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Direct instruction approaches, as well as the design processes that support them, have been criticized for failing to reflect contemporary research and theory in teaching, learning, and technology. Learning systems are needed that encourage divergent reasoning, problem solving, and critical thinking. Student-centered learning environments have been touted as a means to support such processes. With the emergence of technology, many barriers to implementing innovative alternatives may be overcome. The purposes of this paper are to review and critically analyze research and theory related to technology-enhanced student-centered learning environments and to identify their foundations and assumptions.
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Instructional Science 25: 167–202, 1997. 167
c1997 Kluwer Academic Publishers. Printed in the Netherlands.
The foundations and assumptions of technology-enhanced
student-centered learning environments
1Learning and Performance Support Laboratory, University of Georgia, U.S.A.;
2Department of Educational Psychology, University of Oklahoma, U.S.A.
Abstract.Directinstruction approaches, aswellas the design processes that supportthem, have
been criticized for failing to reflect contemporary research and theory in teaching, learning,
and technology. Learning systems are needed that encourage divergent reasoning, problem
solving, and critical thinking. Student-centered learning environments have been touted as
a means to support such processes. With the emergence of technology, many barriers to
implementing innovative alternatives may be overcome. The purposes of this paper are to
review and critically analyze research and theory related to technology-enhanced student-
centered learning environments and to identify their foundations and assumptions.
Key words: student-centered learning, learning environments, technology
The pursuit of ideal teaching and learning methods has challengededucators
for centuries. Recent emphases in student-centered approaches have revi-
talized interest in alternative teaching and learning perspectives. The most
closely-studieddifferenceshave been between “traditional”directed-teaching
methods and learner-centered constructivist approaches. Direct methods have
been criticized for failing to emphasize practical problem solving and critical
thinking (e.g., Brown, Collins, & Duguid, 1989; National Science Teachers’
Association, 1993). Some educators haveattributed performance deficiencies
to teaching approaches that cultivate oversimplified, and often superficial,
understanding (Spiro, Feltovich, Jacobson, & Coulson, 1991). Externally-
centered instructional methods,according to critics, fail to address the knowl-
edge requirements of a rapidly expanding technological society.
Several perspectives have emerged among designers of learning systems.
Many believe that instructional design methodologies, themselves, are not
inherently limiting. Limitations in their use, it is argued, result from
narrow interpretation rather thanshortcomings in the approaches themselves
(Reigeluth,1989). Others advocate extendingor adapting conventionaldesign
methodologies to better accommodate diverse perspectives and contempo-
rary research and theory (Lebow, 1993; Rieber, 1992). Still others disagree,
truc127.tex; 5/05/1997; 16:54; v.6; p.1
noting that the assumptions and pedagogy associated with instruction are
incompatible with non-objectivist approaches (Cunningham, 1987; Kember
& Murphy, 1990). Finally, interest has surfaced in “students as designers,”
that is, learning environments that support user-centered construction activity
(Harel & Papert, 1991; Reigeluth, 1996). Interest in and the need for alterna-
tive approaches is apparent; it is not clear, however,how best to support such
Student-centered learning environments have been touted as an alterna-
tive to externally-directed instruction. While, at face value, the potential
of student-centered learning environments is compelling, the logistical
problems associated with implementing them are formidable. Recent
advances in computer and related technologies, however, have facilitated
the management of electronic resources, making student-centered alterna-
tives both possible and feasible. Computer-enhanced learning environments
promote engagement through student-centered [learning] activities”
(Hannafin, 1992, p. 51). Technology-enhanced, student-centered learning
environments organize interrelated learning themes into meaningfulcontexts,
oftenin the form ofa problemtobe solved or an orienting goal, thatbind func-
tionally their features and activities. They provide interactive, complimen-
tary activities that enable individuals to address unique learning interests and
needs, study multiple levels of complexity, and deepen understanding. They
establish conditions thatenrich thinking and learning, and use technologyto
enable flexible methods through which the processes can be supported.
Many technology-enhanced student-centered learning environments have
been developed, ranging from situated, problem-based approaches (e.g.,
Jasper Woodbury Series, Voyage of the Mimi), to microworlds (e.g., Logo,
Project Builder), to specialized manipulationtools (e.g., Geometer’s Sketch-
pad). Research on these environments, while promising, has focused largely
onthepresumed uniqueness of theapproaches.Among constructivists,beliefs
about how to promote understanding vary widely (Phillips, 1995). Design
guidelines and heuristics have occasionally been offered (e.g., Perkins, 1991;
Young, 1993), but they have not stimulated what Glaser (1976) characterized
as a “science of design.” Consequently, apart from isolated studies, compar-
atively little understanding of the role of technology in the design of student-
centered learning environments has evolved.The purposes of this paper are to
provide a brief overview of technology-enhanced,student-centered learning
environments, and to identify the foundations and underlying assumptions
common across student-centered designs.
truc127.tex; 5/05/1997; 16:54; v.6; p.2
The emergence of technology in student-centered learning environments
Interest in environments that immerse individualsin authenticlearning experi-
ences, where the meaning of knowledge and skills are realistically embedded,
has been long standing. John Dewey (1933, 1938), for example, charac-
terized schools as settings in which students received life-apprenticeships.
Piaget (1952) suggested that children innately build and alter understanding
through everyday interactions with their environments; the goalof education,
in effect, is to provide a stimulating environment to supportthe child’s natural
epistemic curiosity. More recently, attempts to situate cognition in authentic
learning-performing tasks have become widespread (e.g., Brown, Collins, &
Duguid, 1989; Cognition and Technology Group at Vanderbilt, 1991, 1992).
Systems are designed not so much to instruct as to provide contexts wherein
understanding and insight can be uniquely cultivated.
In a contemporary sense, Papert’s (1993b) concept of microworlds as
incubators for knowledge” (p. 120) reflects the philosophical biases of
many computer-enhanced, student-centered learning environments. Micro-
worlds nurture individual learning and understanding rather than teach
explicitly (cf. Olson, 1988). They emphasize empowerment through meta-
knowledge which individuals invoke and refine while attempting to make
sense of their environment. Microworlds employ technologies that enable
learners to manipulate complex concepts in tangible, concrete ways. They
emphasize the uniquenessof these processesand the need to discover, predict,
test,reformulate,and constructpersonally-relevantmeaning (Edwards,1995).
Similarly, interest has grown in interactive multimedia environments
that are student-centered. Such systems provide rich databases, tools, and
resources to support self-directed inquiry and information seeking and
retrieval, as well as individual decision making (Land & Hannafin, 1996).
Understanding is assumed to evolve through the processes of exploring,
inquiring, and constructing representations and/or artifacts (see for example,
use of the World Wide Web (Shotsberger, 1996), Perseus (Crane & Mylonas,
1988), and Intermedia (Yankelovich et al., 1988)).
Perspectives on the role of technology in student-centered learning have
expanded, both conceptually and operationally, during recent years (see for
example, APA, 1992). Changes have been reflected in the nature and breadth
ofexperiencesmade availableandin the capacity to supporttheseexperiences
technologically. Learning systems of enormous power and sophistication
have been developed to represent evolving notions of the partnerships among
learners, experience, discourse, and knowledge. Student-centered learning
systemsreflect researchandtheoryranging from situated, contextualteaching
and learning (Brown & Duguid, 1993; Roth & Roychoudhury, 1993) to
resource-based models of education (Reigeluth, 1989).
truc127.tex; 5/05/1997; 16:54; v.6; p.3
Views about learning
Student-centered learning environments evolvedas a result of shifting beliefs
and assumptions about the role of the individual in learning. Contemporary
designers have been influenced heavily by constructivists who assert that
understanding transcends the encoding of literal information and is uniquely
constructed (Guba, 1990; Jonassen, 1991; Phillips, 1995). Knowledge must
be assimilated; perceptions of value, meaning, and importance must be ten-
tatively derived; existing knowledge must be evaluated concurrently with
new knowledge; and understandings must be reconstructed accordingly
(Hannafin, Hill, & Land, in press). In effect, student-centered learning
environments emphasize constructing personal meaning by relating new
knowledge to existing conceptions and understandings; technology promotes
access to resources and tools that facilitate construction.
Recently, researchers have examined how learners evolve understanding
in technology-rich learning environments. Effective environments support
the individual’s intentions to derive and solve problems through the use of
available resources and tools (Edwards, 1995; Jonassen, 1992). The result is
a complex interaction among prior knowledge, perception of events, intents,
actions, observations, and reflections attendant to on-going thoughts and
actions (Land & Hannafin, 1996). Actions, goals, and processes are initiated
as a result of both previous system experiences and intuitive assumptions
about the concepts under study. Learning, then, is a dynamic process of
“reflection-in-action” where action is used to extend thinking, and reflection
is governed by the results of action (Sch¨
on, 1983).
Views about teaching
Several efforts, principally in the sciences and mathematics, have demon-
strated alternative roles for technology in teaching and learning (see, for
example,Cognition and TechnologyGroup at Vanderbilt, 1991,1992;diSessa
& White, 1982; Levin & Waugh, 1987; Roth & Roychoudhury, 1993; Linn
& Muilenburg, 1996; Tobin & Dawson, 1992). The focus has often been on
developing critical thinking, problem solving, and reasoning skills. The over-
arching goals are to encourage manipulation rather than simple acquisition,
and to root the learning process in concrete experience. These systems, it
has been argued, represent fundamentally different views and beliefs about
teaching and the nature of learning, not a simple re-hosting of traditional
The utility of instructional approaches, the means through which tradi-
tional teaching and learning assumptionsare often operationalized, has been
detailed by several theorists who have derived very different inferences (see,
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for example, Winn, 1993). Gagn´
e & Merrill (1990) noted that instruction
must focus on broader, more integrative outcomes than typically assumed,
a theme that has become increasingly popular. Merrill, Li, & Jones (1990a,
1990b) cited the closed nature of traditional approaches, absence of guidance
for interaction design, and limited adaptation as constraints of traditional
models of computer-based learning. They advocated an extension of tradi-
tional models to account for the capabilities of emerging technologies. While
Merrill and his colleagues advocate changes in the systems used to generate
instruction, their underlying assumptions as to the nature of learning remains
consistent with objectivist epistemology.
This view has been challenged. Kember & Murphy (1990) suggestedthat
alternative models, rooted in constructivism,encourage meaningful learning
and allow for pragmatic design and development. Others have advocated
systems that promote diversity of perspective through individual or social
knowledge construction rather than uniformity of interpretation. The Lan-
guage Development and Hypermedia Research Group (1992), for example,
proposed “open software” designed to develop multiple perspectives. Similar
social construction applications of open, student-centered learning principles
have been devised (e.g., Scardamalia et al., 1989; Yankelovich et al., 1988).
Increased interest in student-centered learning has been evident. Yet, the
nature of the systems seems, to many, to be more dissimilar than alike. It is
important to recognize both similarities and differences among technology-
enhanced, student-centered environments.
Rapid developments in technology have influencedthe evolution of student-
centered learning environments (Strommen & Lincoln, 1992). Complex
information systems can now be designed and accessed for individual
purposes with comparative ease (Marchionini, 1988). Emerging informa-
tion systems, such as the World Wide Web, support varied student-centered
approaches in a variety of settings (Shotsberger, 1996).Integrated multimedia
platforms are now commonplace, providing powerful systems for developing
and using highly sophisticatedlearning environments.
Software innovations have also been prominent. Significant advances in
authoring, multimedia development, production tools, simulation software,
and expert system shells have been apparent (see for example, Li & Merrill,
1990). Simplified use has increased interest in classroom applications of
“learning by designing” (see for example, Harel & Papert, 1991; Pea, 1991;
Trollip & Lippert, 1987). Software developments have increased not only the
power and versatility of emerging systems, but have made them increasingly
friendly and intuitive. Individuals can uniquely define the purposes of tech-
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nology’s uses, and exploit its capabilities to support individual interests and
Despite advances in technology, however, comparatively little impact of
any significant scale has been evident. Teaching-learning approaches have
often been re-hosted, not re-defined. Technology has been harnessed to
accomplish conventional aims, but comparatively few applications have
unleashedthepotentialof either the technologiesorlearners.Student-centered
learning environments representsignificant potential for optimizing the capa-
bilities of both technology and learners. Improved understanding of the
foundations and assumptions of such systems is needed.
Foundations of technology-enhanced student-centered learning
Learningenvironments are rooted in fivefoundations:Psychological,pedago-
gical, technological, cultural, and pragmatic. Direct instruction environments
typically draw upon foundations that are consistentwith objectivist, designer-
centered perspective. Student-centered learning environments’ foundations,
on the other hand, reflect a more user-centered view about the nature of
knowledge and the role of the learner. Both are rooted in psychological
foundations, but the approaches differ.
All learning environments, explicitly or tacitly, reflect underlying beliefs
about how knowledge is acquired and used.Psychological foundations reflect
views about how individuals acquire, organize, and deploy knowledge and
skill. Psychological foundations are subsequently operationalized through
various design frameworks, activities, and strategies, which reflect beliefs
about how individuals think, learn, understand, and act.
Historically, learning environments were rooted psychologically in behav-
iorism, with stimulus-response-reinforcement associationism as the core
explanatory learning paradigm. Relevant information was presented, practice
elicited, and specific, contiguous feedback provided (Hannafin et al., 1996;
Hannafin & Rieber, 1989a). Directed drill and practice programs, as well as
convergent tutorial programs, are consistent with behavioral foundations.
Much of the latter-day psychological tradition of learning environments is
derived from cognitive psychology (APA, 1992). Cognitive research focused
on the processes associated with learning, such as selecting and processing
limitations and capacities, organizing stimuli into meaningful units, inte-
grating new with existing knowledge, and retrieving and using knowledge
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and skills (Hooper & Hannafin, 1991). Information-processing theory led to
fundamental shifts from the external, behavioral conditions of learning to the
underlying processes involved in selecting, encoding, and retrieving. Gagn´
(1985), for example, used information-processing theory as a cornerstone of
his conditions – internal and external – of learning. Seminal concepts have
been derived related to the influence of limited short-term memory capacity
(Klatzky, 1975; Miller, 1956), depth of processing (Craik & Lockhart, 1972;
Craik & Tulving, 1975), elaboration (Anderson & Reder, 1979), meaning-
fulness (Mayer, 1984; 1989), and schemata (Anderson, Spiro, & Anderson,
1978). Likewise, individual variables, such as metacognition and perceived
self-efficacy (Salomon, 1979), have enhanced present-day understanding of
the psychological make-up of the learner. Cognitive research has been instru-
mental in shaping views of learning as an internally-mediated process.
Social cognitivists have focused on the relationship between context and
knowledge, emphasizing the socially-mediated aspects of learning (see, for
example, Belmont, 1989; Brown, Collins & Duguid, 1989; Lave & Wenger,
1991) as well as the influence of social context on understanding (Young &
McNeese, 1995). Knowledge, and the contexts in which it derives meaning,
are considered to be inextricably related. Conversely, knowledge isolated
from contexts is of little productive value and is likely to be “inert” (White-
head, 1929). The emphasis on contextually-rich, authentic experience rather
than decontextualizedinformation is a direct outgrowth of these perspectives.
Contemporary interpretations of constructivism evolved from the contri-
butions of Piaget (1952) and Vygotsky (1978), among others. Knowledge,
according to constructivists, is not fixed or external; it is individually
constructed. Thus, understanding is derived through experience. Ideally,
student-centered learning environments emphasize concrete experiences that
serve as catalysts for constructing individual meaning. This premise is central
to the design of many contemporary learning systems.
Technology-enhanced, student-centered learning environments manifest
diverse psychological foundations. For instance, in the Jasper Woodbury
Series (Cognition and Technology Group at Vanderbilt, 1992), situated
learning is the conceptual foundation for embedding knowledge and skills
into a practical, authentic context. Microworlds often draw upon concepts of
conceptual development and mental models to support building and revising
of ongoing beliefs (Edwards, 1995; Rieber, 1992; Twigger et al., 1991). Other
programs use constructivist perspectives to develop critical thinking and
science process skills (Roth & Roychoudhury, 1993; Tobin & Dawson, 1992).
Despiteapparent variations, however,commonpsychologicalfoundationsare
manifested in the roles of technology in supporting activities, features, and
opportunities to support student-initiated, student-directed understanding.
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A host of influences has been extrapolated from psychological research
and theory, including those garnered from traditions as well as those based
in contemporary perspectives. In comparatively few cases, the psychological
foundations of the learning system have been clearly identified (see, for
example, Cognition and Technology Group at Vanderbilt, 1992; Twigger
et al., 1991). Learning systems need to reflect, and be consistent with, the
underlyingpsychological model uponwhich they are based. Student-centered
learning environments emphasizelearners as constructors of knowledge, the
importanceof contextin understanding, and the essentialnatureof experience
in learning.
Pedagogical influences focus on the activities, methods, and structures of the
learning environment; pedagogical foundations emphasize how an environ-
ment is designed and its affordances are made available. In concert with an
underlying psychological model, they provide the basis for the methods and
strategies employedand the ways in which to-be-learned contentis organized.
Pedagogical foundations represent the operational bases for the different
methods and activities generated using varied design models (see, for exam-
ple, Hannafin & Rieber, 1989b). Direct instruction approaches frequently
emphasize instructional strategies such as hierarchical structure of to-be-
learned content, objective-relevant questioning, feedback, and assessment
of progress toward mastery (see for example, Dick & Carey, 1990; Gagn´
Briggs, & Wager, 1988). Typically, these issues concern the influence of
lesson organization and sequence and mathemagenic activities to address
known, externally-defined performance requirements.
In contrast, generative activities such as learning strategy training (Derry
& Murphy, 1986) and learner choice and control (Chung & Reigeluth,1992)
are designed to capitalize on the unique cognitive capabilities of individual
learners. While external structure tends to influencethe success of such strate-
gies, they are designed to empowerthe learner with methods that are widely
applicable across diverse learning tasks.
Clearly, varied assumptions yield different strategies and frameworks.
Objectivists tend to stressthe hierarchical nature of knowledge, and advocate
“bottom-up,” externally-structured approaches to learning. Operationally,
concepts are presented according to stated objectives, mathemagenic strate-
gies are embeddedto ensure the attainment of the objectives, intendedlearn-
ing is assessed, and alternatives are invoked (e.g., repeat, recast, or review
background material) if needed. In contrast, constructivist designers tend
to emphasize exploration among related resources and concrete manipula-
tion (see, for example, Perkins, 1991). In each case, the pedagogical options
truc127.tex; 5/05/1997; 16:54; v.6; p.8
reflect distinctly different underlying assumptions, and draw upon different
pedagogical foundations.
Technology-enhanced, student-centered learning environments establish
The individual must reasonbefore acting, assess whatneeds to be understood,
and identify and executemethods believed helpful. TheScienceVisionSeries,
for example, uses brief orienting scenarios to describe problems confronting
a student team (Tobin & Dawson, 1992). The problems are often systemic
in nature, focusing on topics such as river pollution. Using technological
tools, students navigate, reference on-line resources, conduct experiments,
and collect data in their quest for a solution. They need to reason before
acting, assess their needs, identify and select methods believed helpful, and
reflect on the information selected, encountered, generated, or constructed
(Land & Hannafin, in press).
Technology-enhanced, student-centered learning environments create
contexts within which knowledge and skill are authentically anchored, and
provide a range of tools and resources with which to navigate and manipulate
(Hannafin, Hall, Land, & Hill, 1994). They afford opportunities to seek rather
than to comply, to experiment rather than to accept,to evaluate rather than to
accumulate,and to interpret rather than to adopt. Yet,they may also draw upon
related constructs, such as generative strategies and elaboration. Pedagogical
foundations, therefore, are not confined to methods derived from construc-
tivism, but represent a synthesis of research and theory which establishes
contexts, resources, and tools to promote learning.
Taken independently, technological capabilities suggest what is possible
through advances in technology, not necessarily what is required or desired.
When considered with the other foundations, technological foundations
represent how the capabilities and limitations of available technologies can
be optimized.
Technologies can be distinguished by the operations they support and the
symbol systems they employ. Computers, for instance, utilize printed text,
graphics, sound effects, and animation. They also utilize various aural, visual,
and tactile modalities and options for digital, analog, still, or synthesized
media. Yet, computers also offer capabilities such as data processing and
management that often are unavailable with print or other type of media.
The options can be integrated and manipulated via sophisticated technologies
capable of complex processing and presentation. Technological capabilities
constrainor enhancethetypesoflearner-systemtransactionsthatare possible.
truc127.tex; 5/05/1997; 16:54; v.6; p.9
Technological foundations influence the design of learning systems by
establishing the toolkit available to both the designer andthe learner (see, for
example,Bagley&Hunter,1992).Computerscanmonitor responses,provide
individualized feedbackabout choices, and maintain records of performance.
However, these capabilities exist independently of particular design assump-
tions or decisions; design decisions regulate how, or if, technological capa-
bilities will be utilized.
Technological capabilities dictate not how much learner control is
supported, but how much is possible. They determine not what should be,
but what could be. Furthermore, technological capabilities can be exploited
as tools (e.g., to select text for electronic notebooks, perform calculations,
request additional help) to manipulate objects. Finally, technological capabil-
ities can be used to personalize instruction or to advise learners about useful
processes or information at appropriate times.
Technology-enhanced, student-centered learning environments often facil-
itateunderstandingofabstractconceptsvia concrete experience. For instance,
a thermodynamics environmentallows learners to collect real-time tempera-
tures of various objects, noting changes as they are displayed graphically
(Lewis, Stern, & Linn, 1993). Learners vary parameters such as initial
temperature, surface area, and insulation material. Technological tools, in
this instance, redefine the experiences available to learners and the cognitive
requirements of a learning task.
Technological capabilities may also promote heretofore untested designs
andstrategies.They can redefinewhatispossibleor feasibleandstimulatenew
perspectives on the teaching-learning process. The challenge for designers is
to capitalize on the capabilities of emerging technologies based upon existing
designs, while generating new designs rooted in emerging psychological
and pedagogical research and theory. For such shifts to occur, foundations
related to teaching, learning andtechnology, and the features related to those
foundations, need to be aligned.
Cultural foundations reflect prevailing beliefs about education,the values of a
culture, and the roles of individuals in society. The American school system,
for example, initially accommodated agriculturalcalendars in predominantly
rural areas. The need for vocational and technical training evolved due to
industrialization, with changes in structured “factory models” of school-
ing (Reigeluth, 1989). Likewise, the USSR’s launching of Sputnik in 1960
catalyzedthe UnitedStatestoa nationalagenda to improvescience and mathe-
matics teaching and learning. More recently, the need to meet the knowledge
requirements of our rapidly expanding technological society has emerged.
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Computers are increasingly prevalent in classroomsand educational software
is widely available; schools mirror the values and priorities of an increasingly
technological society.
Cultural foundations influence the design of learning systems by reflect-
ing social mores and values concerning the nature and role of education.
Educational systems in industrialized Asia, for example, place exceptional
emphasis on competition and the acquisitionof rote knowledge. They provide
intensive classroom instruction, administer highly competitive national tests
to determine eligibility for very select colleges and universities, and require
significant commitments by families to ensure the futures of children. The
educational systems are microcosms of the highly competitive and success-
ful industrialized societies which evolved during the latter half of the 20th
century. Many European nations, on the other hand, place substantially less
emphasis on competition and rote learning in favor of reflection and self-
study. Evolution in a given culture’s educational priorities occur because of
the real (or perceived) need to increase, decrease, or shift focus based upon
prevailing attitudes, beliefs, and societal mores.
The same can be said for individual school districts, schools, classrooms,
teachers, instructional units, and classroom modules. Each reflects, in a very
real sense, the philosophy of its parent organization (e.g., school boards,
teachers). This is important in any learning system, but it is of special rele-
vance in the design of learning environments. The culture affecting learning
environments can be traced to groups such as scientists, engineers, corpo-
rations, and advocacy groups. Vocational education, for example, has been
influenced heavily by both projections about future workforce needs and
perceptions of the readiness of current students. Likewise, many scientists
have advocated increased attention to reasoning, critical thinking, and prob-
lemsolvingthatis, learningscienceas a scientistratherthanabodyofformal
knowledge. Consequently, technology-enhanced, student-centered learning
environments have evolved to support philosophical shifts in the nature of
teaching, learning, and technology.
Each setting has unique situational constraints that affect the design of
learning systems. Issues such as run-time requirements, hardware/software
availability and compatibility, and financial concerns establish significant
constraints. Pragmatic foundations bridge the gap betweentheory and reality.
They emphasize the practical reasons a particular approach can or cannot be
used in a given learning environment.
Pragmatics might also dictate that learning environments blend aspects
of varied pedagogical models. For instance, the Space Shuttle Commander
truc127.tex; 5/05/1997; 16:54; v.6; p.11
environment utilizes both manipulation strategies and direct instruction in
recognition of everyday classroom teaching-learning constraints (Rieber,
1992). Similarly, the Cognition and Technology Group at Vanderbilt (1992)
outlined several alternative implementation strategies to accompany the
Jasper series. The strategies range from a “basics first” to open problem-
solving approaches. Such systems are designed to promote usage in ways
that accommodate situational biases and constraints.
In a very real sense,pragmatic foundations dictate what can bein a learning
environment, accounting for both human and technological assets and limi-
tations as well as situational factors. However, not all perceived constraints
are real. Some concerns reflect limited perspectives rather than legitimate
constraints. As technological, psychological, and pedagogical research and
theory continues to advance, designers must develop systems that accommo-
date the real constraints of the learning environment while overcoming those
rooted in narrowness of their perspectives.
An integrated view
Though presented in isolation, the foundations are functionally integrated in
learning system designs. The various manners in which they are manifested
reflect fundamentally different assumptions about the nature of teaching,
learning,knowing,and understanding. As illustrated conceptually in Figure 1,
each foundation should interact to some degree with all others, indicating
mutual interdependence. As foundations become increasingly or decreas-
ingly interdependent,the intersection increases or decreasesaccordingly.The
more complete the coincidence, the better integrated the foundations; the
better integrated the foundations, the greater the probability of success in
the setting for which the learning environment is designed. In practice, the
larger the coincidence among foundations, the better aligned the learning
system’s underlying psychological, pedagogical, technological, cultural, and
pragmatic factors.
Complete alignment is, however, relatively rare. Environments rooted
primarily in a single foundation(e.g., fascination with the use of the Internet
in the classroom) may be limited if they fail to reflect coincidence among
important foundations. Figures 2a–2d reflect a familiar problem encountered
in technology-enhanced, student-centered learning environments. Figures
2a–2b illustrate an environment well-aligned in psychological, pedagogical,
and technological foundations. The psychological framework is consistent
with constructivist-situated cognition perspectives, emphasizing powerful,
authentic learning contexts and student-centeredness.The pedagogical strate-
gies are largely consistent with these foundations, providing problem state-
ments and framing, and a variety of methods such angling and scaffolding.
truc127.tex; 5/05/1997; 16:54; v.6; p.12
Figure 1. A conceptual representation of a balanced, integrated technology-enhanced student-
centered learning environment.
Figure 2a–2d. A conceptual representation of a partially integrated technology-enhanced,
student-centered learning environment.
truc127.tex; 5/05/1997; 16:54; v.6; p.13
Technologicaltools for manipulationand a host of highly indexed multimedia
resources are provided,perhaps via the World-Wide Web, again in ways that
support the environment’s pedagogical and psychological foundations.
When cultural influences are considered, however, substantial inconsis-
tencies are indicated with the beliefs and priorities of those implementing
formal education (workplace environment, school, classroom, etc.) (see Fig-
ure 2c). In this case, the school, teachers, and community may haveadopted a
basics-first approach, and employed a mastery-based curriculum. The given
culture values direct, structured teaching and learning features not consis-
tent with the preceding foundations. Figure 2d suggests that pragmatic fac-
tors also mitigate against the student-centered approach (though pragmatic
and cultural foundations are highly aligned). Perhaps the school day is not
amenable to needed re-scheduling, financial resources have been exhausted,
or there is a limited capacity for additional activities in an already busy school
day. This is commonplace where technology resources are insufficient to
support sophisticated technologies (e.g., limited access to high-speed access
to the World-Wide Web, few computers). The available resources have been
dedicated to hiring personnel and purchasing supplementary materials to
reinforce and focus teaching and learning. An innovative learning system has
been developed, but it simply does not fit the environment for which it was
Assumptions of technology-enhanced student-centered learning
In theory, all learning environments draw upon each root foundation. The
conceptual overlap among foundations may be extensive, but still reflect
fundamentally different requirements. As noted previously, an environment
rooted in objectivist epistemology, such as those in highly focused technical
training, may involve strong alignment among behavioral theory, mathema-
genic learning strategies, and highly directed use of technology features. An
open learning systemmay yield the same degree of conceptualalignment, but
draw upon different subsets of the foundations (e.g., learning as construction,
manipulation tools, and navigationbrowsers).
Underlying assumptions determine, in unambiguous ways, how (or if) the
foundations are connected.The importance of underlying assumptions,there-
fore, cannot be overstated: they dictate how foundations are operationalized
in any environment. As the assumptions vary, the foundations, and hence the
features and methods, of the learning environment change accordingly.
Technology-enhanced student-centered learning environments comprise
many forms, often with few apparent similarities. The efforts are often very
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dissimilar in functions, goals, and features, making it difficult to identify
overarching design principles. Despite such variations, common assump-
tions are manifested either explicitly or implicitly within the environment.
Table 1 summarizes the assumptions, with supporting examples, functions,
and research.
Instruction, traditionally operationalized,is too narrow to support varied
learning requirements
Direct instruction is inherently neither good nor bad; it is very effective in
promoting particular kinds of learning and problematic for others (Hannafin,
1992). Dick (1991) described instruction as “ an educational intervention
that is driven by specific outcome objectives and assessments that deter-
mine if the desired changes in behavior [learning] have occurred” (p. 44).
To the extent that learning or performance outcomes are explicitly known,
explicit procedures must be learned, or efficiency in acquisition is valued,
direct instruction provides a powerful methodology. Instruction, and the
processes of traditional design, emphasize prescribed instructional objec-
tives, congruence among objectives, methods, and performance standards,
hierarchical analysis of to-be-learned lesson content, and convergent,
externally-prescribed instructional activities. Bottom-up” approaches inher-
entlyemphasizetheimportanceof formal prerequisiteknowledge and specific
content outcomes. Instruction is directive in nature, tending to be concerned
more with what performance is elicited than how it is derived (Trollip &
Lippert, 1987).
Much of what should be learned, however, need not be taught directly;
indeed, some cannot be taught directly. The emphasis on the hierarchical
natureof prerequisites,forexample,maylimitthepotentialfor novice learners
to engage complex, formal ideas. Papert (1993b), for example, noted that
even very young children can encounter advanced ideas such as differen-
tial equations and Newtonian physics through use of a transitional, enabling
system that exists in the child’s world. Everyday phenomena and experi-
ences provide concrete instancesof otherwise abstract concepts. The learning
potential achieved through collaboration between the environment and the
learner is represented theoretically as Vygotsky’s (1978) “zone of proximal
development.” Within this zone, the learner interacts in ways not possi-
ble independent of the environment (Belmont, 1989; Salomon, Globerson,
& Guterman, 1989). Others have noted that children’s learning capabilities
have been underestimated (Novak & Musonda,1991; Roth & Roychoudhury,
1993). Understanding, it is argued, is neither inherently hierarchical nor
the product of incremental teaching methods, but a natural consequence of
curiosity, experience, reflection, insight, and personal construction.
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Table 1. Examples, functions, and supporting research for the assumptions of student-centered learning environments.
Assumption Examples of methods & Activities Functions Associated research & theory
Instruction, Basics first vs. initial exploration of Allows learners to “make sense” out of Hierarchical, “bottom-up” approaches
traditionally complex concepts what they know; engages them in (Dick & Carey, 1985) vs. anchored
operationalized, complex ideas instruction (CTGV, 1990)
is too narrow Decontextualized instruction vs. Supports meta-knowledge about Strategy training (Derry & Murphy, 1986)
to support contextualized learning problem solving; addresses complex vs. cognitive apprenticeships (Brown,
varied learning thinking vs. rote memory & Collins, & Duguid, 1989)
requirements disassociation problem
Direct instruction vs. exploration and Leads to deeper understandings and External conditions of learning (Gagn´
manipulation personal model building and refinement 1985) vs. model building and
reconstruction (Piaget, 1986; Papert,
Presentation of facts vs. cultivation of Increase meaningful understandings Behaviorism vs. mathetics (Papert,
individual sense-making and relationships with phenomena 1993a; 1993b) and reflexivity
(Cunningham, 1987)
Understanding Technology-enhanced automation of Allows novices to get familiar with Distributed intelligence (Pea, 1993);
is best selected processes complex notions without excessive Effects “of” technology (Salomon,
supported cognitive load; supports conceptual Perkins & Globerson, 1991)
when cognitive manipulation
processes are Learner-generated predictions, model Facilitates building and evolving of Mental model building (Mayer, 1989;
augmented, building, & testing theories or beliefs Rieber, 1992); theories-in-action
not (Karmiloff-Smith & Inhelder, 1975)
supplanted, by Socially, materially, & technologically Leads to deeper understanding; Phenomenaria (Perkins, 1991); cognitive
technology rich environments understanding surpasses what could apprenticeships (Brown, Collins &
be achieved without support Duguid, 1989)
Cognitive tools Empowers learners to extend thinking Cognitive tools (Kozma, 1987);
and process higher-order concepts. mindfulness (Salomon, 1986).
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Table 1. Continued.
Assumption Examples of methods & Activities Functions Associated research & theory
Learning Support individual sense-making Increases meaningful learning and Mathetics (Papert, 1993a; 1993b)
environments connections among ideas
need to Process-oriented resources Promotes cognitive engagement and Process learning (Hannafin & Grumelli,
support the development 1993); phenomenaria (Perkins, 1991)
underlying Support making cognitive/ Supports learning of self-regulation metacognition: covert processes made
cognitive metacognitive processes overt skills as learners become aware of overt (Scardamalia et. al, 1989);
processes, strategies; Supports development executive control (Perkins, 1993)
not solely of meta-knowledge
products of
Understanding Learner-generated predictions, model Supports learners in formulating Knowledge reconstruction; assimilation-
evolves building, testing, and revising intuitions or mental models accommodation (Land & Hannafin,
continuously 1996)
Experiments, manipulations, Understanding is refined through “incubators of knowledge” (Papert,
simulations and microworlds experience 1993a; 1993b; Edwards, 1995)
Concept mapping, generative Addresses compliance vs. evaluation Intentional learning (Scardamalia et. al,
learning strategies issue 1989); model building/enhancing
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Table 1. Continued.
Assumption Examples of methods & Activities Functions Associated research & theory
Individuals Encourage awareness of learners Encourages a richer understanding of Reflexivity (Cunningham, 1987);
must assume personal knowledge construction beliefs; gives learners control over mathetics (Papert, 1993a; 1993b)
greater process learning process
responsibility Emphasize making metacognition Supports learning of self-regulation Strategytraining(Derry&Murphy,1986);
for their overt and deployment/use of skills as learners become aware of intentional learning (Scardamalia et. al,
learning metacognitive skills strategies 1989)
Emphasize construction of products Supports active learning and individual Constructionism (Harel & Papert, 1991)
to represent understanding (e.g., construction of knowledge; more
programming and multimedia motivating than being passive recipient
environments, etc.)
Learners Expand finite set of variables; reduce Supports development of learner’s Variable stepping (Rieber, 1992)
make, or can or expand complexities adaptively “need to know” more information (self
be guided to regulation)
make, effective Establish problem to solve and Establishes an “anchor” upon which Anchoring (CTGV, 1992); problem-based
choices provide supporting resources as further complexities can be added environments (Tobin & Dawson, 1992)
“need to know”
Learner-generated predictions, model Learners see errors as a cue for further model building in microworlds (Edwards,
building, & testing; experimentation information in the natural process of 1995; Rieber, 1992)
with immediate feedback about working towards a goal
results (microworlds; simulations)
Expert commentaries/feedback Learners can check their own ideas Amplification of relevance/expert
with that of an expert (as a part of processes for self-monitoring of learning
self-monitoring) (Spiro et. al, 1991; Thurber et al., 1991)
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Table 1. Continued.
Assumption Examples of methods & Activities Functions Associated research & theory
Learners Activities supporting multiple Diminishes over-simplification problem; Reflexivity (Language Development and
perform best knowledge representations and supports flexible, decontextualized Hypermedia Research Group, 1992);
when perspectives knowledge that can be applied outside Cognitive Flexibility (Spiro et. al, 1991)
varied/multiple of a particular context
representations Activities supporting varied Supports more complex and multi- Analogs and extensions (CTGV, 1992);
are supported contexts/cases faceted understanding Criss-crossed landscape (Spiro et al.,
Activities supporting multiple and Addresses complex learning goals issue Constructionism (Harel & Papert, 1991)
varied purposes of knowledge
Knowledge Situate learning in context of a De-emphasizes misconceptions and Anchoring (CTGV, 1992); Situated
is most problem to be solved; Embed passivity due to disassociated learning knowledge (Brown, Collins & Duguid,
meaningful data/supporting resources into 1989)
when rooted problem solving scenario
in relevant, Simulate the natural, situated process Orients learners to interrelatedness of Everyday cognition (Lave & Wenger,
scaffolded of learning knowledge; learners use knowledge as 1991)
contexts a “tool”
Root learning in concrete contexts “Inert” knowledge problem is addressed Concrete experiences (Wilensky, 1991)
Understanding Technologies or environments for Normally abstract notions can be Concrete manipulation (Edwards, 1995;
is most relevant making abstract notions concretely experienced, manipulated, scrutinized Papert, 1993a; 1993b; Rieber, 1992)
when rooted accessible
in personal Provide multiple experiences for Richer understanding develops from Phenomenaria (Perkins, 1991); concrete
experience exploring concepts, and building learning from experience experiences (Wilensky, 1991);
connections affordances (Pea, 1993)
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Table 1. Continued.
Assumption Examples of methods & Activities Functions Associated research & theory
Reality is Theory building/enhancing Learners formulate and modify initial Knowledge reconstructing (Piaget,
personally understanding 1952); theory development (Land &
constructed via Hannafin, 1996)
and Support natural consequences of Errors are useful as data for refining Model/intuition building (Papert, 1993a)
negotiation experimentation (i.e., errors) understanding; lead to persistence in
the face of problems.
Understanding Model building and testing Cultivate rather than provide Microworlds as “incubators for
requires time understanding knowledge” (Papert, 1993a)
Immerse learners in problems – Deeper understanding through “getting Generative models and environments
provide experiences for to know” phenomena; formulate and (Land & Hannafin, 1996; Linn &
extended investigation and develop personal understanding and Muilenburg, 1996; Papert, 1993a;
concept manipulation decisions 1993b)
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Others have expressed concern regarding the limited capacity of traditional
methodstosupporthigher-order,complex thinking(Kember&Murphy,1990;
Papert, 1993a, 1993b). Indeed, some have argued that traditional instruction
may engender rigid, oversimplified, knowledge which hinders subsequent
learning (Spiro & Jengh, 1990). Highly structured, algorithmic approaches
work well for teaching information and skills that are compatible with behav-
ioral and information processing models about learning, but fail to address
the complex linkages required for thinking critically and solving problems –
key components of contemporary theory (see Bereiter, 1991).
In technology-enhanced student-centered learning environments, a deeper
understanding of cognitive requirements and associated learning tasks
is necessary (Hannafin, 1989). These environments focus on cultivating
problem-solving skills in authentic contexts, promoting flexible knowledge
and thinking skills (Spiro et al., 1991), as well as understanding of multi-
ple perspectives (Language Development and Hypermedia Research Group,
1992). Learning is best achieved through extended investigation and experi-
ence with phenomena under study. These experiences may be confined to a
fixed set of parameters (e.g., microworlds) or be open-ended and constructed
by the learner (e.g., programming endeavors,open software).
The goal, then, is to bring learners into contact with richly supported
experiences, wherein they can deploy diverse, personalknowledge and tools
with which to think. Learners are not only at the center of the environment;
they are integral to it. Universal outcomes, activities, and assessments often
cannot be established a-priori, but must be derived through the efforts of
individuals. Student-centered learning environments afford opportunities, but
do not impose explicit conditions, for learning.
Understanding is best supported when cognitive processes are augmented,
not supplanted, by technology
The unique potential of the learning environment is realized in the extent
to which it supports or alters cognitive processes. A successful environment
encourages learners to use its resources and tools to process more deeply and
extend thinking (Jonassen, 1996; Jonassen & Reeves, 1996; Kozma, 1987).
Learners use system features to derive problems, vary solutions, and expand
the boundaries of their understanding. On the other hand, environments may
supplant important cognitive processing by assuming the processing require-
mentsofintegraloperations.Additionally, whiletoolsor resourcesmay afford
an opportunity for cognitive processing, they may not be used “mindfully”
(Salomon, 1986) by thelearner to extend thinking or understanding.
Salomon, Perkins, & Globerson (1991) differentiate cognitive effects with
and effects of technology. Effects with technology comprise intellectual
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endeavors that occur only as a result of the learner-technology partnership.
Technology, in effect, supports individuals by performing complex tasks such
as generating answers via a spreadsheet. In this case, technology provides
surrogate intelligence which can be used to accomplish tasks not possible
individually. Effects of technology yield cognitive “residues” as a result of
the learner-technology partnership, which changes the way learners think or
enhances their understanding. By eliminating non-essential computational
requirements, for example, learners can predict the effects of varying values
for one or more spreadsheet variable, test their predictions, and revise their
understanding accordingly. Cognitive resources can be re-directed, allow-
ing the learner to think in ways not generally possible in the absence of
the supporting tools. In both situations, learners are typically incapable of
generating answers without considerable effort; in the latter case, however,
the partnership does more than provide an answer – it provides an incubator
for thinking which deepens understanding of the underlying processes.
Learning environments need to support underlying cognitive processes, not
solely the products,of understanding
e (1985) has been instrumental in establishing the interdependence
between thought and action, emphasizing internal and external conditions for
given learning outcomes. According to Gagn´
e, once the internal and external
conditions necessary for learning have been identified (i.e., defined knowl-
edge, skills, or attitudes), activities can be structured to induce the required
processes. The engineering of external conditions is believed to activate the
internal processes needed for effective learning.
Many, however, have challenged the separation of content and process
(see, for example, Brown, 1985; Brown, Collins, & Duguid, 1989; Piaget,
1952). The separation simplifies the task of designing a learning system, but
often fails to induce important processes. When problem-solving skills are
broken down and taught via directed learning approaches, personal insight
and understanding of the problem-solving processitself often fails to develop
(Hannafin & Grumelli, 1993).
Student-centered learning environments utilize process-oriented resources
– activities that promote cognitive engagement and knowledge reconstruc-
tion.CSILE,for example, employscomputertechnology tofacilitate the trans-
formation of covert cognitive processes into overt procedures and artifacts
(Scardamalia et al., 1989). CSILE allows learners to generate connections
between new and existing knowledge and to continuously reconstruct under-
standing.Onceovert,learnersbecome increasinglyconsciousofthe processes
and adapt their thinking accordingly.
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Technology-enhanced student-centered learning environments emphasize
processes to a greater extent than do traditional approaches. They support
decision-making, problem-solving, manipulating, interpreting, hypothesiz-
ing,and experimenting(Roth & Roychoudhury,1993).Papert(1993a,1993b)
coined the term “mathetics” to describe the processesand stages invoked by
individuals in their quest to know or understand. Individuals, as the princi-
ple arbiters of their learning processes, evolve unique sense-making methods
to interpret their environment. To support and cultivate mathetics, learners
need to be empowered through their own strategies or executive functions,
not forced to adapt their own methods to accommodate, or supplant their
methods with, those provided externally (Perkins, 1993).
Understanding evolves continuously
Objectivists assert that knowledge is an external entity that, once identified,
can be organized optimally and disseminated to learners (see, for example,
Dick & Carey, 1990; Gagn´
e, Briggs, & Wager, 1988). These beliefs contrast
with constructivist views of knowledge and understanding, where learning
is a complex, dynamic process through which understanding is continuously
facilitated (Hannafin, 1995). Even young children formulate models, though
unsophisticated or naive,of their world (Piaget, 1952).
Understanding involves continually modifying, updating, and assimilating
newwithexisting knowledge. It requiresevaluation,notsimplyaccumulation.
Understanding results from the testing of theories-in-action, and the efforts
of learners to reconcile their beliefs in the face of ever-changing experience
(Karmiloff-Smith & Inhelder,1975). Thus, “telling” correctanswers or proce-
dures may short-circuit learning, since learners often fail to identify, examine
critically,develop, or evaluate theirintuitive notions aboutthe world. Intuitive
theories-in-action have proven very resistant to change – even when tools and
resources for revising naive understanding are available and deployed (Land
& Hannafin, in press). Learners accept as valid the accounts and explanations
of teachers and textbooks, becoming compliant rather than evaluative in their
thinking processes (McCaslin & Good, 1992).
Student-centeredlearning environments capitalize on the dynamic nature of
knowledge by providing means for developing, testing, and refining personal
theories. Rather than an end-product of instruction, original understanding
exists as an “anchor” from which subsequent insights are derived (Linn &
Muilenburg, 1996). It is constantly modified and refined as a result of succes-
sive experiences and reflections. Several technology-based science simula-
tions (see, for example, Driver & Scanlon, 1988; Lewis et al., 1993; Rieber,
1992) as well as mathematics environments (see, for example, Edwards,
1995; Schwartz & Yerushalmy, 1987) nurture the learner’s intentional model
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building and reconstruction.Learners design experiments, predict results, test
and revise predictions, and revise both beliefs and strategies based upon their
evolving understanding. The dynamic nature of understanding, then, requires
that it be acted and reflected upon and constantly refined (Karmiloff-Smith &
Inhelder, 1975; Sch¨
on, 1983).
Individuals must assume greater responsibility for their learning
While numerous positive results have been reported, negative consequences –
both cognitive and affective – may also result from dependence on externally-
driven teaching and learning. Attribution theorists have speculated that
external control tends to minimize the personal investment and responsi-
bility individuals feel for their learning. This was a driving force behind
the early movement for increased learner control of computer-based instruc-
tion (Hannafin & Rieber, 1989a). Since they have little control over what
is taught or how it is taught, many learners fail to assume responsibility for
their learning, pointing to problems with teachers or materials. Presumably,
given the opportunity to make their own choices, individuals evolve greater
responsibility for their learning.
Evidencealsoexiststo suggest that learners become increasinglycompliant
in their thinking (McCaslin & Good, 1992). They tend to view the learning
task as matching the expectations of external agents rather than pursuing
personal understanding. As perceptions are reinforced through external
evidence of success (e.g., test scores), the cycle strengthens and perpetu-
ates. Learners attempt to model external views of importance rather than
evaluate their own needs and pursue understanding accordingly.
Technology-enhanced student-centered learning environments require that
individuals are active in the learningprocess. They emphasize notonly assim-
ilation but the development of meta-knowledge for both solving existing
problems and generating new ones. Through experience, learners become
increasingly facile with available tools and resources, and skilled in assess-
ing how and when to employ them. Learning environments often utilize
activities that aid learners in constructing and generating artifacts of their
understanding. [See, for instance, open-ended programming (Harel & Papert,
1991), evaluative, interactive essays (Thurber, Macy, & Pope, 1991), multi-
media projects (Pea, 1991), and personally relevant health goals (Lebow &
Johnson, 1993)].
Increased responsibility may also encourage awareness of the knowledge
construction process. Cunningham (1987) refers to this as reflexivity and
suggests activities that encourage learners to evolve a richer understanding
of their beliefs. Influencing learners to become active in inducing mental
activities enables them to reproduce thought processes intentionally without
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explicit external prompting (Scardamalia et al., 1989). Technology-enhanced
student-centered environments require greater individual direction, but they
may also promote self-sufficiency.
Yet, there remain many instances where externally-facilitated approaches
are necessary and appropriate. As noted previously, learners often lack core
knowledge or skills, such as word recognition or simple arithmetic, needed
to engage in complex reasoning. When not effectively scaffolded, learners’
executive functions may be best facilitated directly by external agents: the
teacher designsand presents the instruction; the workbook provides exercises
and questions; the computer presents tutorial information and confirms lean-
ing mastery. Many learners cannot effectively engage higher-order tasks until
they acquire sufficient background knowledge or skill. In such instances,
conventionaldirected learning approaches support the automization of impor-
tant foundation knowledge and skills (cf. Perkins, 1993).
Learners make, or can be guided to make, effective choices
Constructivists stress that learners determine what, when, and how learning
will occur. Such methods, however, tacitly presume that students possess the
metacognitive skills needed to make effective judgments, or can be induced
to make appropriate choices using advice or hints (Hannafin, Hill, & Land, in
press). Yet, ineffective strategy monitoring and usage have been observed in
numerous technology-based learner control studies (Steinberg, 1977; 1989).
These are serious concerns since learning environments rely heavily on the
quality of individual learner decisions for their success.
Technology-enhanced student-centered systems rely on the learner to
generate and implement individual learning plans. Judgments may be based
ontheindividual’sassessments of learning needs or those which, though made
by the learner, are guided by the system. Guidanceis provided in the form of
tools, resources, and, if needed, direct instruction. Individuals can be guided
during learning if designs are situated in authentically complex contexts
and proper guidance is provided (Pea, 1993; White & Horwitz, 1987). For
instance, athletes such as gymnasts or dancers are often “spotted” when they
learn new routines. Performance is initially facilitated to enable actions which
they are unable to produce independently.Successful learners, like success-
ful athletes, gradually use facilitated action to perceive critical elements of
the process and build upon them until they are able to perform indepen-
dently; guidance is reduced and eventually eliminated as familiarity and
facility increase. Student-centered environments use technological resources
to facilitate the perception of critical processes and to provide experiences
that approximate the requirements of an activity (Choi & Hannafin, 1995).
With proper guidance, learners can understandin ways otherwise impossible.
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Others have designed broad frameworks within which students specify a
problem to be solved or goal to be pursued (see for example, the Jasper
Woodbury series, Cognition and Technology Group at Vanderbilt, 1992).
Within anchored contexts, students are provided the resources necessary to
generate and solve problems. Thus, as students define a need to know, they
access information from menus, books, peers, instructors, or other resources
to locate information deemed helpful. Generic thought and probe questions
are often available to consider, but they augment the thinking of individuals,
and do not provide explicit solutions.
Guidance can also be solicited as a natural consequence of manipulation
and experimentation. These features support learners in working toward a
goal, exploring and testing relationships, and evaluating results. For example,
failure to attain one’s goal often provides the basis for seeking additional
information. As learners recognize further needs to know, they begin to
acquire the insight essential for understanding to occur (Bransford et al.,
Guidance is often essential,however, for learners to reflect upon thoughts,
intentions, and system feedback as they work within the environment.
Evidence suggests that learners may not easily recognize limitations in their
theories or beliefs without concrete opportunities to evaluate them (Land &
Hannafin, in press). Determining the implicit cognitive triggering mecha-
nisms – the events likely to cause learners to seek or invoke the appropriate
support – is essential to effective design.
Learners perform best when varied/multiple representations are supported
e & Merrill (1990) noted that conceptually-complex learning goals
are often problematic in traditional instruction. Complex goals are quali-
tatively different from rule-basedlearning and objectives-basedinstructional
goals. Spiro et al. (1991), for example, noted the need for multiple contexts,
purposes, and resources in order to construct knowledge for individual
purposes. Complex concepts, due to their ill-structured and highly condi-
tional nature, require multiple representations.
Technology-enhanced student-centered learning environments strive to
support the individual’s efforts to both organize and represent knowledge.
Learners are assisted in connecting relevant knowledge via varied represen-
tations and opportunities to interact with, and construct meaningful relation-
ships among, the phenomenon under study. Bubble Dialogue (Language
Development and Hypermedia Research Group, 1992), for example, allows
learners to manipulate thoughts and ideas through unstructured internal and
external dialog, encouraging sharing of viewpoints and perspectives across
users. Similarly, Harel & Papert (1991) examined how fifth graders adapt
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multiple perspectives and purposes (e.g., programmer, teacher, learner;
understandings of fraction use. Citizen Kane (Spiro et al., 1991) utilizes
random access (hypertext) principles to support the learner in criss-crossing
contexts and case irregularities to promote understanding. Varied methods
and activities help to promote deep, flexible understanding.
Knowledge is most meaningful when rooted in relevant,scaffolded contexts
Instructional methodstypically proceed from concrete to abstract as quickly
as developmentally possible. Abstract knowledge is considered desirable,
since it is presumed to be context-independent and transferable. Recently,
some researchers and theorists have questioned this emphasis. Compelling
evidence contraindicating such practices have been reported in studies of
science and mathematics misunderstandings (diSessa, 1982; Lee, Eichinger,
Anderson, Berkheimer, & Blakeslee, 1993; McDermott, 1984; Perkins &
Simmons, 1988). Although able to apply abstract rules in textbook-likeprob-
lems, fundamental learner misconceptions often surface for non-standard
problems. Many researchers believe that to-be-learned knowledge and its
associated cognitive processes cannot be separated from their concrete refer-
ents (Perkins & Salomon, 1989). Concrete contexts allow learners to evolve
understand through use.
Technology-enhanced student-centered learning environments are
designed to facilitate the unique construction of understanding. However,
understanding is not viewed as solely an internal process occurring “ as a
property of the minds of individuals” (Pea, 1993, p. 47). Understanding is a
goal-directed activity that, although ultimately internalized, takes place with-
in an “ influential and responsive social context”(Belmont, 1989, p. 142).
As such, the role of the environment cannot be separated from understanding;
it is profoundly influenced by contextswhich frame learning processes.
In technology-enhanced student-centered learning environments, the
processes associated with understanding and the contexts in which it occurs
are inextricablytied. Rather than isolating information, it is embeddedin con-
texts wherein knowledge and skills reside naturally. Science Vision (Tobin &
Dawson, 1992) immerses learners in problem contexts that promote learning
activities that are consonant with practices of experts. Students learn about
physics by designing a virtual roller coaster; they study ecology and chem-
istry by resolving problems related to polluted water. The environment is
considered authentic because it induces participation in realistic activities
and science processes of the scientific community. Information anchored in
relevantcontexts enables learnerstoexploreas ascientistto reveal why,when,
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and how knowledge is used (Cognition and TechnologyGroup at Vanderbilt,
Traditionally, learning has been viewed as a process internal to, and
possessed by, the learner. Recently attention has emphasized the social nature
of cognition (Bandura, 1982; Brown et al., 1989; Lave & Wenger, 1991;
Perkins, 1993). Cognition, it is proposed, is not possessed solely by learners;
instead, it is constructed and acted upon as a shared enterprise (Belmont,
1989). From a social cognition perspective, thinking is viewed as the basis
of activity that requires an interpersonal context to develop. As such, learn-
ing can be analyzed by examining interactions of learners, individually and
collectively, within that context.
The shared, social construction of understanding underlies the design of
manystudent-centeredenvironments.Many approacheshaveutilized teacher-
student or student-student interactions to model or scaffold reflection and
performance (see for example, Palincsar & Brown, 1984; Scardamalia &
Bereiter, 1985). In such environments, teachers coachand “ model expert
strategiesin aproblemcontextshared directlyand immediately with students”
(Collins, Brown, & Newman, 1989, p. 463). Scaffolding is provided in the
skills and processes necessary to carry out a thought, task, oroperation. Over
time, the teacher’s involvement is gradually faded and the responsibility for
learning is increasingly assumed by the learners. Such interactions effect
the sharing of sense-making processes and progressive refinement of ideas.
In subsequent interactions, students assume the dual roles of producers and
critics (Collins et al., 1989).
Scaffolding, however, is not limited solely to student-student and
teacher-student interactions. Rather, technology-enhanced environments
often provide the conceptual scaffolding and means (resources, tools) to
promote personal and individual reflection. In this sense, technological tools
provide models, opportunity for higher level thinking, and metacogni-
tive guidance in a learner’s zone of proximal development” (Salomon,
Globerson, & Guterman, 1989, p. 620). The features of a learning envi-
ronment (tools, resources, people, designs) profoundly shape, direct, and
constrain how learners think. They enable occasions – often transitional in
nature – during which rich learning opportunities are created. Learners may
think or understand in ways that would normally be impossible without
technology-supportedscaffolding (Salomon et al., 1991).
Technology-enhanced student-centered environments provide the opportu-
nitytoengagehigher-orderprocesses,teston-goinghypotheses,and construct
andrevisephysicalmodels of concepts under study.In combinationwith guid-
ance and facilitation of the learning process, technology can serve as a “more
capablepeer” (Salomon et al., 1989, p. 621). CSILE (Scardamaliaet al., 1989)
truc127.tex; 5/05/1997; 16:54; v.6; p.28
provides a series of organizing prompts that help students to internalize new
thinking processes and reduce the accompanying processing load associated
with the cognitive task (Salomon et al., 1991). The social context for student-
centered learning may include human and/or technological partnerships.
Understanding is most relevant when rooted in personal experience
In many teaching and learning settings, information is extracted from its
native contexts to simplify the learning task and avoid potential confounding
from extraneous, and presumably unnecessary, information. Information is
structured and presented so that individuals can learn about important infor-
mation. Yet, evidence suggests that children often fail to connect abstract,
formal notions since the concepts are not physically accessible to them.
Learning is more concrete and meaningful as more personal connections
are made among ideas, contexts, perspectives, and the models that represent
them (Wilensky, 1991). It appears that simply supplying conceptual informa-
tion to learners and allowing them to practice does not necessarily deepen
understanding of concepts.
Contemporary researchers and theorists suggest that understandingis facil-
itated when derived from rich, hands-on experience (APA, 1992; Linn &
Muilenburg, 1996; National Science Teachers’ Association, 1993; Perkins,
1991). Experience enables the learner to reshape and revise ongoing theories-
in-actionbased upon personalsense-makingefforts.Inthe practice of science,
for example, scientists espouse notions such as “getting to know” an idea,
exploring a body of knowledge, and becoming sensitive to distinctions in
the learning process (Papert, 1993b). Scientists emphasize the importance of
encountering objects or concepts under study – experiencing them – rather
than being told about them.
Technology enables learners to become immersed in concrete experiences
– or phenomenaria (Perkins, 1991) – rather than presenting information about
them. Learners derive not only formal understandings from their experience,
but deeper insights into the subtleties of the concepts under study. Opportu-
nities are provided for students to vary conditions or parameters, increase or
decrease complexity, and manipulate normally abstract concepts in tangible
ways. Activities maximize the learning experience by presenting phenomena
in ways that make them amenable to both scrutiny and manipulation, such as
the manipulation of Newtonian motion concepts (e.g., Rieber, 1992; White
& Horwitz, 1987). Learning environments provide contexts that are rich in
experience, knowledge, and opportunity potential.
truc127.tex; 5/05/1997; 16:54; v.6; p.29
Reality is personally constructed via interpretation and negotiation
Perkins & Simmons (1988) reported that, following traditional mathematical
instruction, learners retained fundamentally naivebeliefs despite the ability to
readily apply formal knowledge to solve textbook problems. When problems
failedto matchprototypicaltextbookstructures, naive and incomplete percep-
tions surfaced. Formal knowledge was over-generalized and little attention
focused on whether solutions were reasonable or possible. Learners acquired
formal knowledge butfailed to understand.
Content-driven instructional approaches may hamper deep understanding
infavorofbreadthof coverage, or limit perseveranceforthesakeof efficiency.
Traditional science labs, even with their focus on hands-on-experience, often
emphasize finding a definitive answer or illustrating a particular scientific
truth. Although such methods may help students concretize some concepts,
they essentially tell students why specific scientific phenomenon occur and
reinforce canonical truth. Piaget (1952) noted that understanding is rooted
in initial intuitions or models and evolves as learners correct, reinforce, or
differentiateinitial notions.After preconceptions areidentified, conveyed,and
adhered to in the face of conflicting experiences, learners begin to question
the limitations of their models and formulate new, integrated structures (see
also Ackermann, 1991).
building and theory-enhancing. Although personal theories or models may
be incomplete initially, learners become immersed in experiences that allow
themtoidentify,makeandtestpredictions,andmodifytheir intuitions.Errors,
discrepancies, and misconceptions provide the basis for refining understand-
ing. Learners formulate initial and often “flawed” beliefs, but subsequently
evolve understandings through manipulation and experimentation. These
beliefs help to establish assumptions that can be subsequently tested. Impos-
ing canonical beliefs, without allowing opportunities for learners to create
and test their own, may short-circuit the opportunity to reconcile intuitions
with instruction.
Understanding requires time
Understanding is cultivated via methods that emphasize investigation and
exploration. By spending time immersed in a problem, learners encounter
intricaciesand subtletiesthatareofinterest,enablingthemtoexplorea domain
inrich,meaningful ways (Papert,1993a).They progress fromsimplyknowing
to understanding. They can better discover what they need to know if their
efforts are supported socially, materially, and technologically. Thus, “playing
with problems” – exploring, predicting, manipulating, and testing – leads to
truc127.tex; 5/05/1997; 16:54; v.6; p.30
qualitatively different learning goals. These goals build upon experience and
intuition, which requires immersion.
Student-centered learning environments assume that understanding is
refined over time as a result of rich, concrete technology-facilitated inter-
actions. Microworlds embody this assumption as they promote manipulation,
model building, and revision of beliefs. Exploration leads to ideas about how
or why a particular rule, equation, or concept exists. Pea (1993) noted that
individuals must be introduced to, and participate in, activities that suggest
or reveal underlying meaning to abstractions (such as an equation or notation
system).Withoutextended exploration,understanding is oftenincompleteand
rigid. Conceptual understanding becomes impoverished, inherently limiting
learners in its utility. Understanding, and the insights derived through efforts
to understand, requires sustained engagement in learner-centered activities.
Technology-enhanced student-centered learning environments are not simply
dichotomous alternatives to direct instruction; they represent alternative
approaches for fundamentallydifferent learning goals. Any learning environ-
ment is ultimately shaped by its foundations and assumptions about learning,
pedagogy, and the learner: As the assumptions change, the interplay among
the foundations changes. The issue is not the inherent superiority of one
approach over another, but recognition of the foundations, assumptions and
methods appropriate to specific learning goals and cultures.
Student-centered learning environments, with or without technology, will
not be the system of choice for all types of learning. In some instances,
they may be impossible to justify. Some initial tool skills are likely best
achieved via “basics first” approaches where knowledge and skills are sepa-
rated from their contexts. Specific performance demands, time constraints,
contextual demands, and the need to be productive quickly may militate
against approaches that emphasize sustained study. Pragmatics often limit
significantly the ways in which learning goals can be addressed.
It is important to recognize, however, that viable alternatives to direct
instruction methods exist, alternatives that reflect different assumptions and
draw upon different research and theory bases than do traditional approaches.
The shifts are fundamental, not cosmetic or semantic in nature. The issue is
not simply one of emphasizing similarities across approaches, but compre-
hending the differences in assumptions and foundations that underlie them.
Simply renaming traditional processes, without altering basic beliefs about
the processes themselves and the supporting methods, will not significantly
alter the nature or quality of a learning environment. If we aim to address
truc127.tex; 5/05/1997; 16:54; v.6; p.31
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... Student-centered learning (SCL) is a learning process in which students have autonomy and responsibility for their own learning (Hannafin & Land, 1997;Lee & Hannafin, 2016). Classes that focus on SCL often encourage individual or group projects where students choose their topic of interest, conduct research, consult with others, and share their project outcomes (Lee & Hannafin, 2016). ...
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Supporting student mental health and wellbeing continues to be a foremost concern in Higher Education (HE), as rates of students presenting with mental health conditions, distress and poor wellbeing increase and as demand for counselling and support services exceeds supply. The age range of students in third level education often coincides with the challenging transitional period of emerging adulthood, where instabilities are further compounded by academic, financial, and social pressures. As HE institutes are distinct settings where academic work, hobbies, social life, as well as health and other services are often integrated, HE presents a unique opportunity to address this wider societal concern through a systems-based lens. Despite calls for holistic, whole of institution approaches, a transformation of student wellbeing has yet to be realised. During a national initiative for valuing teaching and learning in HE in Ireland, the authors hosted an engaged online event to mobilise learning and action in the student wellbeing community. The event included case study presentations from existing examples of wellbeing in the curriculum, a panel discussion on the national landscape, and an open discussion on the future of wellbeing in HE. Attendees included academic faculty, HE management, researchers, staff from health and counselling services and health promotion, student representatives, careers and support services, and others. Data were collected via the recorded oral contributions, Zoom chat, and an anonymous survey. A deductive thematic analysis was completed with the guiding concept of an institution as a system that supports wellbeing. Findings proposed shared values as the compass for organisational culture, leaders and decision makers as key enablers of change, academic structures as both a vehicle to promote positive wellbeing and mitigate negative impacts, academic staff as the embodiment of the institution and its values, and the student voice as a guide for informed decision making. Recognising an institution as part of a wider system of HE which is influenced by political and economic climates, there is a requirement for HE to set out its stall with respect to its purpose and responsibility to wellbeing. This affirmation could enable a shared understanding of and commitment to wellbeing across the sector, through which collaborative and system-based efforts to support wellbeing can be actioned.
... Student-centered learning (SCL) is a learning process in which students have autonomy and responsibility for their own learning (Hannafin & Land, 1997;Lee & Hannafin, 2016). Classes that focus on SCL often encourage individual or group projects where students choose their topic of interest, conduct research, consult with others, and share their project outcomes (Lee & Hannafin, 2016). ...
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A significant number of children in relation to educational institutions in the European Union, subjected to inappropriate and discriminatory position, represent the basis of the document Union of Equality: Strategy for the Rights of Persons with Disabilities 2021-2030 (COM, 2021). At the same time, the social model of inclusion in education (Slee et al., 2019; Sunko, 2021) and articles 2 and 23 of the Convention on the Rights of the Child (1989) advocating equal rights of all children to education, indicate the dichotomy of desired and achieved. Each individual, whether he/she has certain difficulties or not, differs in his/her abilities, and each of them has their “personal needs”. It is important to note that students with special educational needs are students with disabilities and gifted students. Meeting the diverse needs of students through inclusive practices is often difficult or even impossible for those teachers who have not acquired the necessary skills and knowledge, so it is imperative to empower and support teachers primarily through formal education so that teachers learn to use effective inclusive teaching methods at all levels (Loveys, 2022). The aim of this research was to determine whether personal experience and student’s attended academic year of the teacher study (N = 304, all academic years) from three Teachers’ Faculties in the Republic of Croatia, correlate with their sense of personal competence, motivation for further professional development, or the need to change the study program. The results of this research show that students’ personal experiences with children with developmental disabilities (DD) affect the sense of their personal competence for working with children with DD, and that students of all attended academic years are equally motivated to teach children with DD. They also point out the need for additional training, and 84.64% of them emphasize the importance of practice in learning that deals with teaching children with DD. Data suggest that the same percentage of students feel the need to change/adapt the content of the study program accordingly. The main feature and implication of this research is the insight into the development of future teachers’ needs for further higher education for teaching children with DD.
... This belief nurtures the notion that technology changes the way we perceive realities in the 21st century, such as the way we teach and students learn. Thus, to a large extent, technology is seen as a "golden key" for facilitating technology-enhanced and studentcentered teaching environments (Hannafin & Land, 1997). ...
... The term computer-based learning environments (CBLEs) refers to open-ended studentcentered learning environments that can adapt to the needs of individual students by systematically and dynamically providing platforms of key learning processes [72]. CBLE uses technology to engage students in problem solving, exploring, and manipulating concepts in order to assess and reflect on their knowledge [73]. ...
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The COVID-19-induced distance education was perceived as highly challenging by teachers and students. A cross-national comparison of five European countries identified several challenges occurred during the distance learning period. On this basis, the article aims to develop a theoretical framework and design requirements for distance and online learning tools. As one example for online learning in mathematics education, the ASYMPTOTE system is introduced. It will be freely available by May 2022. ASYMPTOTE is aimed at the adaptive and synchronous delivery of online education by taking a mobile learning approach. Its core is the so-called digital classroom, which not only allows students to interact with each other or with the teacher but also enables teachers to monitor their students' work progress in real time. With respect to the theoretical framework, this article analyses to what extent the ASYMPTOTE system meets the requirements of online learning. Overall, the digital classroom can be seen as a promising tool for teachers to carry out appropriate formative assessment and-partly-to maintain personal and content-related interaction at a distance. Moreover, we highlight the availability of this tool. Due to its mobile learning approach, almost all students will be able to participate in lessons conducted with ASYMPTOTE.
... Self-regulated learning strategies and procrastination in online learning settings have become more important as the students and instructors are not physically present. While designing student-centered learning settings have always been valued in face-to-face learning (Hannafin & Land, 1997), this has also been prioritized in online education (Means et al., 2009). Although designing of such online learning environments remains a challenge, flipped learning model has started to be used by researchers as an alternative to create student-centered learning environments. ...
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To understand the relationships between flipped learning, self-regulated online learning and academic procrastination, this study constructs a mediation model to examine the impact of flipped learning on pre-service teachers’ self-regulated online learning behaviors and academic procrastination. A total of 396 pre-service teachers enrolled in a university in Turkey participated in the study, 306 (77.3%) of the participants were female and 90 (22.7%) were male. Three different data collection tools were used in the study: Flipped Learning Scale, Self-Regulated Online Learning Questionnaire, and The Scale of Academic Procrastination. Path analysis technique was used to analyze the data. The study found that while learning support, the dimension of the flipped learning scale, predicts self-regulated online learning in a significant and positive way, self-regulated online learning predicts academic procrastination in a significant and negative way. One unit increase in learning support increases students’ self-regulated online learning by 0.25 units (t = 5.05; p < .001). On the other hand, a one-unit increase in self-regulated online learning decreases students’ academic procrastination by 0.54 units (t = -12.80; p < .001). In addition, self-regulated online learning has a full mediating effect in the relationship between learning support and academic procrastination.
... ey believe that teaching-learning in a clinical setting is the result of the activity, context, and culture in which it occurs. As a result, in addition to psychological, pedagogical, technological, and pragmatic perspectives, cultural and contextual factors should be considered when building clinical teaching models and environments [50]. e learning environment or "atmosphere" is a key aspect of the curriculum, which is less tangible than the other aspects of the curriculum. ...
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Teaching as the center of every curriculum is a set of actions undertaken to create and facilitate student learning. In recent years, the number of qualitative studies of effective teaching in clinical education of undergraduate medical education has increased. As the number of research studies grows, it is necessary to aggregate them. Therefore, the purpose of this study was to synthesize effective teaching-learning factors through the meta-synthesis in clinical education. In this study, the meta-synthesis approach was used to synthesize qualitative evidence in relation to effective teaching-learning factors in clinical education. From 1990 to 2021, electronic databases and journals were searched to identify studies and publications on effective teaching-learning factors in clinical education. Based on the combination and search strategies in the databases (OVID, PubMed, Web of Science, SCOPUS, Eric, Magiran, and SID), 33,799 and 56 studies were identified from other sources. Following reviewing the full text of the articles, 53 studies were selected. Forty-five studies were selected and included in the meta-synthesis process after critical appraisal using the Critical Appraisal Skills Program. The synthesis of qualitative evidence was developed as an effective teaching-learning framework in clinical education. Based on this comprehensive framework, effective teaching-learning in clinical education can be seen through behavioral or content (learner, teacher, and patient and their behaviors), social (collaborative learning community), pedagogical (instructional design and teaching-learning opportunities), contextual (positive and supportive clinical environment), and educational leadership (classroom management and structure) lenses. The current research is the first qualitative meta-synthesis to provide a thorough and comprehensive review of effective teaching-learning factors in clinical education. The results of this meta-synthesis provide a holistic view of clinical education and can help with clinical teaching-learning design.
... Technology-enhanced learning environments can be used for many purposes, such as supporting collaborative learning and knowledge building (Häkkinen and Hämäläinen, 2012;Deng and Tavares, 2013), facilitating students' understanding of the topic, for example through visualisation tools (e.g., Sorva et al., 2013;Guillén-Gámez et al., 2021), giving students feedback and monitoring their learning progress (Jääskelä et al., 2017), and implementing online exams and assessments for learning (Myyry and Joutsenvirta, 2015;Marcelo and Yot-Dominiguez, 2019). In general, educational technology is believed to enhance the design of student-centred learning environments (Hannafin and Land, 1997;Ottenbreit-Leftwich et al., 2010;Reilugh, 2014). Recent studies have shown that, despite universities' efforts to increase and improve digital teaching and learning, both teachers and students only use a limited number of digital tools, and teachers use them mainly to organise teaching, not to promote studentcentred learning or for pedagogical purposes (Tømte et al., 2015;Bond et al., 2018;Amhag et al., 2019). ...
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This study examines, using a cross-sectional approach, the digital competence of academic teachers at a time when teaching shifted to digital distance learning at the start of the COVID-19 pandemic. Teachers from different academic fields at a large multidisciplinary Finnish university (N = 265) responded to a questionnaire about the purposes for which they use digital tools in teaching, how they evaluated their competence at distance teaching during the lockdown of March-May 2020 and their beliefs about distance teaching. The respondents used digital tools in teaching mostly for delivering information. According to their evaluations, their competence in distance teaching increased during the early stages of COVID-19 pandemic in 2020, but their beliefs about distance teaching did not relate to the feelings of competence. Respondents with no experience in distance teaching before the lockdown evaluated their competence as having increased more than did respondents with previous experience. The implications of the findings for understanding competence development are then discussed.
... To be effective, planning for e-learning must focus on T&L strategies that are directly linked to student-centered approaches emphasized by the constructivist paradigm of instruction (Restuati et al, 2001;Hosnan, 2017;Hannafin & Land, 1997). This implies that students in such a flexible learning environment will be in more need of guidance, to be able to plan their individual learning and assume a more independent role than they normally would have in a traditional learning setting (Reichert & Tauch, 2005). ...
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Post COVID-19 outbreak, higher education practices changed considerably around the world. In Bahrain, institutions of higher learning as well as the country’s education quality authority have been challenged in different ways because of the pandemic and have been compelled to resort to e-learning and virtual approaches to quality reviews, respectively. This paper presents an analysis of e-learning in higher education in Bahrain and related quality assurance processes pre-and-post COVID-19. This is in addition to arriving at conclusions about what the next relevant steps forward are. The research method followed in this paper is qualitative, taking the form of a small-scale inquiry cast from a quality assurance perspective, that is social context-dependent and focusing on a specific case (e-learning in Bahrain pre-and-post COVID). It, thus, provides an interpretivist perspective in terms of knowledge. The data analysis points to a need for the establishment of a comprehensive set of guidelines for higher education institutions in Bahrain, to develop their own ‘Quality Assurance in E-Learning’ models. These models are simultaneously ones that can facilitate more accurate and in-depth quality evaluations by the Kingdom’s quality authority.
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In this study, we explored the possibilities for enhancing the user experience (UX) of technology-enhanced workplace learning to stimulate acceptance. This was studied using a systematic literature review to gain possibly helpful insights and thematic and causal analyses of questionnaires regarding a naturalistic case, to ultimately develop UX design propositions to stimulate acceptance. We found that existing literature focussed on 9 aspects: self-regulation, the need for interventions, usability and user experience, social influence, (real-life) interaction, availability, relevance, demographics, and safety. Regarding user experience, hedonic factors seemed to be of bigger concern than pragmatic factors. The analyses of the questionnaires, however, showed that, in the studied case, the pragmatic factors were of greater importance for acceptance. The initial design propositions we developed thus mainly focus on pragmatic quality. We hope this study provides a source of information for future research in this field of study, providing insights which could be helpful for the research community. Future research could focus on further verifying and developing the initial design propositions. The acceptance of technology enhanced workplace learning may be an even more unexplored field of study than we initially thought.