study design in a way that may be familiar to instructional design educators, saying that Naive designers expect to memorize algorithmic solutions to problems; experienced designers learn to deal with ill-structured problems, seemingly paradoxical situations and design thinking (p. 378). Groeneboom, renowned product designer (quoted in Lawson & Dorst, 2009 ), expresses the view that offering algorithmic solutions, represented in his fi eld by design methods, is counterproductive. The big disadvantage [of design methods] is that through this kind of teaching we take away the insecurity of the students. It is a way of quickly and ef fi ciently explaining design but that is deadly. Students have to learn to deal with uncertainty, and we take that away by this kind of teaching In the end, I would say that dealing with uncertainties is the core of our design profession (p. 33). Part of the problem may be that the novice designer takes models literally, [while] experts adapt (Gibbons & Yanchar, 2010, p. 20). Another may be that designing is such a complex activity that no one model can capture or explain it suf fi ciently well to engender rich practice (Lawson & Dorst, 2009 ). In fact, Cennamo et al. (2011 ) state that the education of engineers, instructional designers, architects, landscape designers, and the like must, by necessity, prepare students to solve the very complex and ill-structured design problems with which they must grapple as professionals (p. 13) and describe the methods used to do this in an industrial design class, an architecture class, and three human computer-interaction classes. Studies on examining design expertise indicate general agreement that expertise is a signi fi cant factor in designer s problem solving (Le Maistre, 1998 ), in their strategy selection (Christensen & Osguthorpe, 2004 ), and in managing the process of the design situation (Tracey & Morrison, 2012 ). In a classic study of how designers actually think when they design, Kerr ( 1983 ) explored how 26 novice instructional designers cope with making design decisions. He determined that beginners have dif fi culty articulating a design problem to themselves and to others, have problems entertaining multiple design solutions and eliminating alternatives rapidly, and do not know when and how to determine when to stop the design process. Experienced designers on the other hand draw on their knowledge of previous designs and seem to have learned the value of rapid problem-exploration through solution-conjecture. They use early solution attempts as experiments to help identify relevant information about the problem (Cross, 2007, p. 46). Where novices work diligently attempting to understand the problem before considering 655 solutions, experts use solution ideas to help clarify the problem (Lawson, 2004a, 2004b ). Even instructors who do not begin teaching from a process model may subscribe to the idea that novices can perform like experts if they are told how to do so. Verstegen, Barnard, and Pilot ( 2008 ) studying the use of methods to support novice instructional designers working on a complex design problem, provided 11 support interventions to novices during design. Support included solving the design problem with speci fi c instructional design guidelines, managing the design process with speci fi c process-oriented guidelines, gathering information using speci fi c templates, and communication throughout the design process with speci fi c guidelines for exchanging information. Results indicated that novice designers could solve complex design problems when given enough time and adequate support for the given task. However, in spite of the extensive support, variation in the quality of the designed product was evident. This may be partially because students entering design fi elds are saddled with naive or misconceptions about design and design activity (Newstetter & McCracken, 2001a ; p. 63). These scholars in design education at the Georgia Institute of Technology explain that student conceptions, or mental models, concerning design are strong and that having students follow prescriptive models of design does not constitute confrontation of the sort that can begin the dismantling of [these] mental models (p. 66). They call for a science of design learning, involving extensive and rigorous study of novice designers and their learning. Evolving Views of Designers and Designing Scholars and instructors focused on design discuss specialized activities and particular habits of thought termed design thinking (Cross, 2007 ; Lawson & Dorst, 2009 ) and reflective designing (Lowgren & Stolterman, 2004 ). In this view, no single approach to designing can address every future situation effectively, so the designer must be prepared to appreciate design situations subtly and with discipline, invent and reinvent processes, and take personal responsibility for the effects of their designs rather than handing off responsibility for quality outcomes to a single process or theory (Nelson & Stolterman, 2003 ). Designers act as human instruments, analogous to researchers in a naturalistic study, bringing their own acknowledged perspectives to the enterprise, working within emergent frameworks and adapting to situations unknown and unknowable in advance (Boling, 2008 ). Students of instructional design and technology (IDT) bring different backgrounds and abilities to the classroom along with very different understandings of what design is and their role in it. Historically, IDT has focused on the systematic design process, client, and content, with very little on the

4 656 M.W. Tracey and E. Boling designer role in design situations. However, those who view design as a tradition distinct from science and who study how it occurs in practice, present design not as a smooth systematic process, but instead that designer s values, belief structures, prior experiences, knowledge and skills, and their approach to design affect the fi nal outcome (Nelson & Stolterman, 2003 ). Observations of designers carried out by researchers reveal that they reason from previously encountered solutions rather than from theories or fi rst principles, recognizing the affordances of a solution and using it as a gambit holding it up to the problem and interrogating the result to re fi ne their view of the problem then selecting another solution as a gambit, and so on in a tight circle of progress toward the fi nal design (Lawson, 2004a ). The knowledge required for this activity is precedent, episodic memory of existing designs and other in fl uences, categorized by the designer more and more effectively as her experience grows (Oxman, 1994 ). Related to this use of specialized knowledge, researchers also observe designers selecting a primary generator, a preliminary and strategic idea of how a solution in a given design situation might play out and using this as both a fulcrum and a fi lter to manipulate other elements of the design problem, revealing and reshaping it in order to handle its complexity (Darke, 1978 ). Other scholars address the nature of design situations rather than the nature and behaviors of designers. Goel ( 1995 ) discusses design problems as distinct from those that may be rationalized and addressed using regular symbol systems. He characterizes the complexity of design problems in detail, including the critical feature that design problems do not contain the data that will dictate their solutions and they do not include stopping rules by which the designer may be assured that a complete solution has been found. Lawson & Dorst ( 2009 ) presents a three-dimensional model of the constraints on designs, a view not intended to represent all facets of designing, but one which casts the designer not as a traveler along a winding process path, but as an actor in a space shaped both externally by constraints and internally by the designer himself (p. 131). In this view, designers have to appreciate and impose constraints, and they have to manipulate the conceptual space in which they are working in response to those constraints. Tatar (2007 ) describes design tensions as a paradigm that conceptualizes design not as problem solving but as goal balancing. They [design tensions] draw explicit attention to con fl icts in system design that cannot be solved but only handled via compromise (p. 415). These intertwined design tensions assist in organizing rationales about goals and about consequential design choices. Tatar explains that design tensions do not set boundaries or simplify the problem, but provide a framework for creating a space of relevance. Rather than a focus on simplifying a problem, design tensions provide a framework in which the designer can manage complexity and trade-offs. Designers may also be seen as change agents, rather than simply as creators of artifacts and experiences. The instructional designer s role is to alter knowledge, skill, and/or the performance of the learner (Spector, 2008 ), which means that they work as active change agents in numerous social and cultural contexts and should be prepared to work in various organizational cultures. Larson and Lockee ( 2009 ) in their 2004 Instructional Design Career Environments Survey, solicited feedback on preparation and practice of instructional designers, discovering a gap between the culture and value systems of business and industry environments where instructional designers practice and the educational environments in which they are prepared. Design practitioners identi fi ed six cultural workplace issues that were challenging in practice including workplace politics, trade-offs between quality, timeliness, and cost, project resources, freedom given to make decisions, employer attitudes toward change, innovation and risk, and workload (p. 18). Strategies identi fi ed by faculty in the study to assist in preparing designers to work in various organizational cultures include collaborative teamwork activities to develop interpersonal communication skills (Julian et al., 2000 ), authentic ID projects involving client relations to promote designer re fl ection (Knowles & Suh, 2005 ; Tracey et al., 2008 ), design cases (Boling, 2010 ), and case studies (Ertmer & Quinn, 2003 ) providing designers an opportunity to interact with design problems; and internship/apprenticeship opportunities (Rowland, Parra, & Basnet, 1994 ). Schwier, Campbell, and Kenny ( 2004 ) address social constructivism, placing the individual designer in learning communities of practice supporting a shared identity, interdependence, and shared culture. Christensen and Osguthorpe ( 2004 ), when studying how ID practitioners make ID decisions, discovered designers most often select instructional strategies by brainstorming with others involved in a project. Designers also reported that they learn about new theories, trends, and strategies more often from interactions with their peers and coworkers than by other means. These views point to forms of knowledge shared across designers, not always in tangible ways and not always available for explicit transmission to novice designers. Boot, Botturi, Gibbons, and Stubbs ( 2008 ) address yet another facet of designing; the development by communities of designers of languages speci fi c to what they do. Such languages are seen to bring community members together in a shared understanding different than that available through more general discourse, but also to offer a means of considering designs that would not be possible without these languages.

5 52 Preparing Instructional Designers Studio-Based Education Studio-based education is the norm across multiple domains of design education. Lawson and Dorst ( 2009 ) trace the development of university-based design education as it is carried out around the world in multiple disciplines from the days when designers learned their craft as apprentices in the fi eld through the period of highly conventional pattern of progressive exercises employed at the cole des Beaux-Arts and on to the working and learning communities of the Bauhaus and HfG Ulm schools connecting art and industry through design and setting the primary features of design schools as we know them today: studio, design tutorial, critique, and libraries of materials (pp ). Schon ( 1985 ) studied this pedagogical pattern rigorously; laying out its features, mechanisms, and bene fi ts, and 25 years later Shulman ( 2005 ) has sparked discussion in multiple content areas by arguing that studio-based pedagogy offers distinct advantages for teaching complex performances in the professions. Over a decade ago, Tessmer and Wedman ( 1995 ) and Quinn ( 1995 ) discussed the potential for studio-based practices in instructional design to provide preparation for instructional designers aligned with evolving conceptions of designing. More recently, Brandt et al. ( 2010 ) are studying studio-based design across multiple domains of design education, identifying its key properties and the ways in which they support development of design capabilities in students. Design education incorporating features of studio-based education has been, and continues to be, practiced in the fi eld of instructional design and technology. Clinton and Rieber ( 2010 ) document a 10-year implementation of a studio curriculum at a southeastern U.S. university, focusing on the application of theory to practice in preparing students in instructional design. Three successive masters courses meet together in one common learning space with a team of instructors and a mix of more and less experienced students working side by side. As part of a 6-year study, Boling and Smith ( 2010 ) report on the design activities of students enrolled in a studio-based course within an instructional design program at a major U.S. institution. Over the course of the study, structured time in the course (lectures, planned demonstrations, organized discussion) has been reduced to zero, while traditional studio activities (desk critique, group critique, independent project work) have replaced it. Clinton & Hokanson ( 2011 ) organized a panel session during the annual meeting of the Association for Educational Communications and Technology at which representatives of fi ve major programs around the country (including the two previously mentioned here) described studio-based experiences in place or soon to be in place to prepare instructional designers for practice. 657 The Open University offers a design education program at a distance in an effort to offer a universal education model appropriate to our emerging post-industrial society and technology (Cross, 2007, p. 45). The program operates on the premise that design education must be accessible to everyone, must be a life-long process in order to keep practitioners up to date and well educated on changing skills and knowledge, and fi nally, that it is explicit meaning that design education involves well-articulated and understood principles. The philosophy of the faculty in the program is that design often occurs in an unsystematic way. In an effort to prepare designers for practice, the focus is not on a systematic process, rather the clari fi cation and instruction of elements of design ability. Lawson and Dorst ( 2009 ) do raise questions regarding the assumption that traditional design pedagogy is the best or only way to teach design. Others also entertain questions about the limitations of studio pedagogy (Habraken, 2007 ), about speci fi c practices in the studio ( Anthony, 1991 ), and about power relationships enacted in the studio (Dutton, 1987 ). Clinton and Rieber (2010 ) also report that the Studio experience is not the ideal method to prepare every student. As the faculty state, The studio curriculum is best suited to those who bring some background knowledge of multimedia development into the program with them (p. 775). For novice students with no prior background, this preparation approach may be most bene fi cial after successful completion of other design experiences providing students with the background necessary for the studio. In an earlier report on their studio experience, Boling and Smith ( 2009 ) discuss the design tensions accompanying the incorporation of studio experiences into a traditional curriculum, including the problems of securing appropriate space, shifting perspectives as an instructor, and managing student expectations in a setting unusual for them. Emerging Concepts in Design Education Recently, whole-task models, instructional models that apply a holistic approach in which complex contents and tasks are analyzed and taught from their simplest yet, still meaningful, version toward increasingly more complex versions (Van Merrienboer & Kester, 2008, p. 442) have been introduced in response to criticisms of teaching learners complex concepts too simply. This holistic approach to present instruction to learners may also be a design approach for instructional designers. According to constructivist theories, it is important to apply a holistic or systemic approach that considers all of the instructional factors in increasing detail throughout the design process (Kirschner et al., 2002 ). Applying holistic design may include a systemic approach, or one where you begin with the fi nished product in mind

6 658 M.W. Tracey and E. Boling and work backwards, then continue to circle throughout the design and repeatedly revise the instruction (Gibbons & Yanchar, 2010 ). Efforts continue to improve traditional instructional design education and to explore the question of expertise development in our local domain. Ertmer, York, and Gedik (2009 ) are working to incorporate the experiential knowledge of expert designers into the curriculum, correlating this knowledge with existing standards. Hardre, Ge, and Thomas (2006 ) examine the multiple dimensions of students studying instructional design for their effects on the development of expertise, suggesting that design education be responsive to these differences. Newstetter and McCracken ( 2001b ) discuss, not a speci fi c approach to design education, but the need to develop a science of design learning through posing and answering dif fi cult research questions regarding how design happens that have dogged design researchers over the last thirty years (p. 3). This effort from within our fi eld echoes the earlier effort by Cross, Christaans, and Dorst ( 1997 ) to investigate valid methods for exploring design cognition and activity by setting up rigorously prepared design sessions and capturing a rich data set from them, then giving these to multiple researchers who then presented and published their separate analyses. The emerging approach in both of these cases is to focus on studies of designing before creating or recommending tools for designing or methods for teaching design. Future Research Two gaps are apparent in our knowledge regarding the preparation of instructional designers; one involves the basis for that preparation understanding how designers actual work and the other involves development and validation of methods for teaching the complex performance that is design. Broad and intensive study of competent design practice in the fi eld is needed. This investigation must be carried out in a spirit of curiosity and exploration, asking what designers in the fi eld do, rather than measuring them against what academics think they should be doing. We need, as part of this effort, to identify and describe the conceptual tools actually used by practicing designers. We need descriptions and models for aspects of designing in our fi eld that move beyond process to describe designers and design teams, the individual activities and tools of design, and the mechanisms of invention. These should be viewed as tools of scholarship and tools for expert designers, and not simpli fi ed into starting points for novices who are forming their design character. We also need to conduct empirical studies of the effect that methods used in design education have on the activities and thinking of novice designers during their studies, as well as on those same designers as they practice in the fi eld. We need a detailed examination of the progression from novice to competent and expert practice by instructional designers, bearing in mind that this progression will not be monolithic but will be affected by those who study with us and the varied experiences they bring with them. Conclusions In 1999, Nigel Cross concluded his contribution to a special issue of Design Issues devoted to research in design observing that We still know relatively little about the mystery of design ability this is the goal for design research (p. 10). Since then, Lawson and Dorst ( 2009 ) have built on multiple studies, most carried out in architecture and product design and examining the actual nature of designing, to present several models of designing intended to be used in tandem to consider the education that will build design expertise. In this fi eld research is needed speci fi cally to expand and enrich our understanding of designing as designers carry it out, to provide the foundation on which we prepare our designers for practice. Such research will describe what designers are doing as they work, rather than producing more guidelines for how they should work or what decisions they should make. As Holt (1997, p. 120) noted in discussing engineering design education, when an exclusively scienti fi c world view or attempts to fi t design education into a scienti fi c mould dominate, the exercise of judgment is reduced to choosing the right formula. He points out that every advance, or change of direction, in the design process is the result of the designer s judgment. But the notion of judgment is somewhat elusive (p. 113). This remains the case today, and part of the research agenda related to design education in the fi eld must focus in this direction. These foci in research are prerequisites to effective design education. Systematic study is also required to determine, in the context of instructional design education, how novice designers conceive of the activity of design and of problems in design and their solutions. Study is required to understand when and how students of instructional design develop these views, what obstacles exist for them in the development of professional judgment and character, and how they surmount those obstacles in the course of their education. Studio education, as applied to the preparation of instructional designers, needs to be studied as it is practiced to determine the effective adaptations that can be made to the basic pedagogy and the limitations it presents for this fi eld. Studies will need to be carried out among students of instructional design who are simultaneously employed in the fi eld to understand their dual development as professionals and student-professionals.

7 52 Preparing Instructional Designers Although such an agenda may be lagging 20 years behind the research in traditional fi elds of design, if we begin to focus on these efforts, we may be able to use emerging perspectives on preparing instructional designers to improve practice in the field. References Anthony, K. (1991). Design juries on trial: The renaissance of the design studio. New York, NY: Van Nostrand Reinhold. Bichelmeyer, B., Boling, E., & Gibbons, A. S. (2006). Instructional design and technology models: Their impact on research and teaching in instructional design and technology. In M. Orey, V. J. McClendon, & R. M. Branch (Eds.), Educational media and technology yearbook (pp ). Westport, CT: Libraries Unlimited. Boling, E. (2008, October). Design is not systematic : Alternative perspectives on design designer as human instrument. Panel session presented at the Meeting of the Association for Educational Communications and Technology. Orlando, FL. Boling, E. (2010). 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9 Technology-Based Instructional Design: Evolution and Major Trends 53 Gilbert Paquette Abstract This chapter surveys ICT-based tools and methods that support instructional designers in planning the delivery of learning systems. This fi eld has evolved since the 1970 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 insuf fi cient educational support. New challenges make this fi eld 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 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 1960, that we see the beginning of the new discipline, mainly under the in fl uence of the work of B. F. Skinner on programmed instruction, Jerome Bruner on the cognitivist approach and David Ausubel (Reigeluth, 1983 ). In the 1970s and 1980s, research on instructional theories blossomed as illustrated by t he: (a) the G. Paquette (*) CICE Research Chair, LICEF Research Center, Télé-université du Québec, 100 Sherbrooke St, West Montreal, QC, Canada development of a cybernetic approach (Landa, 1976 ), (b) the exposure of learning conditions (Gagné, 1985 ), (c) the identi fi cation of instructional strategies based on structural learning theories (Scandura, 1973 ), (d) the development of a cognitive teaching theory based on enquiries (Collins & Stevens, 1983 ), and (e) 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 educational science is analogous to the link between engineering methods and the physical sciences, or between medicine and life sciences. J.M. Spector et al. (eds.), Handbook of Research on Educational Communications and Technology, DOI / _53, Springer Science+Business Media New York

10 662 G. Paquette Fig The basic life cycle of a learning environment The life cycle of a learning environment is presented in Fig This fi gure 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 de fi ciencies 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 53.1 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 subprocesses more or less parallel, sharing information between them with frequent interaction between the actors. In this chapter, we will focus on the instructional design (ID) 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. Authoring Tools and Languages The use of computers in education started 50 years ago, at the beginning of the 1960s. The fi rst applications were in fl uenced mainly by programmed instruction strategies (Crowder, 1959 ; Skinner, 1954 ). 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 had powerful answer parsing and answer judging commands, and it had features to simplify student records by instructors. TUTOR s fl exibility, 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.

11 53 ICT-Based Instructional Design Later, templates were developed to ease the programming part of courseware creation. For example, ( Schultz, 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, Schneider, & Fletecher, 1980 ) attempted to provide built-in complex instructional templates in the mid 1970s. 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 an 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 systems (LCMSs), 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, Lo, Oyuki, & Wing Li, 2003 ). 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. 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 prede fi ned, 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 663 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, Aubin, & Crevier, 1994 ; Paquette, 2004 ) is a longterm 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 in Fig. 53.2, 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 construction of the model or document its properties. 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 identi fi ed. 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-de fi ned 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 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 fi rst one was AGD, a standalone performance support system for ID (Paquette et al., 1994 ). Later, 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 is presented in the next section. More recently, MISA/ADISA design scenarios can be edited and processed by the ontology-driven TELOS system (Paquette & Magnan, 2008 ). Expert Systems and Automated/Guided ID Beginning also in the 1990s, expert systems and arti fi cial intelligence techniques started to be applied to the fi eld of instructional design 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 & Park, 1992 ). A second category of systems is concerned with helping designers construct Intelligent Tutoring Systems (Wenger, 1987 ) ; the Generic

12 664 G. Paquette Fig Overview of the MISA instructional system design method 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, Polson, & Muraida, 1993 ) provides a guided approach to ID Advising 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 speci fi cation that provides a skeleton from which instructional materials can be built. The domain of the fi rst ID Expert was limited to goals involving concept classi fi cation 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 con fi rm 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 example, shown from the learner s point of view (see Fig ). The user can shift from this learner view to a designer view that provides an elaboration of why speci fi c learner activities were designed as they were. ADISA is the successor of the AGD system. It is a Webbased system developed to enhance the performance level of instructional designers, in particular to assist teams who

13 53 ICT-Based Instructional Design 665 Fig A screen from GAIDA/GUIDE 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 semiautomated ID systems? First, productivity improvements have been observed due to performance support 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. 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 and 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 Since then a host of other speci fi cations have been published by IMS Global1. ISO has started publishing at the end of 2010 the fi rst documents of its new Metadata for Learning Resource (ISO-MLR, 2012 ) standard, based on the W3C ( 2004 ) Resource Description Framework (RDF). The work on Educational Modeling Languages (Koper, 2001 ), and the subsequent publication of the IMS Learning Design Speci fi cation (Grif fi ths, Blat, Garcia, Votgen, & Kwong, 2005 ; IMS-LD, 2003 ; Koper & Tattersall, 2004 ), is

14 666 G. Paquette the most important initiative to date that integrates instructional design modeling into the international standards movement. This speci fi cation 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 speci fi cations such as MD (metadata), SS (simple sequencing), CP (content packaging), RDCEO (learning objectives and prerequisites), QTI (questionnaires and tests), LIP (learner information pro fi le) 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 singleuser simpler hierarchical activity structures. IMS-LD expands SCORM speci fi cations in many ways: IMS-LD describes methods as multiactor work fl ow processes 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 modeling and cross-users noti fi cations Most important, IMS-LD favors instructional strategies like collaborative learning, problem solving, projectbased learning, communities of practices, and multifacilitators 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 fi les. 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, 2010a, 2010b ). Besides their strong in fl uence 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 has move the focus towards aggregating resources and interactions, instead of producing more text, multimedia, or Webbased document. In this new approach to ID, the learners and the facilitators are resources themselves, interacting within activities using and producing learning resources, a more cognitive and constructivist process than simple information transmission. 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 fi rst Internet browser did 15 years ago with information access. Semantic Web technologies have the same potential to dramatically improve Web 2.0 activities that are often limited to super fi cial 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 multiactor work fl ows, 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 work fl ow is built while collaborating, in an emergent way such as in project-based learning where the learners become their own designer. 2. Taking into 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

15 53 ICT-Based Instructional Design 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. 5. Aggregating Social-Semantic tools into Learning Environments. Data Mashups have been identi fi ed by the Horizon study (2008) as one of the leading trends for Using social environments like Facebook or Wikipedia, users become Web designers, assembling text, pictures, and sound according to their needs. The issue of learning quality then comes to the forefront, while the impact of these new technologies 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 multiactor 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 on semantic Web techniques, an area part of the Adaptive Semantic Web (Dolog, Henze, Nejdl, & Sintek, 2003 ) that we call Ontology-Based Assistance Systems. Recent research on assistance systems at LICEF ( Paquette & Marino, 2011 ) proposes that advisor agents be grafted on environments/scenarios, built in the context of the TELOS system (Paquette & Magnan, 2008 ; Paquette, Rosca, Mihaila, & Masmoudi, 2006 ). TELOS is a 667 service-oriented, ontology-driven system that helps build online 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 multiactor processes or work fl ows integrating a variety of control patterns between tasks 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 fi nal users who interact in these scenarios. Figure 53.4 presents the upper graph of a design process (build by an educational technologist) to help designers produce IMS-LD compliant designs: in the fi rst 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 identi fi ed and de fi ned; in the third one, a scenario model is built of each act as well as a knowledge/competency model and the association between the two structures. This third activity has a complex submodel not shown on the fi gure where knowledge and competencies are associated with actors, activities and resources. 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. 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 fi eld. From Tutoring to Open Learning Design As shown in section Introduction: De fi ning the fi eld, 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

16 668 G. Paquette Fig A multiactor design scenario spreadsheets. Nowadays, even though there are many programmed instruction courses that are useful in some 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 fi nd 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 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 fi rst 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, should not 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 fi nd 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 fi nd 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. From Automating to Supporting Instructional Design Most persons designing instruction are not trained in instructional design. To address this problem, a number of

17 53 ICT-Based Instructional Design 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 Modeling Instructional Design and Job Aids was to have a designer interact with an expert system enhanced with ID knowledge that could recommend design components to be used for the de fi nition 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 semiautomated. As mentioned earlier, these semiautomated 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 fl exibility and adaptivity. These issues can be addressed by building support environments for designers in the form of mash-ups produced using work fl ow 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 fl exibility for adaptation to a design context. From Individual to Distributed and Collaborative ID The fi rst 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 and LCMSs, the focus is also on individual designers; however, the design software is Webbased 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 very well. 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. 669 Methods like MISA and the IMS-LD speci fi cation presented above integrate a multiactor 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. 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 dif fi cult. There are many reasons for instructional design to evolve towards ontology-based educational modeling (Paquette, 2010a, 2010b ). 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. Knowledgebased 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 multiactor work fl ow 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 fl exibility and adaptability.

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