Chatti, M. A. (2010). The LaaN Theory. In
(pp. 19-42). Aachen, Germany: Shaker Verlag.
The LaaN Theory
Mohamed Amine
Chatti
RWTH Aachen University
chatti@cs.rwth-aachen.de
ABSTRACT
One of the core
issues in Technology Enhanced Learning (TEL) is the personalization of the
learning experience. This implies a need for new TEL models that start from the
learners and satisfy their unique needs in order to achieve a personalized
learning experience for everyone. This paper discusses the Learning as a Network (LaaN) theory as a
theoretical foundation for self-organized and networked learning models. LaaN
builds upon connectivism, complexity theory, and double-loop learning. It promotes
a theory of change, movement, dynamism, self-organization, emergence, and
effectiveness, which puts the learner at the center and represents a knowledge
ecological approach to learning. In many ways, LaaN overlaps with other
learning theories; yet it is distinctive enough to be treated as a separate
perspective on learning.
Keywords
Learning
Theories, LaaN, Personal Knowledge Network, Knowledge Ecology
Introduction
In the past few years, the discussion about
technologies for learning has moved away from only institutionally managed
learning management systems to the use of personal and social tools for
learning. Central aspects of this discussion are the concepts of the personal
learning environment (PLE) and connectivist massive open online courses (cMOOCs),
where the learner is in control of her own development and learning. The PLE concept represents a
significant shift in pedagogic approaches toward a self-organized learning
model that puts the learner at the center and gives her more autonomy and
control over the learning experience. cMOOCs combine
MOOCs with PLEs and refer to open-ended, distributed, networked, and
learner-directed learning environments where the learning services are not
predetermined, and most activities take place outside the platform
While pedagogical and technological aspects of PLEs and cMOOCs are
increasingly discussed in the TEL literature, the discussion of a theoretical
framework for these concepts lacks behind. This missing theoretical framework
hinders PLEs and cMOOCs from being more widely understood and adopted. This
paper discusses the Learning as a Network (LaaN) theory as a theoretical
foundation for self-organized and networked learning models. We start with a
brief introduction of connectivism, complexity theory, and double-loop
learning, which build the backbone for LaaN. We then provide a detailed
discussion of the key characteristics of LaaN that distinguish it from dominant
learning and social theories. These theories are behaviorism, cognitivism,
(social) constructivism, situated learning, activity theory, and actor-network
theory.
Connectivism
Siemens
(2005) argues that knowledge and learning are today defined by connections. The
author stresses that “know where” and “know who” are more important today that
"knowing what" and "how", and introduces connectivism as a new learning theory
that presents learning as a connection/network-forming process. He suggests that
learning which resides outside the individual learner is focused on connecting
specialized information sets and the connections that enable us to learn more
than our current state of knowing. According Siemens, the main intent of
network creation is to enable learners to continue to stay current in the face
of rapidly developing knowledge. He further points out that the half-life of
knowledge is shrinking and argues that learning networks can resolve some of
the challenges of the rapidly diminishing half-life of knowledge.
Connectivism
is also the assertion that “the pipe is more important than the content within
the pipe” (Siemens, 2005). That is, the connections that enable us to learn are
more important than our current state of knowing. As the author puts it: “Our
ability to learn what we need for tomorrow is more important than what we know
today [...] As knowledge continues to grow and evolve, access to what is needed
is more important than what the learner currently possesses”.
A
similar view of connectivism is provided by Downes (2007) who writes: “At its
heart, connectivism is the thesis that knowledge is distributed across a
network of connections, and therefore that learning consists of the ability to
construct and traverse those networks”.
Complexity Theory
Complexity theory refers to the study of complex
systems. A complex system is an open, dynamic, and constantly evolving network
of many interacting identities, where the behavior of the whole is far more
complex than the individual behavior of the parts. The term complex adaptive
systems (CAS), or complexity science, is often used to describe the academic
field that has grown up around the study of complex systems. Complex adaptive
systems are special cases where a complex, non-linear, interactive system has
the ability to learn from and adapt to a constantly changing environment
(Holland, 1992, 1995).
Emergence is central to the theory of
complex adaptive systems (Holland, 1998). The term “emergent” was coined by the
pioneer psychologist G. H. Lewes in 1875, who wrote "The emergent is
unlike its components in so far as these are incommensurable, and it cannot be
reduced to their sum or their difference" (Lewes, 1875, p. 413) (as cited
in Corning, 2002). A more recent definition of emergence was provided by
Goldstein (1999) who refers to “the arising of novel and coherent structures,
patterns and properties during the process of self-organization in complex
systems” (p. 49). Holland (1998) stresses that emergence must be the product of
self-organization, not centralized
control (as cited in Ryan, 2007).
Drawing
on complex adaptive systems theory, Snowden (2002) developed the Cynefin
framework, which is made up of four domains:
· Simple, in which the relationship
between cause and effect relationships is well known.
· Complicated, in which the
relationship between cause and effect is knowable but some effort is required in
order to analyze the system.
· Complex, in which the relationship
between cause and effect are intertwined and cannot be known in advance.
· Chaotic, in which there is no
perceived relationship between cause and effect.
Double Loop Learning
The
concept of double-loop learning was
introduced by Argyris & Schön (1978) within an organizational learning context. According to the authors, organizational
learning is the process of detecting and correcting errors. It takes account of
the interplay between the actions and interactions of individuals and
higher-level organizational entities such as departments, divisions, or groups
of managers. Each member of an organization constructs his own representation
of the theory-in-use of the whole. Organizational learning then occurs when
individuals within an organization experience a problem (error detection) and
work on solving this problem (error correction). Error correction happens
through a continuous process of organizational inquiry, where everyone in the
organizational environment can inquire, test, compare and adjust his
theory-in-use, which is a private image of the organizational theory-in-use.
Effective organizational inquiry then leads to a reframing of one’s
theory-in-use, thereby changing the organizational theory-in-use.
Argyris
(1991) asserts that most people define learning too narrowly as mere “problem
solving”, so they focus on identifying and correcting errors in the external
environment. This is what Argyris calls single-loop
learning. But, in the words of Argyris:
if learning is to persist, managers and employees must
also look inward. They need to reflect critically on their own behavior, identify
the ways they often inadvertently contribute to the organization’s problems,
and then change how they act. (p. 99)
This
deeper form of learning is what Argyris terms double-loop learning.
To put it simply, single-loop learning differs from
double-loop learning in that the former aims at efficiency (i.e. doing things
right) and the latter focuses on effectiveness (i.e. doing the right things).
Argyris & Schön (1996) define single-loop learning as "learning that
changes strategies of actions or assumptions underlying strategies in ways that
leave the values of a theory of action unchanged" (p. 20), and double-loop
learning as "learning that results in a change in the values of
theory-in-use, as well as in its strategies and assumptions" (p. 21).
In other words, Argyris and Schön differentiate between
learning that does not change the underlying mental models of the learners but
merely revises their application scenarios (single-loop), and learning which
does affect such changes (double-loop). Double-loop learning starts from a
learner’s mental model (i.e. theory-in-use) defined by base norms, values,
strategies, and assumptions, and suggests critical reflection in order to
challenge, invalidate, or confirm the used theory-of-use. Double-loop learning
also encourages genuine inquiry into and testing of one’s actions and requires
self-criticism, i.e. the capacity for questioning one’s theory-in-use and
openness to change the same as a function of learning. The result of
reflection, inquiry, testing, and self-criticism would then be a reframing of
one’s norms and values, and a restructuring of one’s strategies and
assumptions, according to the new settings.
The LaaN Theory
The Learning as a
Network (LaaN) theory draws together some of the concepts behind
connectivism, complexity theory, and double-loop learning. An abstract view of
LaaN is depicted in Figure 1.
Figure 1:
The LaaN Theory
Connectivism
focuses on making connections (at external, conceptual, and neural levels) and seeing
patterns. However, it misses some of the double-loop learning concepts, which
are crucial for learning, such as learning from failures, error detection and
correction, and inquiry. On the other hand, double-loop learning aims at
detecting and correcting errors by changing the values, strategies, and
assumptions of the theory-in-use according to the new setting. Double-loop
learning however, does not recognize the power of connections and networks that
can help us operate in highly dynamic and uncertain knowledge environments,
characterized by increasing complexity and fast-paced change.
Within
LaaN, connectivism, complexity theory, and double-loop learning converge around
a learner-centric environment. LaaN starts from the learner and views learning
as the continuous creation of a personal
knowledge network (PKN). A PKN shapes the knowledge home and the identity
of the individual learner. For each learner, a PKN is a unique adaptive
repertoire of:
· Tacit and explicit knowledge nodes
(i.e. people and information) (external level)
· One’s theories-in-use. This
includes norms for individual performance, strategies for achieving values, and
assumptions that bind strategies and values together (conceptual/internal
level)
In
LaaN, the result of learning is a restructuring of one’s PKN, that is, an
extension of one’s external network with new knowledge nodes (external level)
and a reframing of one’s theories-in-use (conceptual/internal level).
LaaN-based learning implies that a learner needs to be a
good knowledge networker as well as a good double-loop learner. A good
knowledge networker is one who can:
· Create, harness, nurture, sustain,
and widen her external network to embrace new knowledge nodes.
· Identify connections, recognize
patterns, and make sense between different knowledge nodes.
· Locate the knowledge node that can
help achieving better results, in a specific learning context.
· Aggregate and remix.
· Cross boundaries, connect, and
cooperate.
· Navigate and learn across multiple
knowledge networks.
· Help other knowledge networkers
build and extend their networks.
Furthermore, a good double-loop learner is one who has
the ability to:
· Build her own representation of the
theories-in-use of the whole.
· Reflect.
· (Self-) criticize.
· Detect and correct errors with
norms and values specified by the new setting.
· Inquire
· Test, challenge, and eventually
change her theories-in-use (i.e. her private image of the theories-in-use of
the whole) according to the new setting.
LaaN-based
learning also implies new roles for the learning institution and the teacher.
In LaaN, the learning institution needs to act as a hub connecting third
parties providing personalized learning experiences to the learners. And,
teachers need to step back from their traditional role of instructors and experts.
The new role of the teachers is to act as co-learners and facilitators of the
learning experience. Their major task is to help learners build their PKNs in
an effective and efficient way, by providing a freeform and emergent
environment conducive to networking, inquiry, and trial-and-error; that is an
open environment in which learners can make connections, see patterns, reflect,
(self)-criticize, detect and correct errors, inquire, test, challenge and
eventually change their theories-in-use.
At
the heart of LaaN lie knowledge ecologies.
A knowledge ecology is based on the concept of PKNs, loosely joined, and can be
defined as a complex, knowledge intensive landscape that emerges from the
bottom-up connection of PKNs. The definition of knowledge ecology is highly
compatible with complexity theory. As with complex adaptive systems, a knowledge
ecology holds emergent properties, includes self-organized entities,
and can evolve in ways that we may not expect or predict. Knowledge
ecologies blur the boundaries of learning and harness the power of PKNs. They
house self-directed learning that occurs in a bottom-up and emergent manner,
rather than learning that functions within a structured context, of an
overarching framework, shaped by command and control.
Based
on the PKN and knowledge ecology concepts, the next sections outline the major
differences between LaaN and different dominant learning and social theories.
These theories are behaviorism, cognitivism, (social) constructivism, situated
learning, activity theory, and actor-network theory.
LaaN and Psychological
Learning Theories
Learning
has traditionally been the province of psychological theories: behaviorism, cognitivism, and constructivism
(Wenger, 1998).
Behaviorist
theories focus on behavior modification via stimulus-response pairs and
selective reinforcement (positive and negative reinforcement). Their pedagogical
focus is on control and adaptive response (Skinner, 1974) (as cited in Wenger,
1998, p. 279). Behaviorists define learning as a change in behavior in the
learner, and see the mind as a "black box" in the sense that all
behavior can be explained without the need to consider internal mental states
or consciousness. The goal of instruction for the behaviorist is to elicit the
desired response from the learner who is presented with a target stimulus
(Ertmer & Newby, 1993).
Cognitive
theories focus on internal cognitive or mental structures and view learning as the
transformations that occur within them. They address the issues of how information
is received, organized, stored, and retrieved by the mind (Ertmer & Newby,
1993). Unlike the behaviorists, who were only concerned with behavioral
responses and what learners do, cognitivists are interested in what learners
know and how they come to acquire knowledge (Jonassen, 1991). The goal of
instruction for the cognitivist is to make knowledge meaningful and help
learners organize and relate new information to existing knowledge in memory
(Ertmer & Newby, 1993).
The
class of behaviorist and cognitivist learning theories has been referred to as
objectivism (Jonassen, 1991). In the literature dealing with learning theories
and instructional design, objectivism (i.e. behaviorism/cognitivism) has often
been contrasted with constructivism, which holds that "knowing is a
process of actively interpreting and constructing individual knowledge
representations" (Jonassen, 1991, p. 5). Constructivist theories focus on
the processes by which learners build their own mental structures when interacting
with an environment. Their pedagogical focus is task-oriented. They favor
hands-on, self-directed activities oriented toward design and discovery
(Piaget, 1954; Papert, 1980) (as cited in Wenger, 1998, p. 279).
Objectivism
(i.e. behaviorism/cognitivism) and constructivism, as psychological theories,
assume that learning occurs inside a person (Siemens, 2005a) and view knowledge
as a property and possession of an individual mind. Sfard (1998) points out that
the objectivist and constructivist processes can be conceptualized in terms of
the acquisition metaphor, where learning is mainly a process of acquiring and
accumulating desired pieces of knowledge. The author further notes: "The
language of ’knowledge acquisition’ and ’concept development’ makes us think
about the human mind as a container to be filled with certain materials and
about the learner as becoming an owner of these materials" (Sfard, 1998,
p. 5). Instead of knowledge residing only in the mind of an individual, LaaN
focuses on knowledge as both intrinsic and extrinsic. In LaaN, knowledge does
not only reside in the mind but also in a distributed manner across a network
(Downes, 2007, Siemens, 2005). In other words, psychological theories emphasize
knowledge as a thing/object that can be acquired or constructed, and the
individual mind as a container, whereas LaaN emphasizes knowledge as a personal
network, which provides the learner with the means needed to deal with
ill-structured topics/problems and to explore complex knowledge environments.
In LaaN, learning is learner-initiated knowledge networking rather than
knowledge acquisition, internalization, or construction.
LaaN and Social Theories
LaaN
shares a core proposition with dominant social theories, such as social
constructivism, situated learning, activity theory, and actor-network theory in
that knowledge and learning are fundamentally social in nature. However, the
LaaN view of learning as a continuous creation of a PKN is quite distinctive.
LaaN vs. Social
Constructivism
Social Constructivism is a theory of learning based upon
the learners’ social interaction and collaboration. Social constructivist
theorists (e.g. Vygotsky) have extended the traditional focus on individual
learning to address collaborative and social dimensions of learning. Whereas
Piaget’s cognitive constructivism focuses on the individual mind, Vygotsky’s
social constructivism conceptualizes learning as more socially constructed.
A
central concept in Vygotsky’s social constructivism is the ’zone of proximal development’ (ZPD), which highlights the
potential for future learning which can be achieved with appropriate support.
Vygotsky (1978) distinguishes between two developmental levels: The level of actual development is the level
of development that the learner has already reached, and is the level at which
the learner is capable of solving problems independently. The level of potential development is the
level of development that the learner is capable of reaching under the guidance
of teachers or in collaboration with peers. The learner is capable of solving
problems and understanding material at this level that they are not capable of
solving or understanding at their level of actual development. Vygotsky (1978)
further defines the zone of proximal development (ZPD) as "the distance
between the actual developmental level as determined by independent problem
solving and the level of potential development as determined through problem
solving under adult guidance or in collaboration with more capable peers"
(p. 86).
LaaN
differs from social constructivism in four different ways. First, the theory of
social constructivism suggests that learners co-construct knowledge. However,
the usage of the word “construction” implies that knowledge is a robust and
durable object. As Latour (2005) states: "using the word ’construction’
seemed at first ideal to describe a more realistic version of what it is for
anything to stand. And indeed, in all domains, to say that something is constructed
has always been associated with an appreciation of its robustness, quality,
style, durability, worth, etc" (p. 89). Robustness and durability however,
do not apply to knowledge. Knowledge is much more varied and uncertain. As
Siemens (2005) stresses: “In today’s world, knowledge life is short; it survives
only a short period of time before it is outdated”. In LaaN, knowledge is a
personal network rather than an object that can be constructed.
Second,
although social constructivism takes social interactions into account, it still
sees learning as essentially intrinsic (i.e. in the learner’s mind). In fact,
the primary focus of Vygotsky’s social constructivism is to determine the state
of a learner’s mental development by clarifying its two levels: the actual
developmental level and the ZPD. The actual developmental level of a learner
indicates her actual mental abilities and the ZPD represents her potential
mental development. As Vygotsky (1978) puts it: "The actual developmental
level characterizes mental development retrospectively, while the zone of proximal
development characterizes mental development prospectively" (p. 86). The
focus on the mental development of the learner makes thus from social
constructivism a learning theory with a primarily psychological perspective.
Unlike social constructivism, which views learning as internal developmental
processes that result in mental development (i.e. intrinsic), LaaN views
learning as both intrinsic and extrinsic; it occurs through the continuous
building of personal knowledge networks, at both internal/conceptual and
external levels.
Third,
Vygotsky’s social constructivism has centered on the role that adults play in
fostering children’s development. ZPD, which is closely related to the concept
of "scaffolding",
emphasizes that learning occurs best when an expert (either an adult or a more
competent peer; aka the More Knowledgeable Other, MKO) guides a novice from
their current level of knowledge to the level of knowledge they are capable of with
assistance. However, nowadays the lines became blurred between the expert and
novice roles. At each moment the novice can move into the expert role and vice
versa. In LaaN, everyone is a knowledge networker and can thus act as a novice
in one context and step into the expert role in another context. The bridge between
where the learner is and where she is going is through her personal knowledge
network, rather than in the hands of a teacher or a more competent peer.
Fourth,
the notion of ZPD enables learning, which is oriented towards a level of
potential development, embodied in the adult or more competent peer. Knowledge however,
is complex and the level of potential development can thus never be anticipated
or predicted. In LaaN, learning takes place in a knowledge ecology rather than
within a ZPD. Unlike a ZPD which is characterized by a rigid, restrictive, and
unidirectional development (i.e. training the novices within the ZPD towards a
level of potential development), a knowledge ecology is open, multidirectional,
and without an end point of development. The boundaries of knowledge ecologies
are less fixed and can easily be bridged and merged. And, the development
within a knowledge ecology is never fully predetermined and occurs in
unpredictable directions.
LaaN vs. Situated
Learning
Situated learning is a model of learning first
introduced by Jean Lave and Etienne Wenger in 1991. This model proposes that
knowing and learning involve a process of engagement in a community of practice
(CoP). As Lave & Wenger (1991) write: "Knowing is inherent in the
growth and transformation of identities and it is located in relations among
practitioners, their practice, the artifacts of that practice, and the social
organization … of communities of practice" (p. 122). Rather than looking
to learning as the acquisition of certain forms of knowledge, Lave & Wenger
(1991) explore the participation metaphor of learning in which learning is a matter
of legitimate peripheral participation (LPP) within a CoP. According to Lave
and Wenger, in a CoP, a newcomer learns from old-timers through participating
in certain tasks that relate to the practice of the community. Over time the
newcomer moves from peripheral to full participation.
Wenger (1998)
revised his earlier work (Lave & Wenger, 1991) to offer a social account of
learning through the negotiation of meaning and identity formation within CoPs.
While Wenger does not ignore legitimacy and peripherality, it is participation
that he extracts as being crucial to the revised notion of a CoP showing it to
be the key constituent in the processes of the negotiation of meaning.
According to Wenger (1998), participation refers to "a process of taking
part and also to the relations with others that reflect this process. It
suggests both action and connection" (p. 55). Wenger stresses that learning
is social participation. He further explains that any CoP will then produce
artifacts, which reify some aspect of its practice, and refers to this process
of giving form to the experience as reification.
Within
LaaN, the notion of legitimate peripheral participation (LPP) - which is very
close to Vygotsky’s ZPD - is absent. In LaaN, role models are not strictly
defined. There is no distinction between "newcomers, novices, or
peripheral participants" and "old-timers or masters". Every
participant is equally treated as a knowledge networker. Unlike CoPs, which are
characterized by a single movement from the periphery to the center, in a
knowledge ecology, the center does not hold and the movements occur in
unpredictable directions.
Moreover, in Wenger’s social theory of learning, the
emphasis is on the CoP. As Wenger (1998) writes in the introduction of his
book:
Communities of Practice presents a theory of learning
that starts with this assumption: engagement in social practice is the
fundamental process by which we learn and so become who we are. The primary
unit of analysis is neither the individual nor social institutions but rather
the informal "communities of practice" that people form as they
pursue shared enterprises over time.
In LaaN, by contrast, the primary focus is on the
individual learner and her PKN. Knowledge development in LaaN is driven by the
learning demands of the learner, rather than the community in which she
belongs. In contrast to Wenger’s learning theory, where learning for an
individual is "an issue of engaging in and contributing to the practices
of their communities" (p. 7), LaaN views learning for an individual as an
issue of continuously building, maintaining, extending, and restructuring her
PKN.
Furthermore,
the social landscape is quite different within LaaN. A strong distinction can
be made between closed, bounded, structured, and hierarchical CoPs on the one
hand and open, distributed, diverse, emergent, and self-controlled knowledge
ecologies on the other hand.
Wenger (1998) discusses three dimensions of a CoP (p.
73):
1. How it functions (community): A mutual engagement
that bind members together into a social entity.
2. What it is about (domain): A joint enterprise as
understood and continually renegotiated by its members.
3.
What capability it has produced (practice): The shared repertoire of communal
resources (routines, sensibilities, artifacts, vocabulary, styles, etc.) that
members have developed over time.
A
knowledge ecology differs from a CoP on all these dimensions.
According
to Wenger, "the first characteristic of practice as the source of coherence
of a community is the mutual engagement of participants" (p. 73). It is
mutual engagement that binds members of a CoP together as a social entity and
enables them to define themselves as members of the CoP. Unlike a CoP, a
knowledge ecology is a social entity which has no clear boundaries and
membership criteria. It involves an emergent network of people not so tightly
bound as a CoP. A knowledge ecology is driven by independence and autonomy
rather than membership, mutual engagement, and belonging to a community. Rather
than being forced to interact intensely with other members of a CoP, within a
knowledge ecology, everyone can rely on her PKN. Often, people turn to their
personal relations in order to learn and get their work done, rather than
trying to get access to a well established community of mutual engagement.
Wenger further stresses that the kind of coherence that transforms mutual
engagement into a CoP requires work and asserts that "the work of
"community maintenance" is thus an intrinsic part of any
practice" (p. 74). In a knowledge ecology, however, people focus on
forming and maintaining their PKNs and sustaining dense relations with nodes in
their PKNs rather than maintaining the CoP to which they belong.
Wenger
states that "the second characteristic of practice as a source of community
coherence is the negotiation of a joint enterprise" (p. 77). According to
Wenger, a CoP involves organizing around some particular area of knowledge
(i.e. a shared domain of interest) that gives members a sense of joint
enterprise and shared identity. Membership in a CoP implies a commitment to the
domain and a continuous negotiation of a joint enterprise. A CoP is thus a
homogeneous social entity consisting of members with a joint enterprise and a
shared domain of interest. Unlike CoPs, knowledge ecologies are not bound by a
shared practice, a joint enterprise, or an overarching domain. They are open,
flexible, heterogeneous, and multidisciplinary social entities. In a knowledge
ecology, people continuously create their PKNs which shape their identity and
knowledge home, rather than create a shared identity through engaging in and
contributing to the practices of a CoP. Wenger further notes that
"communities of practice are not self-contained entities. They develop in
larger contexts - historical, social, cultural, and institutional - with
specific resources and constraints" (p. 79). Consequently, the practice of
a community is profoundly shaped by conditions outside the control of its
members due to external efforts to maintain influence and control over the
practice. In contrast to CoPs, knowledge ecologies are not positioned within a
broader system and are not bound to the control of any external force. They
emerge naturally without strong predetermined rules or external authority.
Knowledge ecologies are thus self-controlled and self-contained entities.
Wenger
notes that "the third characteristic of practice as a source of community
coherence is the development of a shared repertoire ... The repertoire of a
community of practice includes routines, words, tools, ways of doing things,
stories, gestures, symbols, genres, actions, or concepts that the community has
produced or adopted in the course of its existence, and which have become part
of its practice. The repertoire combines both reificative and participative
aspects" (pp. 82-83). In contrast to CoPs, knowledge ecologies lack a
shared repertoire and are thus open and distributed knowledge domains. The
knowledge resources are distributed over different PKNs within a knowledge
ecology. Unlike participation in a CoP, where the result is the development of
a community’s set of shared resources and practices, the result of
participation in a knowledge ecology is a restructuring of one’s PKN, that is,
a reframing of one’s theories-in-use (conceptual/internal level) and an
extension of one’s external network with new knowledge nodes (external level).
LaaN vs. Activity Theory
The
cultural-historical theory of activity (Activity
Theory) has grown out of the work of Vygotsky, Leont’ev and other Soviet socio-cultural
oriented psychologists. Activity Theory approaches human cognition and behavior
as embedded in collectively organized, artifact-mediated and object-oriented
activity systems (Vygotsky, 1978; Leont’ev, 1978; Engeström, 1987). According
to Engeström (1999b), an activity system "constantly generates actions
through which the object of the activity is enacted and reconstructed in
specific forms and contents - but being a horizon, the object is never fully
reached or conquered" (p. 381).
Activity
Theory develops from the work of Vygotsky, particularly his arguments that
human development and learning is mediated by artifacts, such as language,
signs, and symbol systems. The classical representation of an activity system
is a mediating triangle comprising three central components, namely subject,
object, and mediating artifacts (Vygotsky, 1978). Activities are social practices
oriented at objects. An entity becomes an object of activity when it meets a
human need. The subject constructs the object using mediating artifacts
(Engeström, 1999b). Leont’ev (1978), drawing on Vygotsky’s foundational work,
points out that there is a crucial difference between an individual action and
a collective activity and extends Vygotsky’s original model into a model of a
collective activity system. Leont’ev’s conceptualization includes division of
labour, which helps to differentiate between what is accomplished collectively
or individually. Leont’ev further adds a distinction between activity, action
and operation, as three different levels of human practice in order to
delineate an individual’s action from the collective activity (Leont’ev, 1978,
Section 3.5).
Inspired
by Leont’ev’s work, Engeström (1987, 1999a) also notes that the problem with
Vygotsky’s classical representation of an activity system is that it does not
fully explicate the societal and collaborative nature of the human actions. He
then graphically expands Vygotsky’s original mediating triangle with a social
component by including three contextual factors, namely community, rules, and
division of labor. Engeström uses this expanded activity system model as the
basis for his theory of expansive learning, which focuses on the expansive
transformation of the object of activity in a collective activity system
(Engeström, 1987, 1999b).
Engeström
(1987) conceives the notion of the ‘zone of proximal development’, initially
discussed by Vygotsky, as the cornerstone of expansive learning. Within an
expansive learning framework, Engeström (1999b, 2001) presents the notion of
’expansive cycle’ as the equivalent of Vygotsky’s zone of proximal development.
As Engeström (2001) puts it: “a full cycle of expansive transformation may be
understood as a collaborative journey through the zone of proximal development
of the activity” (p. 137). Engeström traces seven expansive learning actions to
be taken in traveling through the zone of proximal development of an activity.
Together these actions form an expansive cycle or spiral. According to
Engeström (1999b), an ideal-typical sequence of actions in an expansive cycle
includes (p. 383):
1. questioning, criticizing, and rejecting some aspects
of the accepted practices,
2.
analyzing the situation,
3. modeling of a new solution to the problematic
situation,
4. examining the model,
5. implementing the model,
6. reflecting on and evaluating the process,
7.
consolidating its outcomes into a new, stable from of practice.
However, in the new knowledge intensive era, it is increasingly
evident that knowledge is highly complex and that dealing with knowledge is
definitely not reducible to any sequence of actions. The actions which form
Engeström’s expansive learning cycle are not the only kinds of actions that
must be mastered and performed in highly complex knowledge ecologies. LaaN does
not postulate a predetermined sequence of actions; it rather enables a wide
range of learner-driven actions that are neither predetermined nor predictable.
In
general, using Activity Theory as a framework for the analysis of activity in
complex learning environments has a major limitation. Learning as a complex
activity cannot be captured by an overarching activity system (or even a
network of activity systems) purposefully oriented toward the achievement of an
object of activity. Learning is multifaceted and dynamic, and activities in a
learning environment are fuzzy, varied, and often fragmented, which makes it
very hard to elicit a complete picture of the activity system(s) under
observation, encompassing, in activity theory terms, an evolving set of
subjects, objects, mediating artifacts, actions, rules, norms, and division of
labour. The solution to this problem is to understand the learning activity
from the learner’s point of view. Whereas in Activity Theory the prime unit of
analysis is an artifact-mediated and object-oriented activity, LaaN rather
focuses on the individual learner and her PKN. In activity theory, you are what
you do. In LaaN, you are what your PKN is.
LaaN vs. Actor-Network
Theory
Actor-network theory (ANT), also known as the sociology of
translation or sociology of associations, proposes a socio-technical account
that makes no distinction in approach between the social, the natural and the
technological (Latour, 1996, 2005; Law, 1992). ANT is based upon the principle
of generalized symmetry employing a
single conceptual framework when interpreting actors, human and non-human.
ANT
has several limitations to be applied as a framework for dealing with complex
learning environments. ANT explores the ways that heterogeneous networks of
both human and non-human actors are constructed and maintained and focuses on
tracing the transformation of these heterogeneous networks (Latour, 2005).
Central to ANT is the concept of translation,
which is the process of creation of an actor-network and generation of ordering
effects Law (1992). The main problem of ANT’s heavy emphasis on the
construction, maintenance and transformation of actor-networks (i.e.
translation) is that it reduces all actors into black-boxes, and thus ignores
internal actions which are crucial for the creation of PKNs, and hence
learning, such as seeing patterns, reflecting, (self-)criticizing, and
detecting/correcting errors. These actions build the cornerstones of LaaN.
Another
limitation of ANT is that it does not distinguish between complex and
complicated systems. ANT, particularly in Latour’s work, refuses to make the
distinction between human and non-human actors i.e. between complex and
complicated entities (Williams, 2007). ANT’s strong emphasis is on the
heterogeneous nature of the actor-networks. Latour (2005) stresses that, in
ANT, non-human actors, similar to human-actors, are treated as mediators rather
than intermediaries. Latour makes a strong distinction between intermediaries
and mediators. He writes:
For intermediaries, there is no
mystery since inputs predict outputs fairly well: nothing will be present in the effect that has not
been in the cause ... For mediators, the situation is different: causes do not allow effects to be deduced as
they are simply offering occasions,
circumstances, and precedents. As a result, lots of surprising, aliens may pop up in between. (pp. 58-59)
Treating
non-human actors as mediators makes from them complex entities where cause and
effect are intertwined and cannot be separated. Non-human actors, however, are
rather complicated entities - or in Latour’s terms intermediaries. All the components
of a non-human actor are knowable and cause and effect relationships can be
predicted. In LaaN non-human "actors" are just an enabler. At the
heart of LaaN lie individual learners and their PKNs.
Another
point where it also becomes clear that ANT does not make a distinction between
the complex and the complicated is the ANT’s concern with the way in which the
social is constantly reconfigured, or in Latour’s words ‘reassembled’ (Latour,
2005). Law (1992) stresses that the core of the actor-network approach is
a concern with how actors and
organisations mobilise, juxtapose and hold together the bits and pieces out of which they are composed ... and so turn a
network from a heterogeneous set of bits
and pieces each with its own inclinations, into something that passes as a punctualised actor. (p. 386)
Latour
(2005) uses the verb reassemble to describe the same effect. However, in the
new knowledge intensive era, the relationship between different knowledge nodes
or in Law’s terms ‘heterogeneous bits and pieces’ is becoming flexible and is
changing rapidly; thus, it cannot be captured through a reconfiguration
process. Reconfiguration, or in Latour’s terms reassembling, works well for
complicated systems in which different components and associated relationships
can be identified and managed. Reconfiguration, however, cannot work while
dealing with complex knowledge systems comprising many interacting identities.
In the latter case, networking is the solution. In complex knowledge systems,
the way the knowledge nodes network with each other results in unpredictable
movements in the knowledge ecology. Knowledge ecologies lie at the heart of
LaaN.
Furthermore,
Latour (2005) claims that "it’s possible to render social connections
traceable" (p. 16) and that the role of ANT is to trace actor-networks (p.
128). In complex knowledge systems, however, there is no chance to trace social
connections, nor is it possible to follow the actors or their actions. Latour
himself acknowledges that following the actors themselves is not an easy task
since, as he writes, "the actors to be followed swarm in all directions
like a bee’s nest disturbed by a wayward child" (p. 121). Thus, there is
no means to trace actors’ actions and connections because their actions are uncertain,
unexpected, and often hidden; and their connections are varied, ubiquitous, and
open. The role of LaaN is neither to follow actors nor to trace their actions
or connections. Its major role is to help learners build and nurture their PKNs
and to foster connections between different PKNs in order to form a complex,
adaptive, dynamic, open, and living entity; i.e. a knowledge ecology.
Learning
Theories Compared
Schunk (1991, as cited in Ertmer & Newby, 1993, p.
53) emphasizes five definitive questions to distinguish each learning theory
from the others:
1.
How does learning occur?
2.
Which factors influence learning?
3.
What is the role of memory?
4.
How does transfer occur? And
5. What types of learning are best explained by the
theory?
Based
on these definitive questions and other dimensions (i.e. focus, core activity,
learner’s role, underlying social entity and its characteristics), Table 1 and
Table 2 summarize how LaaN differs from the learning and social theories outlined
in the previous sections. The stress here that different
learning theories cannot be considered in isolation. They complement and
enhance each other. They interplay with each other, but cannot replace each
other. In fact, many the assumptions that underlie LaaN are compatible with
those of other theories. LaaN is thus not a replacement for other theories that address different
aspects of learning but rather that it has its own focus and explains specific
types of learning. The primary focus of LaaN is mainly on the learner and her
PKN and complex network learning is the type of learning best explained by
LaaN.
