Unfolding perspectives on networked
professional learning: Exploring ties and time
Maarten de Laata,
Jan-Willem Strijbosb
a Open
University of the Netherlands, Welten Institute, the
Netherlands
b
Ludwig-Maximilians-University of Munich, Department of
Psychology, Germany
Article received 26
June 2014 / revised and accepted 27 June 2014 / available
online 15 July 2014
Abstract
Networked
learning and learning networks are commonplace concepts in
most contemporary discourse on learning in the 21st century.
This special issue provides a collection of studies that
address the need for a growing body of empirical work to
extent the limited understanding of the use and benefits of
networks in relation to learning and professional development.
In this article we attempt to offer a synthesis of the studies
presented in this special issue and reflect on their findings.
The studies in this issue present a rich combination of
networked professional learning research addressing issues
related to the composition and structure of learning networks,
their content and activities, showing how multi-faceted
research in the field of networked learning really is. Based
on the findings and methods used in the articles in this
issue, we articulate some recommendations for further
research. The recommendations are focused on the need for
advanced multi-level analysis to understand the complexity of
learning ties, the need for employing a multi-method research
approach to triangulate and contextualize findings, the need
to conduct process and time-based analysis and finally the
need to further develop a theory and toolkit for applying
Social Network Analysis in the context of networked learning.
Keywords: Networked professional learning, Networked
learning, Professional development, informal-formal learning.
1.
Introduction
The domain of networked
learning research has been around for some time. The term has
been used predominantly in the UK, where the research by
Steeples and Jones (2002) and Goodyear, Banks, Hodgson and
McConnell (2004) played a central role in the early days.
Originally there was a strong focus on higher education, but
nowadays a networked learning approach to understanding learning
practices has extended to include learning in formal, non-formal
and informal settings (Hodgson, De Laat, McConnell, &
Ryberg, 2014).
According to Hodgson et al.
(2014), networked learning refers to
learning through connections between learners, learners and
their tutors, and a learning community and its learning
resources. Within networked learning, learners have always
been seen as proactive and engaging agents. Many contemporary
perspectives on networked learning derive from critical and
humanistic traditions (Dewey, 1916; Freire, 1970; Illich,
1971; Mead, 1934) positing that learning is social, takes
place in communities and networks, is a shared practice,
involves negotiation, and requires dialogue (Hodgson,
McConnell, & Dirckinck-Holmfeld, 2012). Often, digital
technology is used to support networked learning processes
(Goodyear et al., 2004).
The field of networked
learning aims to understand the pedagogical values and beliefs
underpinning networked learning in order to advance teaching and
learning practices and the design of technologies supporting
such practices. The focus is on understanding how relations
between learners influence teaching and learning in physical,
online and/or blended settings. This special issue is an example
of how a networked approach to learning has spread beyond
education, since all the articles address questions around
professional development, in this paper termed “networked
professional learning”.
This special issue forms an important and timely collection of articles especially because there is a strong interest in the promise and value of networked professional learning. There is considerable consensus that professionals organise and carry out their own professional development effectively through their own social networks and communities (Cross & Parker, 2004; Duguid, 2005; Hargreaves & Fullan, 2012; Weinberger, 2011; Wenger, 1998). However, we lack empirical evidence about people’s specific networked learning experiences. In particular, it is not well-understood how professionals build and maintain networked connections for learning, what the composition of these networks is, whether and how these learning relationships create value, and how to assess the outcomes of learning through networks in the context of professional development. Research brought together in this special issue advances our understanding of networked professional learning, allowing us to reflect on and contribute to networked learning theory and helping us to develop and facilitate networked learning in practice. Each article investigates learning from a relational point of view, in formal, informal or mixed settings. This final article offers a reflection on the unfolding perspectives and research presented by the articles collected in this special issue.
2.
Exploring networked professional learning
The articles in this
special issue are focused on understanding how social networks
influence and impact professional development in networks and
communities. Vaessen, Van den Beemt and De Laat (this issue)
present a conceptual literature review to uncover some
underlying mechanisms and factors that influence usage of
networked learning in the context of teacher professional
development. They explicitly explore the tension between formal
and informal learning. They argue that the increased complexity
of work requires professionals to use their networks to access
and/or develop knowledge and expertise to stay up to date and
function successfully. Understanding the role and impact of
these informal social networks on professional development can
foster a better relationship – if necessary – with the
traditional, yet dominant, formal professional development
activities informed by acquisition and transfer of knowledge via
expert-driven, pre-planned courses. Vaessen et al.’s literature
review provides a broader framework for understanding
professional development through participation in social
networks, setting the context for the other articles in the
special issue that examine particular aspects of “networked
professional learning” in greater detail.
Pataria, Falconer,
Margaryan, Littlejohn and Fincher (this issue) investigate
academics’ learning through their personal professional
networks. Pataraia et al. build on Roxå and Mårtensson’s (2009)
research on teacher networks in academic contexts, focusing on
conversations about teaching. More specifically, Pataria et al.
examined whether the composition of personal networks (i.e., the
proximity of people with whom one is connected) and
characteristics of interactions in these networks (i.e., what is
exchanged and how it is valued) may support change of teaching
practice in universities. This important descriptive research
showed that the networks of academics were small,
discipline-specific and strongly localised. Based on data from
interviews from two studies, they conclude that the academics
interacted most frequently with closely proximate colleagues,
typically from the same discipline. These findings support the
notion that homophily (degree of similarity) influences
establishment of ties and the development of networks.
Hytonen, Palonen and
Hakkarainen (this issue) investigated network patterns and
structures that contribute to professionals’ cognitive
centrality within a network. The context of their study was a
professional training course in the field of energy efficiency.
Cognitive centrality was based on ties that represented who
people contacted for professional advice over the course of
twelve months; as such the networked ties constitute the product
of the networked learning. More specifically, Hytonen et al.
examined the central actors within the network and their
learning connections in order to identify possible factors that
could explain cognitive centrality. Their study showed that
cognitive centrality is influenced by several factors, such as
personal characteristics, expertise, and organisation that the
actor represents, but a single decisive factor could not be
found. These findings emphasise the complexity of social
learning, suggesting that learning is highly contextualized and
situated.
Rehm, Gijselaers and Segers
(this issue) examined the transferability of knowledge in
relation to the hierarchical network positions of members of an
online community of learners during a professional development
training program. Rehm et al. addressed the notion that
participants’ hierarchical positions within the organization can
have an effect on the collaborative processes within communities
of learning. They showed that higher in- and out-degree and
centrality scores were associated with higher hierarchical
positions within the organization. Their longitudinal analysis
indicated that these trends were established relatively early on
during the professional development programme.
The studies present a rich
combination of networked professional learning research
addressing issues related to the composition and structure of
learning networks, their content and activities, showing how
multi-faceted research in the field of networked learning really
is. Based on the findings reported and methods used, the
following sections articulate some recommendations for further
research.
3.
Unfolding networked professional learning
The articles in this
special issue provided us with snapshots of networked
professional learning and details about the constitution of the
learning networks in a variety of contexts. Combined these
articles challenge the naïve view that large(r) networks with
many ties, or very elaborate networks with many ties of a
specific type (e.g., weak vs. strong), are better and/or
preferable by default. Needless to say professionals take part
in and maintain many networked relationships, but in essence
networks are always about something, focused on a particular
problem or shared interest. The “whole might be greater than the
sum”, but the merit of the research presented in this special
issue is to understand how particular (sub)networks or networked
activity that professionals take part in contribute to their
learning. For example, Pataraia et al., and to some extent also
Hytonen et al., clearly show that professionals maintain many
networked relationships with a variety of people for a number of
reasons. Done from an ego perspective, this work shows that
professionals use their relations for exchanging information and
ideas, to talk about work-related problems and to seek advice.
Rather than focussing on the impact and effects of networking in
general it is very important to understand in great detail “what
goes on in particular networks” and see how participation in
networks affects learning.
Although the articles
present findings at different levels of analysis and network
scale (ego-personal network, sub-network, and/or whole network),
there are interesting connections between the findings of these
different studies to be reflected upon. In the following
synthesis we will try to address these differences in theory,
method and network scale and explore if and how these different
levels can be connected.
Boud and Hager (2012)
highlighted the importance of uncovering the ways in which
people participate in social settings (networks) through which
they seek to co-create knowledge and become a better
professional. All articles in this issue take a social
perspective on learning. In reflecting on professionals’ network
positions and the role of these networks in the learning
processes, these studies draw on the “participation” metaphor of
learning (as opposed to the acquisition metaphor, see Sfard,
1998, for an elaborate discussion on these metaphors). While
Rehm et al. concentrate on the transfer of knowledge amongst
members of a community of learners, they too position
professional learning as a process of collaborative knowledge
creation in social networks.
Social participation and
network building is predominantly seen as an informal activity
promoted, for example, through professional autonomy (Cross
& Parker, 2004; Kessels, 2012). However, Vaessen et al.
specifically argue that the underlying mechanisms for networked
learning are found in both formal and informal settings. They
further argue that networked learning is the most effective when
located within work practices. In the workplace, learning is
collaborative and situated within social relationships.
Networked learning is most effective in work settings in which
professionals have high levels of autonomy, trust, openness and
accountability and where these is an organisational culture of
management promoting collaboration, discursive and open
communication, and bottom-up learning and change.
The findings presented by
Vaessen et al. are to some extent reflected in the studies by
Pataraia et al., Hytonen et al., and Rehm et al., but criticised
as well. For example, the finding by Pataraia et al. that
academics’ teaching networks were small, discipline-specific and
strongly localised reveals that the establishment of connections
with others is influenced by proximity, homophily, as well as
perceived relevance and anticipated value of these connections.
Pataraia and colleagues’ empirical data seems to suggest that
academics’ teaching networks are predominantly formed around
strong ties. In a similar vein, findings by Hytonen et al. show
that cognitive centrality of core participants is affected by a
multitude of factors, including personal characteristics (e.g.,
expertise, engagement), openness, and their organisational
background. Finally, Rehm et al. show that characteristics of
the formal work setting – i.e. people’s hierarchical position –
influence interaction patterns in an informal setting. The
findings are in line with Vaessen et al. in the sense that a
similar structure (hierarchy) in the formal setting affects
networked learning ties in the informal setting, but not
necessarily as intended (although Rehm et al. do not comment on
this aspect).
Rehm et al. concluded that
more senior professionals could draw more actively upon the
input of colleagues allowing less senior participants to
gradually move towards the centre of a network. Likewise,
Vaessen et al. concluded that the network(s) transcend
organisational boundaries, while Rehm et al. indicate that this
process may also benefit from some facilitation and/or
intervention. Both agree that management may need to promote
networked learning by opening up organisational structures where
management and community members can learn together.
Finally, Vaessen et al.
indicate that informal networks thrive in open practices, in
which strong and weak ties co-exist (Granovetter, 1973). Such
open network practices and “culture of learning” that is
facilitated and promoted by the management appear especially
relevant for professional learning (Price, 2013). Open practices
consist of networks that are collections of individuals across
organisational, spatial and disciplinary boundaries, who come
together to create and share a body of knowledge (De Laat,
Schreurs, & Nijland, 2014). Open networks focus typically on
developing, distributing and applying knowledge (Pugh &
Prusak, 2013). Open network members connect around a common goal
and share social and operational norms. They typically
participate out of common interest and of a shared purpose
rather than because of contract, quid pro quo or hierarchy. They
are not bound or confined by shared identities and knowledge and
meaning is not retained in the way in which it is done in
communities of practice. The relationship between the members is
much more loose and dynamic, yet effective in the creation of
new ideas. Open network practices offer professionals a more
dynamic platform to connect with relevant peers who can help
them to stay up to date than communities of practice do. A
further feature of such open network practices is that they are
self-directed and non-hierarchical. Wellman’s (2002) notion of
networked individualism emphasizes the point that professionals
have a great ability to act on their own, to solve their
problems and organise their lives, but they do this in a
networked way with the help of friends and other relationships.
The diversity of sources in a professionals’ network is also
echoed in the findings by Pataraia et al. and Hytonen et al.
Although rather implicitly, the articles in this special
issue suggest several avenues for further research. In the next
subsections we will discuss some directions for further
research.
3.1
Need to clarify the “what” and “why” of “learning tie”
There is a clear need to
develop theoretically-based and differentiated qualifications of
the meaning of a tie, that is to investigate the “what” and
“why” of a tie. In this special issue, Pataraia et al., Hytonen
et al. and Rehm et al. explicitly unfold the meaning of a tie.
Pataria et al. and Hytonen et al. focused on both the “what”
(content of a tie) and “why” (explanation for a tie or structure
of personal/ego-network or the entire network), whereas Rehm et
al. focused only on the “why”.
Furthermore, networked
learning ties can be treated both as relations that connect
people as well as outcomes of relations (Haythorntwaite & De
Laat, 2012). In the first instance, the tie refers to relational
ties used for learning, such as a student learning from a
teacher, students or professionals learning from peers, or
novice professionals learning from experts. An example of
networked learning ties as outcomes is when a group collectively
acquires a competence in a certain domain that helps them to
deal with new situations. As relational ties can represent both
the process and the product of learning, there is a clear need
to separate them or at least treat each tie as a compound
construct consisting of several layers of process and product
components. For example, at the individual level, a tie may
consist of 30% on-going learning activities, 40% current project
work, 20% personal bonds, and 10% status. A multi-layered
perspective on ties allows for (multilevel) multiple regression
approaches to understand the multifaceted nature of ties. The
conceptualisation of ties as multi-layered constructs also opens
up new directions regarding how these ties can be afforded,
fostered, and facilitated through social interaction, design for
learning, and technology.
3.2
Need to examine networks at multiple levels and the
interplay between levels
Combined the articles in
this special issue cover all levels of scale possible. Pataria
et al. investigated the individual level in terms of academics’
personal teaching networks and the characteristics of their
interactions with colleagues. In a slightly different way,
Hytonen et al. examined the individual level to understand both
the structure and heterogeneity of central participants’
personal networks. They analysed the entire network to identify
which other participants (alters) connected to the
cognitively-central actors and to examine the associated network
clusters and the degree of collaboration within these. Finally,
Rehm et al. investigated networks as communities of learners,
adopting a whole network analysis approach to explore the
positions within these online communities based on participants’
rank and hierarchical position within the organization. Although
these articles cover the range of possible levels – personal
(ego), sub-network (community or larger cluster) and entire
network – the explicit comparison or investigation of the
interplay between various levels was not attempted. It is
conceivable, for example, that an individual’s personal network
may be low in density, yet the individual may hold a key
brokering position in the entire network. Examining the
interplay between levels might be a promising direction in
future research in professional networked learning. Referring
back to the issue of the “whole being greater than the sum”,
research on the interplay of levels will help to uncover how to
potentially assess the nature of learning ties for the
individual, a particular network and the organization. Within
human resource development (HRD) – especially from a formal
management point of view – there is interest in monitoring and
assessing networked learning in order to validate and award it.
The immediate response seems to be on trying to assess networks,
similar to registration of attendance of professional
development programmes, rather than focussing on the value that
is created through networks and communities (Wenger, Trayner,
& De Laat, 2011). Multi-level research on the value of
learning ties can help assess the outcome of networked
professional learning in relation to different stakeholders.
3.3
Need for extending the methodological toolkit
As there are different ways
to conceptualise learning ties, there are different analysis
techniques to study them. For example, who learns from whom,
what do learners learn from each other, the kinds of
interactions between learners, the direction of ties, flow of
resources, and the frequency of interactions. Several of these
aspects are related to communication and information patterns,
whereas others directly deal with learning itself. Networked
learning studies often address these aspects, however some
reflection on how we may be more critical and cautious about the
way in which network analysis is used to understand learning
ties is required.
A popular method for
studying networked (professional) learning is the use of Social
Network Analysis (SNA). Two studies in this issue applied SNA
(Hytonen et al.; Rehm et al.) to understand the network
structures or dynamics. SNA has become rather popular for trying
to understand learning ties, but we have to remain cautious
about its application. SNA was developed to understand for
example the flow of information or communication across networks
– i.e., more factual data. If person A passes something on to
person B, traditional SNA assumes that person B has received it.
However, when learning is concerned this assumption may not
hold. First, the extent to which whatever was passed on was
received may be uncertain. Second, the contribution of
information to the actual learning process of the receiver is
uncertain. Hence, the network theory or operationalization of
indicators behind the tests that researchers conduct may have
different implications. Does density in a communication network
imply the same as density in a learning network? What does the
shortest path mean in terms of “learning”? Are all SNA
indicators by default useful indicators of learning? A more
advanced theory of SNA is needed to guide studies on “Social
Network Learning Analysis” (SNLA). SNA is a very flexible
method, but it requires a solid theoretical framework to enable
interpretation of findings. In the absence of a solid
theoretical framework of learning through networks, researchers
rely on conceptualisation from related research domains. When
applying analysis techniques that reflect a different
theoretical orientation, researchers risk type I and II errors.
Furthermore, despite the ease with which network visualisations
can be produced from SNA, such visualisations should be
approached with more restraint when interpreting the network
structures.
Another approach would be
the application of multilevel analyses, discussed by Rehm et al.
(this issue). An example is the recent study by Eberle, Stegmann
and Fischer (2014), who investigated Legitimate Peripheral
Participation (a well-known construct introduced by Lave and
Wenger (1991) to describe learning processes in communities of
practice), in terms of support structures used to foster
newcomers’ participation. They applied a 2-level model, which
included 14 student councils (communities) and 68 newcomers.
They found that exposure time (duration of community membership)
and the support structure of “accessibility of community
knowledge” positively predicted the newcomers’ participation,
whereas community size and “recruitment strategies” negatively
predicted participation.
Finally, the instruments
and methods applied in the articles in this special issue
reflect that a multi-method approach is required to investigate
networked professional learning and obtain a more complete
understanding of the nature of “learning” reflected by the ties
and the indicators that SNA offers. A potential direction would
be the combination of SNA, content analysis of communication,
and a contextual analysis through interviews (De Laat &
Lally, 2003). The contributions by Pataraia et al. and Hytonen
et al. are examples of such a contextualised approach to
understanding network structures. Rehm et al. acknowledge that
their study would have benefitted from content analysis to help
uncover how the “what” of the tie might have impacted network
position and exchange of knowledge.
3.4
Need to examine networked learning over time
Over the past decade, the
issue of time has slowly developed into a more focal point of
research on interactive learning processes. An early
contribution in this respect is the work by De Laat and Lally
(2003), who identified changes in both interactive and tutoring
patterns within a community of learners, by distinguishing
between the early, middle and end phase of the community
experience. Similarly, the notion of time is receiving more
attention in the domain of (small) group collaborative learning,
where learning is studied longitudinally, in terms of sequences
of actions (Suthers, Dwyer, Medina, & Vatrapu, 2010),
specific timeframes such as days, weeks or months (Arrow, Henry,
Poole, Wheelan, & Moreland, 2005; Reimann, 2009), or in
terms of activities in a learning environment over time
(Schümmer, Strijbos, & Berkel, 2005).
Vaessen et al., Pataraia et
al., and Hytonen et al. implicitly refer to issues of time.
Vaessen et al. describe professional development as an “ongoing
process”, arguing that “networking skills need to be developed
over time”. Pataraia and colleagues’ data were collected over a
12-months time span. They argue that “the temporal component of
interactions determines the strength of ties”, and that the
networks are not only influenced by proximity and/or discipline,
but also have a “historical or temporal component”. Hytonen et
al. analysed data collected after a 12-months period following a
training programme. Their study focused on small group and
community level aspects, but the development of the networks of
cognitively central participants was not part of their aim. In
contrast, Rehm et al. explicitly adopted a longitudinal
analytical lens when analyzing reply structures in online
communities collected over a 14-week time span in terms of two
blocks of about six weeks. Their analysis showed that the more
central positioning of senior management was established
relatively early on and persisted – in fact slightly increased –
over time.
4.
Closing remarks
The articles comprising
this special issue have advanced our understanding of networked
professional learning. The empirical studies provided detailed
accounts of the structure and focus of networked learning at
various levels (ego, sub-network and whole network). They
improve our understanding of the characteristics of networked
learning and contribute to a much-needed empirical knowledge
base in this area of research. The literature review provided by
Vaessen et al. helps to broaden our horizon as well as situating
the findings of the other articles, by offering the mechanisms
that influence networked professional learning. The three
empirical studies, although addressing different levels of
scale, reinforce and supplement each other. For example, where
Pataraia et al. find that professionals maintain multiple
networks for their development, Hytonen et al. identify several
sub-networks centralized around key actors. The emergence of
these personal networks hinges on expertise, interest,
enthusiasm, competency, familiarity, organizational background,
as well as hierarchy and formal organizational relationships and
structures. Simultaneously the studies (implicitly) generated
some directions for future research that we elaborated upon: (a)
the need to clarify the “what” and “why” of “learning tie”, (b)
the need to examine networks at multiple levels and the
interplay between levels, (c) the need for extending the
methodological toolkit, and (d) the need to examine networked
learning over time.
The importance of
professional autonomy and cross-boundary collaboration that
seems to foster networked professional learning brings the
emergence of open practices into perspective. Both professionals
and organizations are increasingly becoming aware that knowledge
and innovation processes are not bounded by the organizational
context and that boundary crossing becomes an important aspect
of professional development. This raises further questions about
how to monitor, promote and assess networked professional
learning. The naïve view of “the more contacts the merrier” is
too simplistic. Studies in this special issue have shown that
important features and mechanisms of networks are personal
(probably small and localized), centralized around shared topics
interests and key members, driven by professional autonomy and
negotiate both informal and formal settings influenced by
hierarchical organizational structures. These findings shed some
light on how networks operate and create value, based on which
knowledge about how to facilitate and manage networked
professional learning can be inferred.
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