Rita Kop
University of Wales Swansea
Adrian Hill
Open School BC, Canada
Open School BC, Canada
Abstract
Siemens and Downes initially received increasing attention in the
blogosphere in 2005 when they discussed their ideas concerning
distributed knowledge. An extended discourse has ensued in and around
the status of ‘connectivism’ as a learning theory for the digital age.
This has led to a number of questions in relation to existing learning
theories. Do they still meet the needs of today’s learners, and
anticipate the needs of learners of the future? Would a new theory that
encompasses new developments in digital technology be more
appropriate, and would it be suitable for other aspects of learning,
including in the traditional class room, in distance education and
e-learning? This paper will highlight current theories of learning and
critically analyse connectivism within the context of its
predecessors, to establish if it has anything new to offer as a learning
theory or as an approach to teaching for the 21st Century.
Keywords: e-Learning; online learning; open learning; distance education; pedagogy; learning theory; educational theory
Introduction
To what extent do existing learning theories meet the needs of
today’s learners, and anticipate the needs of learners of the future?
Since Siemens’ Connectivism: Learning as Network Creation (2005) and Downes’ An Introduction to Connective Knowledge
(2005) initially garnered increasing attention in the blogosphere in
2005, an extended discourse has ensued in and around the status of
connectivism as a learning theory for the digital age. Kerr (2007d)
identifies two purposes for the development of a new theory: it replaces
older theories that have become inferior, and the new theory builds on
older theories without discarding them, because new developments have
occurred which the older theories no longer explain.
If older theories are to be replaced by connectivism, then what are
the grounds for this measure? If connectivism is to build on older
theories, how is the integration of the old and new theories to be
conducted? Forster (2007) maintains that for connectivism to be a
learning theory, the theory’s limitations and the full range of
contexts in which learning can take place must be accounted for.
Otherwise, connectivism’s implementation by teachers may be
insufficient and misguided.
With the changes that have occurred as a result of increased
accessibility to information and a rapidly evolving technological
landscape, educators in higher learning institutions have been forced
to adapt their teaching approaches without a clear roadmap for attending
to students’ various needs. The wide range of approaches and
learning paths that are available to redesign curricula cause friction
for educators and instructional designers who are required to deliver
course materials in accordance with learning outcomes prescribed and
mandated by educational institutions.
Overview of Connectivism
Connectivism is a theoretical framework for understanding learning. In
connectivism, the starting point for learning occurs when knowledge is
actuated through the process of a learner connecting to and feeding
information into a learning community. Siemens (2004) states, “A
community is the clustering of similar areas of interest that allows for
interaction, sharing, dialoguing, and thinking together.”
In the connectivist model, a learning community is described as a node,
which is always part of a larger network. Nodes arise out of the
connection points that are found on a network. A network is comprised
of two or more nodes linked in order to share resources. Nodes may be
of varying size and strength, depending on the concentration of
information and the number of individuals who are navigating through a
particular node (Downes, 2008).
According to connectivism, knowledge is distributed across an
information network and can be stored in a variety of digital formats.
Learning and knowledge are said to “rest in diversity of opinions”
(Siemens, 2008, para. 8). Learning transpires through the use of both
the cognitive and the affective domains; cognition and the emotions
both contribute to the learning process in important ways.
Since information is constantly changing, its validity and accuracy
may change over time, depending on the discovery of new contributions
pertaining to a subject. By extension, one’s understanding of a
subject, one’s ability to learn about the subject in question, will also
change over time. Connectivism stresses that two important skills
that contribute to learning are the ability to seek out current
information, and the ability to filter secondary and extraneous
information. Simply put, “The capacity to know is more critical than
what is actually known” (Siemens, 2008, para. 6). The ability to make
decisions on the basis of information that has been acquired is
considered integral to the learning process.
The learning process is cyclical, in that learners will connect to a
network to share and find new information, will modify their beliefs
on the basis of new learning, and will then connect to a network to
share these realizations and find new information once more. Learning
is considered a “. . . knowledge creation process . . . not only
knowledge consumption.” One’s personal learning network is formed on
the basis of how one’s connection to learning communities are organized
by a learner.
Learners may transverse networks through multiple knowledge domains.
The peripheries of knowledge fields are porous, allowing for the
interdisciplinary connections to be made. Siemens asserts, “The ability
to see connections between fields, ideas, and concepts is a core
skill” (Siemens, 2008, para. 10). The connectivist metaphor is
particularly timely, since the navigation of the Internet and the means
by which information is dispersed on the Internet now provides a
reference point for Siemens’ assertions.
Is Connectivism a Learning Theory?
Gredler (2005) refers to four constituent elements that must exist to qualify a theory as well-constructed:
Clear assumptions and beliefs about the object of the theory should be highlighted; key terms should be clearly defined; there should be a developmental process, where principles are derived from assumptions; and it should entail an explanation of “underlying psychological dynamics of events related to learning.” (cited in Siemens 2006b, p. 28)
Juxtaposed with this framework, Siemens (2006b) suggests:
Instead of modelling our knowledge structures as hierarchical or flat, confined belief spaces, the view of networks enables the existence of contrasting elements selected on the intent of a particular research or learning activities. If the silos of traditional knowledge classification schemes are more fluid, perhaps the individual elements of different theories can be adopted, as required, to solve more nuances of learning problems. When the theory does not require adoption in its fullest (i.e. interpretivism or positivism), the task of seeking knowledge becomes more salient. (p. 29)
In Theories of Developmental Psychology, Miller (1993)
distinguishes between “theory” and “developmental theory,” and
identifies the vast deficit that can exist between the two. In
general, an emerging theory should fall within the domain of scientific
research, use scientific methods, and be based on previously conducted
studies. It should be logically constructed and verifiable through
testing.
In contrast, a developmental theory may attempt to take strides
towards becoming an established formal theory over time. Developmental
theories are fertile testing grounds for ideas, which, in turn, may
lead to empirical research that can then validate – or disprove –
formal hypotheses posited within the framework of the scientific
method. They attribute meaning to facts within the context of a broad
organizational framework. The framework may place particular emphasis
and interest on some facts over others, which in turn can lead to
further inquiry on the basis of a prioritization of information.
Miller (1993) identifies three main tasks that developmental theories should fulfil:
- To describe changes within one or several areas of behaviour
- To describe changes in the relationships among several areas of behaviour
- To explain the course of development that has been described in terms of the first two tasks. (Miller, 1993, pp. 5,6)
How does connectivism fulfil these tasks? The model frames
learning in terms of learners connecting to nodes on network,
suggesting that knowledge does not reside in one location, but rather
that it is a confluence of information arising out of multiple
individuals seeking inquiry related to a common interest and providing
feedback to one another.
Downes (1996) suggests that an ‘emergentist’ theory of learning must treat knowledge as ‘subsymbolic’.
According to Downes, knowledge is treated as “. . . a recognition of a
pattern in a set of neural events [if we are introspecting] or
behavioural events [if we are observing]” (para. 31). Additionally,
knowledge is the experience of “. . . a mental state that is at best
seen as an approximation of what it is that is being said in
words or experienced in nature, an approximation that is framed and,
indeed, comprehensible only from which the rich set of world views,
previous experiences and frames in which it is embedded” (para. 41).
The developmental implications of Downes’ definitions of learning
and knowledge are far-reaching. If learning transpires via
connections to nodes on the network, then it follows that the
maximization of learning can best be achieved through identifying the
properties of effective networks, which is precisely what Downes sets
out to achieve in Learning Networks and Connective Knowledge.
Connectivism is mainly concerned with cognitive development, and as
such does not concentrate on explaining how connections to networks may
be interpreted in relation to physical maturation or the changes that
occur over time via a person’s exposure to, and interaction, with the
social world. This is particularly the case where explaining
behavioural performance and moral development in specific contexts is
concerned.
Siemens (2006b) highlights other factors that may inform the
development of a new learning theory, namely “how we teach, how we
design curriculum, the spaces and structures of learning, and the
manner in which we foster and direct critical and creative thought in
our redesign of education” (p. 6). A multitude of elements could
change with the introduction of a new theory.
With the advent of new considerations in instructional design and
implementation, universities are taking the task of adapting their
instructional approaches seriously. The utilization of information
technology in the classroom has become a feature of instruction. What
remains to be established is whether connectivism holds its own as a
new theoretical model to support this endeavour.
Miller (1983) maintains:
When a person develops or adopts a particular theory, she takes on a whole set of beliefs concerning what questions about development are worth asking, what methods for studying these questions are legitimate, and what the nature of development is. . . There are unwritten rules of the game that are very much part of the theory as it is practiced. (p. 5)
Perhaps with Downes’ ‘theory of distributed knowledge’ the rules of
the game have not yet fully extended from the philosophical domain into
that of applied educational research, though Siemens’ connectivist
model is a ripe training ground for further studies.
Epistemological Frameworks for Learning
Siemens (2008b, p. 9) draws on the work of Driscoll in categorizing
learning “into three broad epistemological frameworks” namely
objectivism, pragmatism, and interpretivism. According to objectivism,
reality is external to the mind, and knowledge and perception are
experientially acquired. Pragmatism suggests that knowledge is a
negotiation between reflection and experience, inquiry and action, and
interpretivism posits that knowledge is an internal construction and is
informed through socialization and cultural cues.
A fourth framework is also introduced, namely Downes’ (2006) theory
of distributed knowledge, which is supported by Siemens (2008b) who
sees “. . . the view of knowledge as composed of connections and
networked entities …The concept of emergent, connected, and adaptive
knowledge provides the epistemological framework for connectivism as a
learning theory” (p. 10). Siemens sees the alignment between
epistemologies and learning theories as detailed in Figure 1.
Figure 1. Alignment of Epistemologies and Learning Theories
The first three are universally accepted, but the concept of
connectivism as a learning theory has had some criticism, including
from Verhagen (2006), who argued that the theory remains
unsubstantiated philosophising. Kerr suggested that existing theories
“satisfactorily address the needs of learning in today’s
technologically, connected age” (Siemens, 2008b). Proponents of
connectivism are “exploring a model of learning that reflects the
network-like structure evident in online interactions,” (p. 12) but is
this enough to constitute its formulation as a new learning theory, and
does connectivism have anything new to offer? Criteria must be met to
establish connectivism as a learning theory. Before exploring these
considerations in greater depth however, let us revisit pre-connectivist
theories of learning that have influenced its development as a model.
Pre-Connectivist Theories of Learning
Kerr (2007a) contends that the relationship between internal and
external knowledge environments was accounted for in Vygotsky's
formulation of social constructivism, long before any explanation was
provided by connectivism. Similarly, Kerr asserts that Papert’s
constructivism and Clark’s embodied active cognition also provided
explanations prior to connectivism. Communities of practice are another
model that treats learning as an inherently social and situated
engagement.
Vygotsky, whose name is inherently linked to social constructivism,
saw two important elements in the learning process: ‘language’ and
‘scaffolding.’ Vygotsky noted how self-talk in children serves as a
means by which learners may work through complex problems by
externalizing them as a form of self-guidance and self-direction. From a
cognitive development standpoint, this observation is important
because the child’s social interaction with others helps formulate
private speech in the child. Instructional scaffolding provides support
for learning and problem solving through the use of hints, reviewing
material, encouragement, and reducing complex problems into “manageable
chunks” (Woolfolk, 1995, p. 49). The relationship between the
individual and external knowledge is present in the relationship
between what is known by the learner in question, and that knowledge to
which the learner is being exposed.
Papert (1991) formulated the theory of constructionism.
Constructionism contends that learning occurs through learners’
engaging in creative experimentation and activity. Papert distinguishes
between learning and teaching, with teaching treated as secondary to
the hands-on creative process – for instance, a group of children
playing with Lego blocks or creating clay sculptures are ‘objects to
think with.’ Learning, therefore, is considered an interaction between
the individual and his or her environment, a relational understanding.
By extension, Papert asserts that the computer’s role in learning
ought to be enabling, as a means for children to use knowledge.
Clark (1997) extended Papert’s position with the theory of embodied
active cognition, in which he argued that the scaffolding provided by
language and ‘objects to think with’ is a mutual interaction between
mind, brain, and the environment, and may draw upon multiple
theoretical frameworks (e.g., connectionist, cognitivist) to explain
cognition. Kerr (2007a) suggests that the ideas that are the basis of
connectivism have already been developed by Clark, and that recent
widespread recognition for the work of connectivism is due to the high
visibility of networks in the current age (e.g., the Internet) compared
with in the past. Whereas language is so ubiquitous that it is not
always noticed, network-based learning theories can now unequivocally
point to existing networks, such as the World Wide Web.
Lave and Wenger (2002) researched the way people learn in their
daily lives and suggested the typology of a ‘community of practice,’
which is based on the premises that humans are social beings, and that
knowledge is developed through active engagement in valued undertakings
throughout their lives. Clearly, learning does not only take place
within a learning institution. According to Wenger (1998),
Our institutions . . . are largely based on the assumption that learning is an individual process, that it has a beginning and an end, that it is best separated from the rest of our activities, and that it is the result of teaching. (p. 3)
Lave and Wenger (2002) do not see learning as individual; in their
view learners make sense of their surroundings in a social setting, by
communicating with others. Knowledge is situated within a community in
which a more 'knowledgeable other' facilitates the move from the
periphery to the centre of the community. People build on earlier
experiences and knowledge.
Downes and Siemens: Connectivism
With connectivism, the formation of connections between nodes of
information (i.e., networks) constitutes knowledge – and in addition,
connectivism posits that “the ability to construct and traverse those
networks” (Downes, 2007) comprises learning. As Siemens (2006b) has
suggested, “the learning is the network.” Downes (2007b) further
states:
“Where connectivism differs from those theories, I would argue, is that connectivism denies that knowledge is propositional. That is to say, these other theories are 'cognitivist', in the sense that they depict knowledge and learning as being grounded in language and logic. Connectivism is, by contrast, ‘connectionist’. Knowledge is, on this theory, literally the set of connections formed by actions and experience. It may consist in part of linguistic structures, but it is not essentially based in linguistic structures, and the properties and constraints of linguistic structures are not the properties and constraints of connectivism. . . In connectivism, there is no real concept of transferring knowledge, making knowledge, or building knowledge. Rather, the activities we undertake when we conduct practices in order to learn are more like growing or developing ourselves and our society in certain (connected) ways.”
Downes (2007b) identifies “the core proposition shared between
connectivism and constructivism” as knowledge ‘not being acquired, as
though it were a thing.’ Moreover, Kerr stresses the importance of
connectivism’s not losing “the lessons of constructivism and the need
for each learner to construct his or her own mental models in an
individualistic way” (Forster, 2007, para. 1).
Verhagen (2006) criticises connectivism as a new theory, primarily
because he can distil no new principles from connectivism that are not
already present in other existing learning theories. Moreover, he is
not convinced that learning can reside in non-human appliances.
Siemens (2006b) responded that a new learning theory, in fact, is
required, due to the exponential growth and complexity of information
available on the Internet, new possibilities for people to communicate
on global networks, and for the ability to aggregate different
information streams. Siemens argues that “knowledge does not only
reside in the mind of an individual, knowledge resides in a distributed
manner across a network . . . learning is the act of recognizing
patterns shaped by complex networks.’ These networks are internal, as
neural networks, and external, as networks in which we adapt to the
world around us (Siemens 2006b, p. 10).
In Miller’s (1993) extended analysis of theoretical frameworks in
developmental psychology, she describes contextual theories as arising
out of “the intertwining of an object or person and its surroundings,
the interconnectedness of contexts, and the intermingling of biology
and culture” (p. 410). Presently, connectivism is lacking an
extensive body of empirical research literature to lend it support.
Miller (1993) argues that the “greater the distance between theory and
behaviour” the greater the problems to prove or disprove the theory (p. 410).
Where connectivism draws its strength is through using Web-based
activity as an example of learning looking through the connectivist
lens. The analogy is intuitive and powerful because of the ubiquitous
use of the Internet in today’s world. In addition, Downes (2006) has
elucidated an epistemological framework for distributed knowledge which
provides a strong philosophical basis for the connectivist learning
framework.
Higher Order Thinking: Learning and knowledge transfer
Kerr (2007b) suggests that no theory, including the connectivist
model, sufficiently explains higher order thinking “as a mechanism
spanning brain, perception and environment.” He states that “knowledge
is not learning or education.” He challenges connectivism to explain
“transferring understanding, making understanding and building
understanding”, and the internal processes that lead to “deep thinking
and creating understanding.”
Siemens suggests that when a learner is engaged in creating and
recreating their own learning network, understanding arises through
applying meta-cognition to the evaluation of “which elements in the
network serve useful purposes and which elements need to be
eliminated.” Downes (2007a) contends “that ‘understanding’ is a
distribution of connections across a network. To ‘know that P’ is
therefore equated with ‘a certain set of neural connections’ that
entail being in a certain physical state” unique to the experiencer of
that state. The physical state in question is not distinct from the
other physical states with which it is intertwined within that
individual. Downes asserts that in connectivism, ‘deep thinking’ or
‘creating understanding’ are equivalent to the process of making
connections, and that there are no mental models per se (i.e.,
no systematically constructed rule-based representational systems), and
what there is (i.e., connectionist networks) is not built, like a
model; but instead it is grown, like a plant.
Kerr (2007c) suggests that words and language are necessary to
sustain long predictive chains of thought – e.g., to sustain a chain or
combination of pattern recognition. He contends that this is true in
chess, for example, where the player uses chess notation to assist his
or her memory. Downes (2007a), however, raises two questions of
importance in response to Kerr’s assertion:
‘First, do we play chess (solely) by constructing strings of inferences (i.e., sequences of moves in chess notation)? And second, even when we construct strings of inferences, is this how we actually think, or is this how we describe how we think?’ (Downes, 2007a)
Though the expression of thoughts is limited by grammatical
principles in language, it may be that thoughts themselves are not
necessarily bound by language, and therefore at least in some cases, may
not be constrained by grammatical principles.
Pattern Recognition
Downes (2006) contends that the assumption that we think in a
language is misguided. He suggests that thinking is actually the
arrangement of ‘pieces’ which are then matched to desirable (or
undesirable) outcomes. What are these pieces? What gives them shape?
Whether these questions can be definitely answered, the reason for
Downes’ drawing the distinction between pattern matching compared with
“long predictive chains of thought” is worthy of consideration. If it
is the case that reasoning is a function of pattern matching, as
opposed to the rule-governed principles of physical symbol systems that
define linguistic structures, then the characterization of
connectivism is dramatically different from that of constructivism.
Kerr's (2007a) assertion is that “the mind is a construct which is
distributed from the brain to the environment.” He stresses that how we
answer the questions ‘What is the mind?’ ‘Where is the mind?’ and
‘How does it work?’ are at the heart of the development of learning
theories, and that the answers have profound practical implications.
Humans may be predisposed to identifying certain patterns on the
basis of their neurological makeup; these patterns, in fact, may be
intrinsic qualities of mind. Kerr (2007a) refers to Kay’s
non-universals, a series of understandings (identified on the basis of
research by anthropologists) that are not learned spontaneously, and
which are common to all known human societies – for instance,
“deductive abstract mathematics, model-based science, democracy [and]
slow deep thinking.” Kerr suggests that if learning these
non-universals is considered important, then methods ought to be
identified to teach them. The suggestion is not to propound the
existence of ‘fundamental knowledge,’ but to question and challenge the
connectivist slogan, ‘the half-life of knowledge is declining’ by pointing out the importance of identifying strategies to ensure that at least some forms of learning persist.
Bruner (1999) describes a situated view of mind, where it is both
represented by and understood in terms of human cultural contexts. This
mode is shared by a community, and is also passed on from generation
to generation to maintain the culture’s way of life and identity.
“Although meanings are in the mind, they find their origin and
significance in the community in which they were created. . . It is
culture that provides the tools for organizing and understanding our
worlds in communicable ways” (Bruner 1999, p. 149).
The Compatibility of Connectivism and Formal Education
Three predominant pressures are influencing and instigating change
in the dissemination and retrieval of information, each of which is
fundamentally altering the formal educational landscape: millennial
learners’ needs are not sufficiently being met by traditional training
models of instruction, information growth has necessitated new means by
which to navigate and filter the information that is available, and
advancing technologies are increasingly enabling learners to connect to
one another and to knowledge networks of their own making (Siemens,
2008b, p. 7).
Verhagen (2006) sees that connectivism fits exactly at this level of
pedagogy and curriculum rather than at the level of theory, since, in
effect, people still learn in the same way, though they continue to
adapt to the changing technological landscape. Learners might move away
from classroom groups and a tutor to online networks and important
nodes on these networks, but in effect the same activity takes place on
a different scale – although learners might miss out on a layer of
critical engagement as their choice of mentor could confirm rather than
challenge views and opinions.
Teaching in a Connected Environment
Developers of e-learning (Siemens, 2008) propose that the increasing
influence of the Internet and online connectedness of people will have
implications for educational practice. The rapid development of
technology and exponential growth in the use of the Internet, along
with Web 2.0 and mobile developments, make new and different
educational structures, organisations, and settings a possibility. The
online and face-to face networks that people build-up throughout their
lives will provide expertise and knowledge, in addition to the
guidance that local or online tutors can provide. Learners will be at
the centre of the learning experience, rather than the tutor and the
institution. Learners will be instrumental in determining the content
of the learning, in addition to deciding the nature and levels of
communication, and who can participate.
The role of the tutor will not only change, but may disappear
altogether. People can move from a learning environment controlled by
the tutor and the institution, to an environment where they direct
their own learning, find their own information, and create knowledge by
engaging in networks away from the formal setting. They still
communicate with others, but their personal interests and preferences –
rather than institutional requirements and choices – are the main
drivers for their engagement with more knowledgeable others in their
learning.
The networks in which people communicate can be small or vast, but
the main characteristics for networks to support knowledge development
will be that they are diverse, open, autonomous, and connected (Downes,
2007c). There are parallels with Illich’s (1971) educational vision
of the 1970s, particularly his idea of ‘community webs.’ Online
networks also come together as interest groups of autonomous
participants, but Illich envisaged his webs in community settings and
aimed at bringing local people together with learners and ‘people with
knowledge.’ Online networks might be open and may facilitate
connections, but local culture and values cannot be incorporated all
that easily as the online networks are global, with diverse
participants, each bringing his or her own ideas and background to the
fore. This might stimulate debate, but the local community and its
development would be of less importance than the dominant culture on
the network.
There have been concerns about the lack of critical engagement
online (Norris 2001), because of the temptation to connect with
like-minded people, rather than in more challenging transactions, with
experts such as the teacher in a classroom, whose role is to make people
aware of alternative points of view. Critical educators, such as
Freire and Macedo (1999), thought it essential that teachers have a
directive role. In this capacity, teachers would enter into a dialogue
“as a process of ‘learning and knowing’ with learners, rather than the
dialogue being a ‘conversation’ that would remain at the level of ‘the
individual’s lived experience. ’ I engage in dialogue because I
recognise the social and not merely the individualistic character of
knowing” (Freire & Macedo,1999, p. 48). He felt that this capacity
for critical engagement would not be present if educators are reduced
to facilitators, which is the role of the tutor that has been widely
accepted in e-learning (Salmon, 2004). Moreover, in a connectivist
online environment, with an emphasis on informal learning and the
individual’s choice to engage with experts outside the classroom, this
critical and localized influence could be lost completely. The lack
of critical engagement by a tutor – on top of the diminishing level of
control by the institution – implicates a high level of learner
autonomy.
Current research in adult education shows that the levels of
confidence and learner autonomy, in addition to discipline, are of
crucial importance to the level of engagement by the learner in a
personalized learning environment, as lack of these in the majority of
participants hampered their learning online. Nearly all students
preferred the help and support of the local or online tutor to guide
them through resources and activities, to validate information, and to
critically engage them in the course content (Kop, 2008), which would
indicate the need for a localized tutor presence.
Downes and Siemens do not suggest that connectivism is limited to
the online environment. The online environment is one application that
has been important for the development of connectivism, but the theory
applies to a larger learning environment, and helps to inform how we
understand our relatedness to the world, and consequently how we learn
and understand from it. Networks are not just comprised of digitally
enabled communications media, nor are they exclusively based in
neurological brain-based mechanisms. The networks to which Downes and
Siemens are referring are the relationship between ‘internal’ and
‘external’ physical environments. As Siemens suggests, the learning is the network.
Though an increase in the ability to converse and collaborate has occurred with the advent of new information and communication technologies, Kerr (2007a) reminds us that “good educators have always recognized the importance of these things.” What has changed is the scalability of communication, though it does not follow that at the level of learning theory, a new innovation or idea has been discovered: “The scaling is not actually innovation.”
Though an increase in the ability to converse and collaborate has occurred with the advent of new information and communication technologies, Kerr (2007a) reminds us that “good educators have always recognized the importance of these things.” What has changed is the scalability of communication, though it does not follow that at the level of learning theory, a new innovation or idea has been discovered: “The scaling is not actually innovation.”
Conclusion: Radical discontinuity
Kerr (2007a) asserts that “we are entering some sort of period of
radical discontinuity,” and further raises the question: “What is the
nature of that radical discontinuity?” In the educational domain, a
multitude of Web applications are being used to enhance the learner
experience, particularly in terms of collaboration and communication.
New learning environments are informing present and future trends from
which both educators and students stand to benefit. Moreover, the way
in which global networks and communities of interest are currently
being formed through emerging technologies is encouraging young people,
in particular, to develop new, creative, and different forms of
communication and knowledge creation outside formal education. Of
course the number of learners who have been immersed in these
technologies all their lives will grow, as the young are more
predisposed to use the latest technologies (National Statistics, 2007)
and will displace the learners who have grown up with books and pen and
paper as resources for learning. This will undoubtedly cause friction
in institutions and class rooms, particularly as (adult) educators
themselves do not always feel comfortable with the new developments
because they have not been shown adequately, or explored for
themselves, how the new and emerging technologies could enhance their
working practice. Furthermore, school systems have not developed a
connectivist model within which to deliver curricula, partly because
educational staff and institutions have not caught on to the
possibilities that digital technology have to offer, and partly because
not all people are autonomous learners. Additionally, school systems
tend to value education that is grounded in traditions of the past,
steeped in values that have developed over centuries. If, however,
learners’ worlds inside and outside education become too disparate, new
learners who are familiar with the opportunities for learning on the
Internet will be able to find their experts elsewhere. There is a need
for (adult) educators to closely follow and influence the developments
and the debates, and seriously research how their institutions can
evolve using the emerging technologies to their and their learners’
advantage. In doing so, they would ensure that (adult) education can
secure its role of critical engager, and at the same time make the best
use of technology – that is in making connections with information and
knowledgeable others all over the world to enrich learners lives and
the communities in which they live.
A paradigm shift, indeed, may be occurring in educational theory,
and a new epistemology may be emerging, but it does not seem that
connectivism’s contributions to the new paradigm warrant it being
treated as a separate learning theory in and of its own right.
Connectivism, however, continues to play an important role in the
development and emergence of new pedagogies, where control is shifting
from the tutor to an increasingly more autonomous learner.
References
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