Emerging Technologies, ISD, and Learning Environments: Critical Perspectives

Emerging Technologies, ISD, and Learning Environments: Critical PerspectivesAuthor(s): Michael J. HannafinSource: Educational Technology Research and Development, Vol. 40, No. 1 (1992), pp. 49-63Published by: SpringerStable URL: http://www.jstor.org/stable/30219997 .

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Emerging Technologies, ISD,

and Learning Environments:

Critical Perspectives

Michael J. Hannafin

Michael J. Hannafin is with the Center for Instructional Development and Services, Florida State University.

During the past three decades, interest in computer-based instruction (CBI) has grown dramatically. Enhanced power, increased availability of peripheral devices, and developments in hypermedia have created extraor- dinary capabilities. At the same time, there have been significant, though largely unex- ploited, advances in research, theory, and practice. Collectively, these advances offer the potential to redefine learner-computer interaction. A rationale for, and description of, computer-mediated learning environments-- multifaceted, integrated systems that promote learning through student-centered activities--are presented in this article.

D During the past 20 to 30 years, the field of instructional systems design (ISD) has prospered in many ways. The number of gradu- ate training programs has grown dramatically. Program graduates have readily obtained em-

ployment in the fast-growing training field, with projections of continued growth into the

twenty-first century. Indeed, with its outcome- driven, performance orientation, the ISD field has been a unique success story.

Despite well-documented growth, the field has failed to evolve in many ways. Develop- ments in cognitive psychology, for example, have implications beyond our predominating externally centered designs (West, Farmer, & Wolff, 1991). Advances in computers and related hardware technologies have far out-

stripped prevailing design methodologies. The field remains insulated from developments of considerable consequence for im-

proving learning, and isolated collectively from intellectual communities where significant work in next-generation learning systems has occurred.

The purposes of this article are to examine the role of ISD in rapidly changing delivery systems and to explore the relevance of de-

velopments in learning environments and

emerging technologies for the ISD field.

ETR&D, Vol. 40, No. 1, pp. 49-63 ISSN 1042-1629 49



50 E-&D, Vo. 40. No.1 No.1

ISD EVOLUTION AND THE TECHNOLOGY' REVOLUTION

Despite the proliferation of models and perspectives in systems approaches (see, for ex-

ample, reviews by Andrews & Goodson, 1980; Gustafson, 1991; and Schiffman, 1991), few substantive changes have been observed in

ISD processes and procedures during the past three decades. The differences among models often are related more to level of detail, terminology, and emphasis than to dearly dif- ferentiated foundations, assumptions, and

learning paradigms. The basic systems approaches to instructional design and de-

velopment have been applied similarly across traditional instructional media.

ISD methods and models also have been applied successfully to computer-based instruction (CBI). Recently, however, new generations of hybrid computer-based instructional systems, called emerging technologies, have ex-

panded the designer's tool kit dramatically (Hannafin & Rieber, 1989a; 1989b). The phrase "emerging technologies" emphasizes creating or extending functions and attributes across

developing technologies, as opposed to attrib-

uting differences to specific media such as interactive video, computer-based instruction, compact disk-interactive (CD-I), electronic databases (including textual, visual, and aural), and alternative input and output devices. In effect, emerging technologies represent, to

varying degrees, the technological capacity to

present, manipulate, control, or otherwise

manage educational activities. While most educators concede that emerg-

ing technologies can revolutionize our historic notions of teaching and learning, some are convinced that the application of ISD methods alone will not support such a transforma- tion (Carroll, 1990). Our methods and models are primarily externally directed and content driven (Johnsen & Taylor, 1991). They emphasize the attainment of highly prescribed, objective outcomes and the organization of to-be-learned lesson content, not the largely unique and individual organization of knowledge. Alternative perspectives may be needed in order to optimize the value of emerging technologies.

Thus far, the ISD field has not significantly influenced the quest for alternatives; indeed, in many cases, we have deterred such efforts. We have re-hosted traditional ISD via computer technology, but have not reassessed the basic foundations or assumptions of our models. The core components of our models--objectives, learning hierarchies and sequences, emphasis on convergent instructional activities-have become the cornerstones of our craft. To question them is regarded as heresy.

One significant consequence has been the insulation from fields where ISD's theoretical orientation is not embraced. The innovative activities of individuals, groups, and entire fields outside the ISD community are often viewed with disdain or antagonism. Much of the seminal work in artificial intelligence and

expert systems has gone unnoticed (see Lawler & Yazdani, 1987). With a few notable excep- tions (see, for example, Jonassen, 1986, 1988, 1991; Kinzie & Berdel, 1990; Streibel, 1988), technological developments in hypermedia have failed to alter instructional strategies ap- preciably. Constructivist models of teaching and acquiring knowledge (Perkins, 1991), wide-

spread throughout the sciences, have been

challenged as impractical (Merrill, Li, & Jones, 1990c). Cooperative teaching and learning work--even projects explicitly focusing on

computer adaptations (Johnson & Johnson, 1986)-has scarcely influenced typical CBI design. ISD's insulation from the broader world of teaching, learning, and technology has con- tributed to its isolation from mainstream educational trends, theory, and research.

THE EVOLUTION OF COMPUTER-BASED LEARNING ENVIRONMENTS

The concept of integrated, supporting activities centered around topical themes is neither new nor revolutionary. Since the early work of John Dewey (1933), idealized visions of learning environments have evolved. Students should develop interests in problems or theme areas, acquire varying degrees of formal knowledge, explore firsthand how relationships among current and other concepts might be



LEARNING ENVMMONMENS 51

established, pursue advanced applications of the concepts under study, and generate new

learning goals and priorities. Yet rarely have these visions been realized. In virtually all

cases, the logistics of implementing integrated learning systems have proved daunting.

Emerging technologies, and their implications for the design of learning environments, offer considerable promise. Learning environments are comprehensive, integrated systems that promote engagement through student- centered activities, including guided pres- entations, manipulations, and explorations among interrelated learning themes (Hannafin & Gall, 1990). Several essential elements are

reflected in this definition. Integration implies that the environment is constructed to support the student in accessing existing conceptual linkages or building new ones. Activities are provided that support the individual's efforts to mediate his or her learning. Guidance

supports the learner's decision making within the learning environment. Themes help to organize contexts, often in the form of a problem to be solved or an orienting goal, that bind the features and activities of the environment.

Learning environments supply interactive,

complementary activities that facilitate student-centered learning. Students are guided (rather than directed) in the availability and use of appropriate activities, each of which is linked conceptually around unifying learning themes. Individually, each component permits students to pursue understanding within established parameters. Students might, for ex-

ample, select a manipulation tool, request tutoring on a topic, or request elaborations of

key terms. Collectively, however, the components provide a rich set of resources that progressively broaden, rather than converge upon, learning themes.

Roots and Influences

Learning environments are neither singular in their attributes nor distinctly classifiable in a conventional sense. Instead, they refer to a

class of systems that integrate, to varying de-

grees, tools, resources, and pedagogical features that deepen comprehension. Several

developments have influenced their evolution:

(1) problems with traditional notions of learn-

ing; (2) shifts in psychological paradigms; (3) emphasis on student-centered learning; (4) unprecedented technological developments; (5) developments outside the ISD field; and (6) efforts within the ISD field.

Limitations of Traditional Learning Outcomes

Several authorities have cited shortcomings in traditional views of learning. For example, Salomon and Perkins (1989) noted that traditional methods support primarily "low road" transfer, that is, transfer that is largely regulated by the limiting focus and nature of the instructional stimuli. Bransford, Franks, Vye, & Sherwood (1989) detailed problems of "inert

knowledge"-knowledge that has been ac-

quired and demonstrated in a conventional sense, but that has little value to the learner in interpreting, modifying, or otherwise in-

fluencing performance. Such knowledge may take the form of momentary learning, where

knowledge is consciously retained until circumstances mediating the retention (e.g., a

test) are completed. Inert knowledge exists as "islands of information," which, while inde-

pendently retrievable, provide little mutual or interactive value.

Spiro's work in cognitive flexibility (see, for example, Spiro, Coulson, Feltovich, & Ander- son, 1988; Spiro, Feltovich, Jacobson, & Coul- son 1991; Spiro & Jengh, 1990), conducted ex- tensively with medical students, emphasized the problems of ill-structured knowledge, that is, domains where precise meaning or utility cannot be provided. Whereas the simplest of elements in a complex domain can almost al-

ways be taught, advanced knowledge invari-

ably requires insights and knowledge that cannot be taught algorithmically. Limitations exist in the ability to comprehend fully new or complex domains. As a result, topics are often taught and learned in simplistic, incomplete ways. Subsequently, learners apply simplistic understanding to more complex aspects of the domain. Teaching strategies need to promote flexibility to enable students to organize and invoke knowledge in



52 E-&D, Vo. 40. No.1 No.1

varied ways under conditions neither fully known nor understood during encoding, as well as foster "high road" (mindful abstrac-

tions) transfer. Other examples of problems associated with

traditional teaching methods have been re-

ported. Andrea di Sessa (1982) demonstrated that even advanced students suffer from naive misconceptions of fundamental Aristote- lian science concepts. Perkins and Simmons

(1988) provided an extensive analysis of common misunderstandings in science, mathematics, and computer programming. Much of the problem, according to the authors, can be traced to dogmatic teaching methods that promote regimental-and incomplete-understanding. Indeed, there is compelling evidence that negative transfer and durability are con-

sequences associated with incorrect initial

learning (see also Hannafin, 1988; McDer-

mott, 1984). Flexible teaching methods may militate

against many problems associated with naive

learning. Constructivists, for instance, view errors as largely transitional and functional if supporting educational methods are provided. They allow the student to evolve beliefs that can be modified, updated, and otherwise reconstructed as additional knowledge and ex-

perience is attained. Knowledge and beliefs about knowledge, even when erroneous, help the student generate hypotheses about the re-

lationships among objects, information, and events. Mistaken ideas help to establish tentative, dynamic beliefs which are subsequently used to interpret new, apparently contradic-

tory evidence. In effect, the student learns not

solely what is correct in an objective sense, but the insight that accompanies progressive refinements in understanding. In this context, errors are seen as supporting, not hampering, meaningful learning. (See Yackel, Cobb, Wood, Wheatley, & Merkel, 1990, for a more detailed account of the role of errors from construc-

tivist perspectives.) Successful teaching encompasses a broad

range of activities that are organized loosely around broad, orienting educational goals. Apart from providing instructional "events" (Gagn6, Briggs, & Wager, 1988), good teachers pose questions requiring comparisons and

informed speculation. Further, they require student self-assessments and stimulate ways to assist in integrating knowledge (Shuell, 1988). In effect, they supply methods that invoke greater introspection and reflection by the student during learning. Good teachers

acquire expertise that rarely limits their functions to knowledge disseminator. Instead, they focus on activities that cause students to process information in unique ways that deepen understanding. Effective teaching rarely em- bodies simple telling and is rarely limited to the transmission of formal knowledge (Berli- ner, 1990).

Ironically, although countless definitions have been offered for ISD, few have been presented for instruction per se. One can com-

fortably infer, however, that it is an organized set of methods, materials, and assessments

designed to promote competence in defined outcomes (cf. Dick, 1991). Instruction is directive in nature; instructional designers typically structure both the content and the methods used to convey lesson content. Les- son content is prioritized and organized into instructional sequences, activities are devel-

oped to support intended learning, and learners proceed through prescribed activities and

sequences. Even in cases where learner control is provided, it typically provides externally dictated access to embedded instructional

strategies (such as help, glossaries, quantity or complexity of examples or questions) and

segments (such as menu selection affecting lesson segment order and continuation-term- ination decision points). The learner may or

may not decide which (and sometimes when) available options will be used.

While instruction, as operationalized in ISD, may be effective for defined outcomes, it may be comparatively ineffective for broader learn-

ing goals. In many cases, learning goals and activities are substantially less explicit, iden- tifiable, and singular, while being substantially more complex, individual, and internally centered, than as addressed via instructional de-

sign methods (Kember & Murphy, 1990). For many educators, traditional instructional procedures are too rigid to be adapted and require too many assumptions about the nature of external control in knowledge acquisition.



LEARNING ENVIRONMEN1S 53

Shifts in Paradigms

Successful learning requires more than literal

encoding of defined aspects of formal instruction. It requires that knowledge be assimilated,

perceptions of value, meaning, and importance be derived, existing knowledge be eval- uated concurrently with new knowledge, and

knowledge be reconstructed accordingly (Han- nafin & Rieber, 1989a). These are principally internal, learner-directed processes that can be supported, but not explicitly regulated, externally.

ISD methods, and instructional design products, are largely convergent and reduc- tionistic in nature. They are perceived as fo-

cusing on the part rather than the whole. They emphasize the systematic organization of to- be-learned lesson information and the design of activities that support the acquisition of dis-

cretely defined knowledge and skills. This process invariably requires the student to learn

according to the sequence and structure of

progressively ordered, externally imposed instructional activities. In many cases, especially where the external structuring of knowledge and learning of clearly specified content and

procedures are required, such methods are effective and valuable.

In other cases, neither external structuring nor strict outcome-based accountability is em-

phasized. In some fields, learning emphasizes process over product; relevant domains are situated within contexts in which they derive

meaning (Bereiter, 1990; Brown, Collins, & Duguid, 1989). In science, for example, authorities have argued that the demise of scientific reasoning among today's youth can be traced to the treatment of science as discrete

knowledge that is presented to children. Stu- dents are taught facts, rules, and "truths" of scientific disciplines but acquire few insights. In many cases, scientists value the processes of scientific reasoning and inquiry (called "sci- encing") far greater than formal scientific knowledge (DeVito & Krockover, 1980a; 1980b). For ISD, this requires more than an identification of the formal knowledge and skills of a complex domain; it requires an understanding of the evolution of understanding, the importance of acquiring insight, and

awareness of mechanisms that induce student

engagement. Shifts emphasizing the individual's role in

mediating learning, and in the correspond- ing design implications, have played an

important role in the evolution of learning environments. Significant work in cognitive psychology, for example, has yielded teaching and instructing guidelines that represent a significant shift in the locus of activity. External

agents (teachers, instructional materials) are viewed increasingly as activators for learning rather than mediators of knowledge.

Developments in situated cognition (Brown, Collins, & Duguid, 1989) and related work in anchored instruction (Cognition and Technol-

ogy Group at Vanderbilt, 1990) are also significant. Such perspectives view cognition and the circumstances supporting learning as in-

separable. Rather than decontextualizing learning by isolating and making explicit "required" elements, it may be fundamentally more productive to embed desired elements within "authentic" activities wherein the

knowledge and skills naturally reside. Con- sistent with, while also extending, cognitive views, learning is more inherently meaningful when relevant contexts are available and

appropriately structured. In Bereiter's (1991) treatment of connection-

ism, a construct studied widely both in cognitive science and among artificial intelligence researchers, distinctions are drawn between

cognition as a rule-based versus connection- based activity. Popular notions of cognition presume that thinking is a process guided by complex sets of rules which, if fully understood, enable cognitive processes to be mapped more or less algorithmically. Connectivists reason that the relationships among connected elements adapt dynamically as varied circumstances affect different member elements in different ways. Learners need not be trained in all procedures likely to be useful, but must evolve strategies for how to manipulate connected elements to adapt to varied circumstances. In effect, knowledge resides in the connections themselves-their richness, strength, and complexity-not in the individual collection of data elements. Such a perspective requires that learning be stimulated not by mastery of



54 ER&D, Vol. 40, No. 1

formal knowledge per se, but by activities that

progressively refine and qualify relationships among connected elements.

Constructivists have also influenced the evolution. Paris and Byrnes (1989) described several principles undergirding constructivist

approaches. Constructivists perceive that the individual, as an active organism, has intrin- sic motivation to seek information. These mo- tivations need to be exploited rather than neutralized during learning. Next, under-

standing is thought to transcend the information given. Learners continually interpret events, and form opinions and tentative con- clusions based upon their interpretations. Constructivists believe that mental representations change with development. Construc- tivists also note that progressive refinements in understanding occur; learning is a contin- uous rather than a discrete process. Construc- tivists further believe that developmental constraints on learning exist. This has been characterized as the "zone of proximal devel-

opment," a cognitive readiness that is essential to profit from given activities (Vygotsky, 1978). Finally, constructivists note that reflection and reconstruction need to be promoted over activities that emphasize assimilation alone.

Recent interest in contextualizing learning experiences has been widespread. This has taken the form of anchoring instruction within

powerful real-life problems or situating cognition in relevant contexts (Cognition and

Technology Group, 1990). Such paradigms rely heavily on the power of a supporting context to embed a variety of potentially complex problems. In such projects, the selection and

design of the supporting context effectively drives the strength of the anchored learning, and vice versa.

Student-Centered Learning

Interest in student-centered learning has

grown dramatically during the past decade. Student-centered learning systems essentially define the student as the principal arbiter in making judgments as to what, when, and how learning will occur. Typically, students not only select and sequence educational activities, but identify, create, cultivate, pursue,

and satisfy their individual learning needs

(Hooper & Hannafin, 1991). Student-centered

learning systems tacitly presume that students

possess the metacognitive skills needed to make effective judgments, or that they can be induced to make appropriate choices through advice, hints, or guided reflection.

The implications for design are that emphasis is typically focused on supporting student-initiated lesson navigation, providing an

organizing theme or context for lesson activities, and embedding aids and support in the form of help, elaboration, and other resources that can be selected by the student to improve understanding. Successful student-centered

learning systems require that a sufficient array of resources be available to enable students to both assess and address knowledge and skill needs as they evolve. The role of instruction in such environments is to provide substantive support for student-initiated knowl-

edge or skill development, not necessarily to

provide the principal vehicle for knowledge transmission. In certain cases, students might successfully learn important knowledge and skills and derive in-depth understandings, yet receive no formal instruction per se.

Student-centered learning systems have taken many forms. For example, several researchers and theorists, largely apart from the ISD field, have espoused the virtues of student-centered microworlds (e.g., Levin & Waugh, 1987). Microworlds are incubators for knowledge, that is, systems that provide environments where learning is nurtured rather than knowledge taught (Papert, 1980). Papert's conceptual framework, influenced more by de-

velopmental than pedagogical theory, emphasizes the ". . . model of children as builders of their own intellectual structures" (p. 7). Al-

though macro-environments for intellectual

development (LOGO in particular) have been challenged within the ISD field, enthusiasm for student- versus instruction-centered learn-

ing is considerable and widespread (e.g., Duffy & Jonassen, 1991; Hannafin & Rieber, 1989b; Kember & Murphy, 1990; Perkins, 1991).

Nevertheless, student-centered learning environments do have their own set of complex problems. The capacity of many students to mediate their learning in accountability-based



LEARNING ENVIRONMENTS 55

educational settings has not been demonstrated. Indeed, many students are ill equipped to make effective choices during a lesson

(Steinberg, 1989). In effect, since much of what mediates effective student choice is related to

prior knowledge, student-centered environments may prove inefficient or ineffective (Mer- rill, Li, & Jones, 1990c). In addition, due to the non-directiveness of typical student-centered environments, students may focus their attention on relatively unimportant lesson features or content. The student's individual need to seek knowledge and pursue his or her own evolving interests may be satisfied, but fundamental knowledge and skills may not be learned (Dick, 1991). Few challenge the goal of supporting the unique intellectual devel-

opment of learners, but the pedagogical im-

plications remain debatable.

Rapid Technological Advances

Unprecedented technological refinements have been reported in areas such as high- density optical storage, miniaturization, input and output, and connectivity among techno-

logical devices. This expanded tool kit has en- abled designers to vary presentation stimuli in ways heretofore impossible. Objects can be

presented in forms that closely represent their

objective properties, supporting the design of

extraordinarily realistic simulations. Human factors considerations, both in the structure of activities and in the nature of human-

computer transactions, are now addressable. For example, the application of real-time, input-output design principles in aviation allow simulation of the sensory aspects of both a pilot's actions (e.g., mistakes causing engine stall) and reactions (e.g., G-force increases dur-

ing acceleration). The management capabilities of the com-

puter, especially in data manipulation, have also improved dramatically. The ability to build and rapidly access large and complex databases, in forms ranging from expert knowledge representations, to encyclopedic resources, to image libraries, to personal knowledge representations, greatly expands the volume of information that can be immediately addressed. By overcoming the many logistical

limitations inherent in traditional instructional units or modules, computer-supported learn-

ing environments have become increasingly viable.

The developments of overarching signifi- cance for learning environments, however, are in hypermedia. Hypermedia refers to computer-mediated access to elements contained in varied media (Marchionini, 1988). The designer's tool kit can be extended substantially by linking a variety of knowledge resources found across a range of media. However, the key dimension in hypermedia is not simply the abil-

ity to link media, but the ability to manage how linkages occur. At the designer's discre- tion, linkages can range from completely unmanaged, allowing the student to access

any information from any of the available resources at any time based on individual beliefs, to tightly managed, contingent on precisely prescribed relationships that constrain access and based on beliefs external to the student.

Hypermedia not only permit the construction of exceptionally elaborate conventional instructional designs, but also enable sophisticated alternative learning environments that stand in sharp contrast with conventional

practice. Hardware technology has far surpassed the

sophistication of our associated design tech-

nology (Hannafin, 1989). Often we have sim-

ply "harnessed" technology, assimilating new technologies to accommodate our traditional notions of instructional design. In other instances, there exists no obvious organized system for making judgments about technol-

ogy utilization. It is apparent that new design notions must evolve if we are to optimize the capability of emerging technologies for

learning.

External Research and Development

Outside the ISD field, several trends have

emerged. Both innovative prototypes and full- scale operational learning systems have flour- ished. There has been a decided emphasis on creating qualitatively different learning experiences rather than re-hosting older ones. Learning systems are widely viewed as a means rather than an end, especially in edu-



56 EM&D,ol. EM&D,ol. 40, No. 1

cational settings (cf. Salomon, Perkins, & Globerson, 1991). The overriding goals of such

systems are to promote application and ma-

nipulation of knowledge, not simply to acquire the knowledge itself.

Significant advances have also been re-

ported in allied fields. Although artificial in-

telligence (AI) is still in its infancy, significant work has been reported on its implications for the design of learning environments (see Brown, 1989, and Lawler & Yazdani, 1987). Ef- forts to support expert Al-based diagnoses, subject matter analyses, teaching tactics, and

teaching strategies have also been reported (Ohlsson, 1987). Attempts to better understand the nature of how experts reason have been widespread.

Duchastel (1990) described different types of cognitive tools, each of which supports functions distinct from those typically consid- ered in the ISD field. Tools, in this context, refer to features that augment an individual's

ability to learn or act. Power tools ". . . augment cognition of a structural nature" (p. 4; see also empowering environments per J. S. Brown, 1985). They support comprehension by helping to overcome misconceptions while

guiding the formation of mental models. Students used di Sessa and White's (1982) "dynaturtle" to manipulate operationally several Aristotelian physics concepts, for which

misconceptions were common. (See also Rieber's article in this issue.) Assimilatory tools, on the other hand, help individuals to

integrate information within existing schemata.

They help the individual to make sense of the

theoretically limitless ranges of data and information available in various forms, sources, and media. Such tools permit on-demand access to relevant resources, such as in many hypertext systems, when the individual's need to know has been established.

Concern over the limited perspectives of the instructional design field has also been ex- pressed. John Carroll (1990) concluded that ISD, by overemphasizing the role of formal instruction versus concrete experience, has failed to provide meaningful educational experiences via the computer. The preoccupation with objective specification is, in large mea- sure, an artifact of the ISD field's own history,

not necessarily a response to the priorities of the varied fields where learning is valued.

Experience, inquiry, manipulation, prediction, and a host of other learning processes are

widely viewed as being at least as essential to successful learning as the attendant formal knowledge. Instructional perspectives alone may limit our views of the design of more inclusive computer-based learning environments.

Not surprisingly, the activity boom outside the ISD field has suffered as well as prospered. Despite tremendous growth in activity, there

appears to be no overriding framework guiding system design and no theoretical founda- tion undergirding most efforts (Spiro & Jehng, 1990). Projects rarely reflect strong ground- ing in contemporary psychological or pedagogical research and theory. The efforts have been fragmented. The processes are often intuitive and untested. Few attempts have been made to study such systems empirically and consensus has yet to be reached as to their

design. There is a significant opportunity for our field to operationalize design methods for

systems of diverse conceptual roots.

ISD Contributions

Merrill, Li, and Jones (1990a) detailed several

shortcomings of traditional ISD that have become increasingly apparent with emerging instructional technologies. Problems cited were excessive fragmentation, the closed nature of ISD processes, and the tendency to

promote passive rather than active learning. While Merrill, Li, and Jones (1990b) retain a commitment to instruction-centered paradigms, they underscored many of the problems inherent in traditional ISD practice.

At the same time, increased attention has focused on reconceptualizing some basic elements of instructional design. Gagn4 and Mer rill (1990), for instance, acknowledged a "... need for treating human performance at a somewhat higher level of abstraction than is usual in most instructional design models" (p. 24). Successful performance, they suggest, is more typically the application of complex sets of knowledge and skills, not the acquisition of the knowledge and skills in isolation. They



LEARNING ENVIRONMEN1S 57

described a need to identify learning goals that, in effect, require concurrent integration of multiple objectives.

Other recent developments related to alternative approaches have been reported. In response to Merrill's second-generation instructional design model, Kember and Murphy (1990) described a host of alternative directions for instructional designs rooted in constructivism. During the 1991 annual meetings of the Association of Educational Communications and Technology and American Educational Research Association, several sessions emphasizing alternative approaches were conducted. Recently, Educational Technology published a special issue related to constructivism, instruc- tionism, and educational technology in which several authorities presented alternative em- pirical or theoretical approaches (see, for example, Duffy & Jonassen, 1991; Cognition and Technology Group, 1991; Perkins, 1991; Spiro et al., 1991). This renewed attention reflects a growing recognition of, and interest in, alternative perspectives in learning system design.

Clearly, although new and potentially revolutionary possibilities exist, many problems remain. As a field, we must acquire a better sense of our fit with contemporary developments. We need to broaden our notions of design to better understand emerging tech-

nologies and the views of others, but we must also seek to influence future developments in ways that are thoughtful and productive. We need extended design methodologies, the likes of which are only beginning to emerge.

Perhaps the time has come to critically reex- amine the foundations, assumptions, and procedures of our craft. Instructional design reflects expert views on the structure of content and strategies designed to teach content, not necessarily the manner in which knowledge could or should be learned. Instruction may be algorithmic, but learning is not; an instructional design provides one way-not necessarily the only way or the best way-to promote learning.

LEARNING ENVIRONMENTS: CLASSIFICATIONS AND EXAMPLES

Learning environments share a variety of dimensions: scope, activities (user and educational), and content integration methods. These are shown in Figure 1. Each dimension exists as a continuum, and learning environments possess attributes along each continuum. The remainder of this article focuses on learning environments and the ways in which they exemplify one or more of the shared dimensions.

FIGURE 1 0 Dimensions of Learning Environment

DIMENSION

SCOPE

CONTENT INTEGRATION

USER ACTIVITY

EDUCATIONAL ACTIVITY

DIMENSION CONTINUUM

Macro

Cross

Generative

Goal-Directed

Micro

Within

Mathemagenic

Exploratory



58 ETR&D. EM&D,ol. 40, No.1

Scope

Scope refers to the inclusiveness of the environment, both with respect to content cover-

age and the extent to which educational features are available to the learner.

Macro-Level Environments

Macro-level learning environments emphasize comprehensive treatment among interrelated information, concepts, and activities. They attempt to provide vehicles for broadening the context for the lesson while enabling students to pursue interests or needs beyond the parameters typically provided in isolated lessons.

ScienceVision (formerly Science Quest) provides a rich set of complementary activities in a hypermedia environment (Litchfield, 1990). Students are provided a wide range of tools and resources from which to explore the various features of the environment, ranging from simple glossaries, to video encyclope- dias, to advice from experts, to manipulation components, and so on. In addition, a significant array of both on- and off-line resources

(e.g., log books) and activities (e.g., project options) are provided. The system incorpo- rates concepts from several fields and varied methods of student-centered learning. (See also Tobin and Dawson's article in this issue.)

Micro-Level Environments

Micro-environments provide a high degree of focus on a relatively discrete domain, permitting detailed examinations and explorations among interrelated skills and concepts. Whereas micro-environments are not inte-

grated explicitly with a larger range of con-

cepts, they often represent a synthesis of

several skills and concepts. Streibel and colleagues developed MENDEL,

a learning environment that provides learning resources rather than instruction in the solving of prescribed genetics problems (Streibel, Stewart, Koedinger, Collins, & Jungck, 1987). Students initially construct tentative hypotheses regarding individual genetics experiments, and the computer subsequently generates data

consistent with expert notions of predicted outcomes. However, MENDEL neither instructs students in "correct" procedures nor solves the problem for them (even though an expert system is available to do so). Instead, the system provides expert advice to students on how to evaluate their own predictions and hypotheses and how to reassess their assumptions to test progressively more refined hypotheses.

User Activity

Learning environments also vary as a func- tion of the nature of learner activity. For exam-

ple, many learning environments provide complex methods for accessing existing information; others emphasize the creation of environments that support the representation of

knowledge.

Generative Environments

Generative environments rely on the individual

(or group of individuals) to create, elaborate, or otherwise represent knowledge. Typically, they supply either a context within which students produce actions designed to clarify, manipulate, or otherwise explore the environment, or a framework within which student

representations of meaning can be generated. In the former case, the situation essentially guides individual cognition; in the latter, the elements of the representation system guide student actions.

The Cognition and Technology Group at Vanderbilt University (1990, 1991) has anchored mathematics instruction in relevant real-life contexts. (See the article by the Cognition and

Technology Group in this issue for a more detailed explanation.) Within these contexts, students are not so much taught as provided circumstances within which critical mathematics problem-solving skills are naturally embedded. Rather than being taught the reasoning skills in a directive manner, students investi-

gate alternatives and determine information requirements. Students generate plans, identify knowledge requirements, test their plans, and revise them as needed to solve the problem.




Scardamalia and her colleagues created Com- puter-Supported Intentional Learning Environment

(CSILE), a prototype designed to support students in the purposeful, intentional processing of lesson information (Scardamalia, Bereiter, McLean, Swallow, & Woodruff, 1989). Using CSILE, groups of students generate knowledge bases, including student notes, related text, drawings, graphs, tabular data, and so forth. The system provides various heuristics and guidelines that assist students in the construction of a shared knowledge base, representing alternate ways that concepts are organized and understood by individuals. Rather than presupposing explicit external structure to the knowledge base, CSILE provides support for individuals or groups to organize and construct knowledge in unique ways.

Mathemagenic Environments

Among the most common applications of hypermedia learning environments are those that support access to various representations of content. In many systems, for example, students might be permitted to access existing glossaries, video clips, encyclopedic information, tutorial instruction, and other representations of to-be-learned content in order to vary the manner in which information is organized as well as the method in which it is provided. The content is structured externally and is often available in multiple ways to permit the student to learn according to externally generated notions of meaning.

At Brown University, Yankelovich and colleagues applied advanced hypertext methods in the design of an environment called Dick- en's Web (Yankelovich, Haan, Meyrowitz, & Drucker, 1988). Conceptual ties (links) to related literary concepts (nodes) were established to connect related textual materials, permitting students to move rapidly among networks of concepts. The environment also allows students to construct their own sets of relationships within the network, allowing the system to learn and subsequently invoke individual representations of the lesson content. The system is designed to sup-

port connections beyond a particular author or topic by providing cross-topic linkages and pathways.

Educational Activity

The nature of the educational activity-the emphasis on goal-directed, intentional learning versus student-directed exploration-is another dimension that differentiates learning environments.

Goal-Directed Environments

Goal-directed environments emphasize intended competence, facility, or comprehension. The activities are designed to support a defined set of learning outcomes. Students may be provided considerable flexibility in employing the features of the environment, but all features are structured to promote flu- ency in prescribed areas.

Harless (1986) reported the design of a sophisticated hypermedia environment designed to simulate intake and follow-up treatment plans required of emergency room physicians. Although a variety of alternative videodisc scenarios could be presented-and many variations could evolve, depending on the physician's responses and the evolving health of the patient-the goals were consis- tently prescribed. Physicians-in-training iden- tified symptoms, selected needed procedures and tests, determined whether or not to admit a patient, prescribed and followed up on treatment plans, and so on. The environment provided a host of both ongoing and summative feedback to the participant as to patient sta- tus as well as success and cost of treatment.

Exploratory Environments

Exploratory environments emphasize processes more than outcomes, at least insofar as intended learning is concerned. Often, students are encouraged to alter, explore, or otherwise manipulate the parameters of the environment to examine possible out-




comes. The emphasis is on learning as a con- structive, individually mediated process rather than as an accountability-based process based

upon external notions of importance and relevance.

Geometric Supposer (Schwartz & Yerulshamy, 1987), for instance, focuses on a comparatively narrow range of mathematics topics, but provides an unusually powerful array of tools that

promote deeper processing and understanding. There are no explicit performance expectations; instead, support is provided for student- centered exploration and manipulation. Stu- dents are given tools that enable them to

explore, predict, and manipulate geometric phenomena to create highly visual and interactive experiences with geometry.

Content Integration

Although content integration is a trademark of all learning environments, the manner in which integration occurs can vary widely. In

many cases, the environment promotes inte-

gration among closely allied knowledge or

concepts; in others, the environment emphasizes content integration beyond the range nor-

mally associated with a given topic or subject.

Cross-Content Integration

Cross-content integration attempts to minimize the explicit or implicit boundaries of subject matter by featuring information, concepts, and skills in varied contexts. Multiplication skills, for example, might be integrated with social studies (e.g., one candidate receiving three times more votes than another), with language (e.g., one word having three times more let-

ters than another), with science (e.g., one

weight being three times heavier than another), and so on. Skills and knowledge are not isolated and taught out of context, but are introduced and developed within a variety of

meaningful contexts. Again, ScienceVision integrates content

across various fields. Several related areas

(e.g., citizenship, mathematics, career education) are represented within a relevant con-

text. For example, information regarding a

range of careers related to the environment is nested within an ecology unit. This is provided both in the form of descriptive information as well as by virtue of interviews with various professionals in the field. Addition-

ally, a number of mathematical concepts are

presented in context, as are various map- ping methods and activities. Students, again within the context of a real-life problem, also

explore the rights and responsibilities of individuals to protect and maintain environmental standards. Although the overarching theme is related to science, the content is heavily contextualized and integrated with other related fields.

Within-Content Integration

Content integration can also be focused within

given domains. For example, in learning the

meaning of the term "freedom," students

might receive a variety of situations and exam-

ples from history that exemplify concepts related to freedom. The context may shift from the flight of the Pilgrims, to the early Revolu-

tionary War, to the repression of the slaves, to current-day examples of civil riglfts viola- tions. All instances enrich the understanding of freedom within the context of history; all

presumably enrich the students' understand-

ing of freedom within historical contexts.

Spiro et al. (1988; 1991) reported the devel-

opment of hypertext systems designed to broaden the number of ways in which advanced, often ill-structured, knowledge can be acquired. One system, Cardioworld Explorer, focuses on the complexities involved in under-

standing complex, conditional aspects of the cardiovascular system. Another system, Explor- ing Thematic Structure in Citizen Kane, merges original film and portions of text to promote advanced understanding of a film in which segments can be reexamined for meaning from multiple perspectives. In this system, conditionally relevant knowledge-knowledge that assumes very different meaning under different circumstances-is explored from multiple perspectives to promote cognitive flexibility.



LEARNING EMNRONMENTS 61

It is apparent that varied and powerful learning environments have been developed, reflecting a diversity of pedagogical processes that differ vastly among the systems themselves as well as from typical ISD methods. These samples represent only a fraction of the available applications. Increasingly, open- ended systems such as those mentioned are

redefining "state-of-the-art."

EMERGING TECHNOLOGIES, ISD, AND THE FUTURE OF LEARNING ENVIRONMENTS

How, or will, the ISD field influence future

developments in computer-mediated learning environments? Collectively, we remain en- trenched in our historic views about teaching, learning, and instructing. Perhaps we have been blinded by our own success. Significant innovations have been advanced, yet we are not yet partners in such developments.

What can the ISD field learn from developments in emerging technologies? Learning environments offer alternatives-potentially powerful and effective alternatives--to many traditional instructional goals. The goal of instruction, like the goal of learning environments, is to support learning. However, the foundations, assumptions, and methods are

quite distinct. Instruction seeks to build

competence according to external conventions; learning environments seek to induce it

through largely internal mediation. Individ- uals can, and in some cases must, assume a

greater role in regulating, and not merely par- ticipating in, lesson activities.

Alternative approaches are often complementary to current practice, but they can also be at odds with it. It is not difficult for ISD

professionals to envision environments where knowledge is taught via traditional instructional methodologies while tools and resources are provided to enrich and elaborate, i.e., as supplemental or enrichment activities for instruction. However, this is neither the only way nor the method of choice in many fields.

We must learn both to understand and respect alternative approaches if we expect to influence their evolution.

ISD also has significant contributions to make to emerging technology. We have extracted enviable precision in the processes used to

plan, implement, and validate educational solutions. Few fields have logical design and

development procedures of comparable power or sophistication. We have also evolved robust

psychological and pedagogical foundations from which to generate empirically referenced

design guidelines and heuristics. We have assisted learners during complex lesson nav-

igation, refined human factors considerations, improved the design of complementary mes-

sages, developed protocols for screen design, and so on. In many cases, these are precisely the areas where learning environment design needs are most acute.

The problems and limitations of learning environments must also be addressed. Al-

though evidence of application to widely varied lesson content has been demonstrated, some learning tasks are certainly more ame- nable than others to learning environment features and components. Further, not all

important activities must be delivered via com-

puter. High-technology solutions need not be

employed where they are unnecessary. In- deed, there is much to be learned about the

pragmatic aspects of learning environments. The intent of this article is not to diminish

either the importance or the successes of the ISD field. ISD procedures have proven effi- cient, effective, and valuable across a wide

array of problems and settings. The question is not whether instruction has a place in computer-based learning environments of the future, for it most certainly does. The more critical question may be, "Is that enough?"

Will the ISD field assume a significant role in conceptualizing more inclusive computer- based learning environments in the future? Is instruction as historically operationalized sufficient to accomplish the more inclusive goals of successful learning? Are the models employed to develop instruction sufficiently robust to accommodate learning goals that are qualitatively different from those traditionally addressed? If so, how do we apply them? If not, upon what are our designs based? These are significant questions. They require significant answers. O




REFERENCES

Andrews, D., & Goodson, L. (1980). A compara- tive analysis of models of instructional design. Journal of Instructional Development, 3, 2-16.

Bereiter, C. (1990). Aspects of educational learning theory. Review of Educational Research, 60, 603-624.

Bereiter, C. (1991). Implications of connectionism for thinking about rules. Educational Researcher, 20(3), 2-9.

Berliner, D. (November, 1990). Expertise in pedagogy: Implications for reform. Paper presented at the Dean's Seminar Series, Florida State University, Tallahassee, FL.

Bransford, J., Franks, J., Vye, N., & Sherwood, R.

(1989). New approaches to instruction: Because wisdom can't be told. In S. Vosniadou and A.

Ortony (Eds.), Similarity and analogical reasoning. New York: Cambridge University Press.

Brown, J. S. (1985). Process versus product: A perspective on tools for communal and informal electronic learning. Journal of Educational Computing Research, 1, 179-201.

Brown, J. S. (1989). Toward a new epistemology for

learning. In C. Frasson & J. Gauthiar (Eds.), Intel-

ligent tutoring systems at the crossroad of Al and education. Norwood, NJ: Ablex.

Brown, J. S., Collins, A., & Duguid, P. (1989). Sit- uated cognition and the culture of learning. Edu- cational Researcher, 18(1), 32-41.

Carroll, J. (1990). The Nurnberg Funnel: Designing min- imalist instruction for practical computer skill. Cam-

bridge, MA: MIT Press.

Cognition and Technology Group at Vanderbilt (1990). Anchored instruction and its relationship to situated cognition. Educational Researcher, 19(6), 2-10.

Cognition and Technology Group at Vanderbilt (1991). Technology and the design of generative learning environments. Educational Technology, 31(5), 34-40.

DeVito, A., & Krockover, G. (1980a). Creative sci-

encing: A practical approach (2nd ed.). Boston: Little, Brown.

DeVito, A., & Krockover, G. (1980b). Creative scienc-

ing: Ideas and activities for teachers and children (2nd ed.). Boston: Little, Brown.

Dewey, J. (1933). How we think. Boston: Heath. Dick, W. (1991). An instructional designer's view of

constructivism. Edumational Technology, 31(5), 41-44. diSessa, A. (1982). Unlearning Aristotelian phys-

ics: A study of knowledge-based learning. Cog- nitive Science, 6, 37-75.

diSessa, A., & White, B. (1982). Learning physics from a dynaturtle. Byte, 7, 324.

Duchastel, P. (1990). Assimilatory tools for informal learning: Prospects for ICAI. Instructional Science, 19, 3-9.

Duffy, T., & Jonassen, D. (1991). Constructivism: New implications for instructional technology? Educational Technology, 31(5), 7-12.

Gagn6, R., Briggs, L., & Wager, W. (1988). Princi- ples of instructional design (3rd ed.). New York: Holt, Rinehart, & Winston.

GagnL, R., & Merrill, M.D. (1990). Integrative goals for instructional design. Educational Technology Research and Development, 38, 23-30.

Gustafson, K. (1991). Survey of ID models (2nd ed.). Syracuse, NY: ERIC Clearinghouse of Informa- tion Resources.

Hannafin, M. J. (1988). The effects of instructional explicitness of learning error persistence. Contem- porary Educational Psychology, 13, 126-132.

Hannafin, M. J. (1989). Interaction strategies and emerging instructional technologies: Psycholog- ical perspectives. Canadian Journal of Educational Communication, 18, 167-179.

Hannafin, M. J., & Gall, J. (1990, October). Emerg- ing instructional technologies and learning environments: From instruction- to learner-centered models. Paper presented at the annual meeting of the Association for the Development of Computer- Based Instructional Systems, San Diego, CA.

Hannafin, M. J., & Rieber, L. P. (1989a). Psycho- logical foundations of instructional design for emerging computer-based instructional technologies: Part I. Educational Technology Research and Development, 37, 91-101.

Hannafin, M. J., & Rieber, L. P. (1989b). Psycho- logical foundations of instructional design for

emerging computer-based instructional technologies: Part II. Educational Technology Research and Development, 37, 102-114.

Harless, W. (1986). An interactive videodisc drama: The case of Frank Hall. Journal of Computer-Based Instruction, 13, 113-116.

Hooper, S., & Hannafin, M. J. (1991). Psychological perspectives on emerging instructional technologies: A critical analysis. Educational Psychologist, 26, 69-95.

Johnsen, J., & Taylor, W. (1991). Instructional technology and unforeseen value conflicts: Toward a critique. In G. Anglin (Ed.), Instructional technology: Past, present, and future. Englewood, CO: Libraries Unlimited.

Johnson, D., & Johnson, R. (1986). Computer- assisted cooperative learning. Educational Technol- ogy, 26, 12-18.

Jonassen, D. (1986). Hypertext principles for course- ware design. Educational Psychologist, 21, 269-292.

Jonassen, D. (1988). Designing structured hypertext and structuring access to hypertext. Educational Technology, 28(11), 13-16.

Jonassen, D. (1991). Hypertext as instructional design. Educational Technology Research and Devel- opment, 39, 83-92.

Kember, D., & Murphy, D. (1990). Alternative new directions for instructional design. Educational Technology, 30(8), 42-47.

Kinzie, M., & Berdel, R. (1990). Design and use of hypermedia systems. Educational Technology Re- search and Development, 38, 61-68.




Lawler, R., & Yazdani, M., Eds. (1987). Artificial intelligence and education: Volume I. Norwood, NJ: Ablex.

Levin, J., & Waugh, M. (1987). Educational simulations, tools, games, and microworlds: Computer- based environments for learning. International Journal of Educational Research, 12(1), 71-79.

Litchfield, B. (1990). Science Quest: A multimedia inquiry based videodisc science curriculum. In- struction Delivery System, 4(3), 12-17.

Marchionini, G. (1988). Hypermedia and learning: Freedom and chaos. Educational Technology, 28(1), 8-12.

McDermott, L. (1984). Research on conceptual understanding in mechanics. Physics Today, 37, 24-32.

Merrill, M. D., Li, Z., & Jones, M. (1990a). Limita- tions of first generation instructional design. Edu- cational Technology, 30(1), 7-11.

Merrill, M. D., Li, Z., & Jones, M. (1990b). The second generation instructional design research program. Educational Technology, 30(3), 26-31.

Merrill, M. D., Li, Z., & Jones, M. (1990c). ID2 and constructivist theory. Educational Technology, 30(12), 52-55.

Ohlsson, S. (1987). Some principles of intelligent tutoring. In R. Lawler & M. Yazdani (Eds.), Arti- ficial intelligence and education: Volume . Norwood, NJ: Ablex.

Papert, S. (1980). Mindstorms. New York: Basic Books.

Paris, S., & Byrnes, J. (1989). The constructivist approach to self-regulation and learning in the classroom. In Zimmerman and Schunk (Eds.), Self-regulated learning and academic theory, research, and practice (pp. 169-199). New York:

Springer-Verlag. Perkins, D. (1991). Technology meets constructiv-

ism: Do they make a marriage? Educational Tech- nology, 31(5), 18-23.

Perkins, D., & Simmons, R. (1988). Patterns of mis- understanding: An integrative model for science, math, and programming. Review of Educational Research, 58, 303-326.

Salomon, G., & Perkins, D. (1989). Rocky roads to transfer: Rethinking mechanisms of a neglected phenomenon. Educational Psychologist, 24, 111-142.

Salomon, G., Perkins, D., & Globerson, T. (1991). Partners in cognition: Extending human intelligence with intelligent technologies. Educational Researcher, 29(3), 2-9.

Scardamalia, M., Bereiter, C., McLean, R., Swal- low, J., & Woodruff, E. (1989). Computer-supported intentional learning environments. Journal of Educational Computing Research, 5, 51-68.

Schiffman, S. (1991). Instructional systems design: Five views from the field. In G. Anglin (Ed.), Instructional technology: Past, present, and future. Englewood, CO: Libraries Unlimited.

Schuell, T. (1988). The role of the student in learning from instruction. Contemporary Educational Psy- chology, 13, 276-295.

Schwartz, J., & Yerulshamy, M. (1987). The "Geo- metric Supposer": Using microcomputers to restore invention to the learning of mathematics. In D. Perkins, J. Lochhead, & J. Bishop (Eds.), Thinking: Proceedings of the second international conference (pp. 525-536). Hillsdale, NJ: Lawrence Erlbaum.

Spiro, R., Coulson, R., Feltovich, P., & Anderson, D. (1988). Cognitive flexibility theory: Advanced

knowledge acquisition in ill-structured domains. In Tenth Annual Conference of the Cognitive Science Society (pp. 375-383). Hillsdale, NJ: Lawrence Erlbaum.

Spiro, R. J., Feltovich, P. L., Jacobson, M. J., & Coulson, R. L. (1991). Cognitive flexibility, constructivism, and hypertext: Random access instruction for advanced knowledge acquisition in ill-structured domains. Educational Technology, 31(5), 24-33.

Spiro, R., & Jengh, J. (1990). Cognitive flexibility, random access instruction, and hypertext: The- ory and technology for non-linear and multidi- mensional traversal of complex subject matter. In D. Nix and R. Spiro (Eds.), Cognition, education, and multimedia: Exploring ideas in high technology. (pp. 163-205). Hillsdale, NJ: Lawrence Erlbaum.

Steinberg, E. (1989). Cognition and learner control: A literature review, 1977-1988. Journal of Computer- Based Instruction, 16, 117-121.

Streibel, M. (1988). Instructional plans and situated learning: The challenge of Suchman's theory of situated action for instructional designers and instructional systems. Journal of Visual Literacy, 9(2), 8-34.

Streibel, M., Stewart, J., Koedinger, K., Collins, A., & Jungck, J. (1987). MENDEL- An intelligent computer tutoring system for genetics problem solving, conjecturing, and understanding. Machine-Medi- ated Learning, 2(1&2), 129-159.

Vygotsky, L. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.

West, C., Farmer, J., & Wolff, P. (1991). Instructional design: Implications from cognitive science. Engle- wood Cliffs, NJ: Prentice Hall.

Yackel, E., Cobb, P., Wood, T., Wheatley, G., & Mer- kel, G. (1990). The importance of social interac- tions in children's construction of mathematical knowledge. In T. Cooney (Ed.), 1990 Yearbook of the National Council of Teachers of Mathematics. Res- ton, VA: NCTM.

Yankelovich, N., Haan, B., Meyrowitz, N., & Drucker, S. (1988). Intermedia: The concept and the construction of a seamless information environment. Computer, 21(1), 81-96.



Documents

Emerging Technologies, ISD, and Learning Environments: Critical Perspectives