Interaction Patterns in Web-based Knowledge Communities:
Two-Mode Network Approach
Wouter Vollenbroek and Sjoerd de Vries
Behavioural Sciences, University of Twente, Drienerlolaan 5, Enschede, Netherlands
Keywords: Knowledge Management, Lifelong Learning, Social Network Analysis, Web-based Knowledge
Communities.
Abstract: The importance of web-based knowledge communities (WKCs) in the 'network society' is growing. This trend
is seen in many disciplines, like education, government, finance and other profit- and non-profit organisations.
There is a need for understanding the development of these online communities in order to steer it and to
affect the impact it has. In this research, we aimed to identify interaction patterns in these communities to
visualize and understand community developments, and show the relevance of WKCs for the development of
learning education. We conducted a content analysis and a network analysis on big social data to identify the
patterns in two Facebook-groups which were focused on educational development. Analysis of interaction
patterns enabled us to identify three interaction stages within WKCs in educational settings: introduction,
evolution and maturity. In the first stage, participants mainly introduce themselves. In the second stage, one
shares information and in the final stage, participants are more open to share their opinions. The study shows
that our network analysis approach is appropriate to analyse and visualize the development of interaction
patterns and the results could help us to steer communities effectively and efficiently.
1 INTRODUCTION
Individual and collective professionalization is
already a hot topic for decades (e.g. Sheng and Yao,
2004; Windahl and Rosengren, 1978). As Chugh
(2015) has indicated, there is – in our contemporary
society – still an enormous need for ways to create
and exchange tacit knowledge. One is thus still
looking for the holy grail concerning the successful
development of cooperative professional learning
practices. Multiple initiatives were taken to stimulate
this, some examples are communities of practice,
teams, and physical meetings. But the technological
advances has accelerated the developments in the way
we communicate, collaborate and share our
knowledge with others, this has resulted in a way of
working which is more efficient and effective and
which reaches a larger audience. The result is a
dramatically growing number of professionals who
share their resources, develop (new) working
strategies, solve (existing) problems, and improve the
individual-, the communal- and the organisational
performance (Tseng and Kuo, 2014). Web-based
knowledge communities (WKCs) exemplifies such
continuous professional development mechanisms. A
WKC is “a community that allows individuals to seek
and share knowledge through a website based on
common interests” (Lin et al, 2007). Especially in
educational institutions is the relevance of web-based
knowledge communities recognized by for example
teachers, pedagogics and instructional designers.
These professionals use it to improve their own
knowledge and skills, but also to realize a situation of
continuous educational development. In order to put
WKCs into perspective, we use the concept of
learning education. This phenomenon is defined as
“the learning landscape that facilitates the learning of
all its members within open educational networks and
continuously transforms itself in order to meet its
strategic goals by using the latest online
developments” (Vollenbroek et al, 2014). A learning
landscape consists of different elements and
professional learning practices, such as courses,
conferences, regulations and the web-based
knowledge communities.
In this study, we focus on the development of
web-based knowledge communities. Since these
online spots develop reasonable unstructured, it is
important to visualize and analyse it. In this study, we
make a first attempt by analysing interaction patterns
100
Vollenbroek, W. and Vries, S.
Interaction Patterns in Web-based Knowledge Communities: Two-Mode Network Approach.
DOI: 10.5220/0006035701000107
In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 3: KMIS, pages 100-107
ISBN: 978-989-758-203-5
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
and visualizing it by the two-mode network approach.
Interaction patterns represent the genre which
activates individuals to perform in a certain manner.
This leads to the following research question
which is central to this article: “What kind of
interaction patterns describe the development of an
online web-based knowledge community in an
educational context?” An answer to this research
question helps us to define the development of WKCs
based on interaction patterns and can be used to
monitor and steer the community development.
Furthermore, the method of analysis and visualization
of this development improves our methodological
understanding of approaches to analyse and visualise
these patterns.
2 MATERIAL AND METHODS
In this article, we analyse two specific professional
development cases which are embodied as web-based
knowledge communities. To describe these cases, we
have decided to use a mixed evaluation method,
which includes content analysis and network analysis
(SNA). The Excel-plugin NodeXL is used to
download and convert the data, and Gephi is used to
visualize and analyse it. The first case embodies an
interactive Facebook Group which belongs to a
Massive Open Online Course (MOOC), due to
privacy-issues we call this WKC: “Community of
Learning Innovation”. The second case is a Facebook
Group for the professional linguistic development of
individuals, we call this WKC: “Community of
Linguistic Innovation”. Initially the Community of
Learning Innovation is aimed as a discussion
environment for participants in the MOOC, this group
has for that reason no formally assigned community
leader. The Community of Linguistic Innovation has
been started by a group of linguistic enthusiasts. An
informal community leader takes the responsibility
for stimulating the members to interact. The
development of these public knowledge places offer
a unique opportunity for all educators to learn and
collaborate, despite their social and economic
background.
As said, the download phase has been conducted
with NodeXL, all likes and reactions on the Facebook
posts in the two cases were downloaded during three
periods described by Brown (2001) and Grossman et
al (2001). These periods were operated as a starting
point for the analysis of interaction patterns in the two
cases. We made some adjustments to ensure that the
phases better suit the short-term activities in these
communities. Nevertheless, the first period (4 weeks)
which was downloaded is defined as the introduction
phase. The central focus of this phase is the formation
of a group identity, in which teachers form a pseudo-
community with little interaction. The second phase
concerns the evolution stage (2 weeks). In this stage,
participants share thoughtful ideas. In the final stage,
after a relatively longer period of time and involving
intense association with others, one achieves
camaraderie (6 weeks).
In the convert phase, the social development
within these two cases was analysed using the
concepts central to the genre-theory described in the
work of Naaman et al (2010) to identify the
interaction patterns within two online WKCs in
Facebook, these interactions patterns were converted
by using NodeXL. In the convert stage, the messages
within the WKCs are coded with the genres described
by Naaman et al (2010). The genres introduced were
shown in Table 1.
Table 1: Genres.
Genre(s) Example(s)
Statements / Random
Thoughts
“It feels good to be
appreciated…”
Opinions “I would like to offer my
heartfelt congratulations
to…”
Information Sharing “For those of you who
missed <user>’s
Webinar…”
Introduction “Hello everyone. I am…”
Self-promotion “Please, read my blog
about…”
Me now “I’m watching the webinar
of…”
Question “Can anyone recommend
a nice, powerful, dynamic
and innovative tool for
presentations?”
Presence maintenance “Good morning all…”
Anecdotes (me) “My students mostly use
Moodle as a …”
Anecdotes (others) “<user> told me an
example of…”
When encoding the Facebook-nodes, we used
these genres as a base. For example, since Grossman,
et al (2001) suggested that online WKCs evolve with
a beginning phase where (new) members introduce
themselves, we have added a genre in which one
introduces them: ‘introduction’. Ultimately, the inter-
action patterns describe the development of WKCs
visualized through the various types of interactions
which occur.
Interaction Patterns in Web-based Knowledge Communities: Two-Mode Network Approach
101
In the visualization phase, we exported the
converted data from NodeXL into Gephi in order to
create relevant networks. These networks are visuali-
zed by following a two-mode network approach. A
two-mode network is also often called an affiliation,
bimodal or bipartite network. It means that the matrix
may not represent a network with the same entities
(Monge and Contractor, 2003). In the context of this
research, this is a network were authors are connected
to the genre of their interactions within Facebook. In
this case, the rows denote individuals, the columns
denote different types of posting (genres) and the
cells are filled with the type of interaction (like or
reaction). Broadly speaking, two basic approaches are
available to analyse two-mode data. Borgatti and
Halgin, (2011) distinguish the “conversion” approach
and the “direct” approach. In the conversion approach
visualizes one or both modes of the two-mode dataset
are converted into two one-mode projections which
are then analysed and separated. In the direct
approach, which has been carried out in this study,
both modes are analysed in a single graph.
The colors
in the graphs represent characteristics assigned to the nodes
or edges, for example the genre or type of interaction. The
size of the nodes represents the frequency a certain genre
leads to an interaction.
Effectively identifying interaction patterns within
networked structures was done by modularity
analysis. Modularity is a measure that shows the
density of edges inside groups as compared to edges
between groups (Newman, 2006). Networks with a
high modularity have dense connections between the
nodes in a groups and sparse connections between the
nodes in different groups. Modularity is often used as
an optimization method for detecting community
structures in networks, but in this study we use
interaction patterns to define the dense groups of
members in a WKC. Besides the modularity, we also
use some descriptive measurements to describe the
developments within the two cases.
3 RESULTS
We conducted a systematic content analysis and
network analysis to define – in three different phases
(beginning, evolution and maturity) – the underlying
social interaction patterns in the two WKCs. The
interaction patterns are operationalized by using the
genres, described in the work of Naaman, Boase, and
Lai, (2010), see Table 1 on the previous page for the
genres and a realistic example from the Facebook-
data. In the following paragraphs, we describe the
resulting interaction patterns for each case in various
phases.
3.1 Case 1
The first online web-based knowledge community we
analysed in this research was the “Community of
Learning Innovation” case. Community of Learning
Innovation is a WKC that belongs to a Facebook
group page of a Massive Open Online Course
(MOOC). The goal of this MOOC is to teach
educational stakeholders more about course-design
by using educational technology in order to obtain
extensive practical experience with online technolo-
gies and to develop a working understanding of
incorporating successful online teaching strategies
into the practices of educators. The lessons took place
within Coursera, but the mutual knowledge exchange
(and collaborative development of knowledge)
largely took place on external social media platforms,
such as Twitter and Facebook. The MOOC officially
started in the last week of July 2014, but the
interactions and introductions of the participants
started two weeks earlier. The duration of the MOOC
officially was eight weeks, but including the four
weeks before the starting date, we analysed this case
over twelve weeks.
The first four weeks form the beginning or sowing
phase; during these four weeks, some participants
introduced themselves and shared knowledge. The
following two weeks forms the evolution phase;
during these weeks, the network of participants grew.
The last six weeks forms the maturity or harvesting
stage; during this stage, the online web-based know-
ledge community professionalizes. This is the result
of the course content of the MOOC and the increasing
depth of conversations. However, during the three
phases the number of co-likes decreased, while the
number of co-comments increased (Figure 1).
Figure 1: Level of Interactions (Case 1).
80%
76%
75%
20%
24%
25%
0
20
40
60
80
100
Beginning Evolution Maturity
Co-Likes
Co-
Comments
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102
3.1.1 Beginning Phase in Case 1
Figure 2: Beginning phase Case 1.
The main objective of the first community is to bring
teachers together who want to improve their online
teaching competencies. The following introdu-ctory
text, derived from the course description, gives a brief
explanation of the learning development the
educators need to go through: “The overarching
learning objective is to help existing educators to
establish or improve their own online or blended
teaching practices. As part of the course, there was
the opportunity to develop an own understanding of
effective online teaching practices and their
relationship to the use of different technologies. The
participants have also been encouraged to
progressively design and reflect upon the own online
learning activity, assessment or resource for use in the
own class”. The learning objectives described by the
instructors show to what extent and in what areas the
teachers develop themselves. They learn to establish
and improve their own online or blended teaching
practices; they learn to develop an understanding of
effective online teaching practices, and their
relationship to the use of different technologies. The
Facebook community created for this MOOC started
for socialization purposes. However, despite the lack
of a specific goal, many people still actively
participated in the community. A remarkable fact in
the analysis is that the vast majority of interactions
within the community consisted of participants
‘liking’ information, comments or questions instead
of responding to the content. The sociogram (Figure
2) show the network within Facebook, in which the
interactions were based on co-comments and co-likes
(initiator and responder). The results show an amount
of 349 nodes and 573 edges. The genres of these
interactions are related to questioning and individual
introductions. The modularity within the community
is 0.517 (51,7%), where a high modularity means
more edges (interactions between actors) within the
module than you expect by chance. Based on the
analysis of the modularity, we can identify 14
modules of interaction. The majority of these
interactions are co-likes (red edges), where members
appreciate the contributions of others. The blue edges
represent individual comments on certain genres.
3.1.2 Evolution Phase in Case 1
During the beginning phase, new members introduce
themselves to the community. In the evolution phase,
we see 283 nodes and 474 edges and a modularity of
0.335 (33.5%) with 5 clusters. The genres in this
phase shift from mainly introducing one to sharing
information and opinions. In total, the modularity is
0,335 (33,5%) and this resulted in a network of five
clusters. The major cluster is the exchange of infor-
mation, the second cluster represents the participants
who share their opinions; the third cluster describes
the number of questions from the participants; and the
fourth cluster consists of introducing oneself, and
therefore this fourth cluster strongly connects to the
genre ‘self-promotion’. In the final cluster in our
study, participants gave anecdotes about themselves,
to describe their best practices. In deeply conversa-
tions (information sharing and giving opinions.
Figure 3: Evolution Phase Case 1.
Question
Information sharing Introduction
Question
Information sharing
Opinions
Introduction
Interaction Patterns in Web-based Knowledge Communities: Two-Mode Network Approach
103
3.1.3 Maturity Phase in Case 1
In the maturity phase, we see 446 nodes and 845
edges. The modularity in this phase is 0.276 (27,6%),
with 6 clusters. The largest cluster or genre of the
activities within the web-based knowledge communi-
ty remains stable compared to the evolution phase:
individuals within the community ask questions,
share knowledge and information, and thank the
teachers and other participants for their feedback and
organization. Participants increasingly ask for help
from their colleague-students instead of teachers,
which underlines the benefits of a web-based know-
ledge community. The majority of interactions within
the community are ‘likes’ (~ 75 per cent) and the
remaining are comments, that are responses to
questions.
Figure 4: Maturity Phase Case 1.
3.2 Case 2
The Community of Linguistic Innovation is the
second Facebook group we have analysed in this
research. This group started in mid-September 2013.
The intentions of the Facebook group were to
researchers of English language who teach applied
linguistics. The community aimed to provide a
platform for English language professionals from
around the world to share and exchange teaching and
research information and ideas, despite their
background. The ultimate goal is not only to improve
the knowledge and skills on a micro-level (indivi-
duals), but essentially the improvement of skills and
competences on a macro-level (teaching society). The
content within the community is diverse, from topics
such as the development of curricula and materials to
language teaching methodology and classroom mana-
gement. Figure 5 shows that the mode of interaction
Figure 5: Level of Interactivity within Case 2.
over time increasingly shifts from the relatively
passive ‘liking’ of content, to the more actively
‘commenting’ on content.
3.2.1 Beginning Phase in Case 2
The ‘beginning’ phase of the Community of
Linguistic Innovation consisted in its first phase of
894 nodes and 1.574 edges in four weeks. The
network mainly consisted of females (64,43%). At the
start, the community leader introduced the formal
goals for the community and made the participants
aware of these goals. The community aims to provide
a platform for English language teachers from around
the world to communicate, share knowledge and
collaborate in order to improve their own working
practices, skills, effectiveness and/or competency.
The results of the content analysis and social network
analysis within Community of Linguistic Innovation
clearly show that from its first moments, the
introduction of the community has a special role, with
Figure 6: Beginning Phase Case 2.
90%
87%
84%
10%
13%
16%
0
20
40
60
80
100
Beginning Evolution Maturity
Co-Likes
Co-
Comments
Question
O
p
inion
Me now
Information sharin
g
Information sharin
g
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104
185 interactions. However, within a few days, this
picture completely changed. When the guidelines
were clear and the participants introduced them-
selves, the participants considered it time to share
their knowledge, to ask questions and share opinions,
this is clearly visualized in Figure 6. The information
sharing genre is a cluster that supplants the other
clusters. The majority of the members (90,22 percent)
within the community liked the exchange of
knowledge with their colleagues. Almost 10 percent
(9,78%) of the members commented on the posts,
these are often questions, comments, articles or
announcements.
3.2.2 Evolution Phase in Case 2
The ‘evolution’ phase of Case 2 consisted of 283
nodes and 474 edges in a period of two weeks. The
interaction during these weeks has led to a modularity
of 0.335 (33.5%). The majority of the members in the
community shared, liked and otherwise reacted to
knowledge within the online place. This is – when
following the number of comments and ‘likes’ – the
most appreciated form of interaction by the members.
Another form of interaction; sharing opinions, and
exchanging opinions strongly relates to information
sharing. The code for the exchange of information
with a ‘personal touch’ was ‘opinion’, because of that
the information exchange is not completely objective.
During the evolution phase an increase in co-
comments can be identified, there was an increase
from 9.78% to 12.58%, which show an increase
of2.80% in comparison to the beginning phase. This
may indicate a growing sense of connectedness. The
other 87.42 percent of the interactions are co-likes.
Figure 7: Evolution Phase Case 1.
3.2.3 Maturity Phase in Case 2
The ‘maturity’ phase of the Community of Linguistic
Innovation consisted of 446 nodes and 845 edges
after a period of analysis of five weeks. This resulted
in six clusters with a modularity of 0.276 (27.6%).
The majority of interactions in the maturity phase of
the Community of Linguistic Innovation is
represented in co-likes (84.38 percent), and we
visualized this by the red edges in Figure 8. The major
part of the participants decided to easily ‘like’
another’s message. The remaining 15.62% are
‘comments’ on initial posts (blue edges). Again, we
see an increase in the volume of comments rather than
‘likes’. The interactions within the web-based
knowledge community were mostly based on
information sharing, expressing opinions and
introducing new members. Since Community of
Linguistic Innovation intended to be a continuous
developing community, the introduction of new
members is a continuous process. As the sharing of
information and expressing of opinions increases, we
can confirm that the individual’s self-confidence to
actively participate in the online environment is
rapidly increasing after a certain period of time,
whether this might be by liking other’s reactions or
posts or by commenting on others.
Figure 8: Maturity Phase Case 1.
4 DISCUSSION AND
CONCLUSIONS
In this study, multiple interaction patterns were
identified that represent the development of web-
based knowledge communities. These interaction
patterns were identified during three phases: the
Question
Information sharing
Opinions
Introduction
Question
Opinion
Me now
Information
Interaction Patterns in Web-based Knowledge Communities: Two-Mode Network Approach
105
introduction, the evolution and the maturity of the
community. The resulting patterns provide insight
into how WKCs evolve over time and provide insight
into the increasing voluntary use of such WKCs. The
patterns were identified by evaluating two WKCs in
which innovative educational stakeholders interact
with likeminded others with the common purpose to
improve education. In this study we studied the
development of these WKCs. A typical method for
analysing and visualizing the development of such
cases is by means of conventional methods available
in statistical computer programs (for example SPSS
and Amos). However, in this study we have opted for
a method based on secondary data: social network
analysis. We analysed and visualized the
development of WKCs by using a two-mode network
approach where the connectivity is represented in a
relationship between individuals and the genre of
conversations. The size of the nodes represent the
weight of the nodes. The larger nodes embody the
genre of interactions. The larger the nodes, the more
relevant the genre. The colour of the relations
characterize the type of interaction which can be a
more passive ‘like’ or a more active ‘comment’. The
majority of likes were given when one introduces
themselves, in case of opinions the number of
comments increased.
The research question posed in the beginning of
our study was: What kind of interaction patterns
describe the development of an online web-based
knowledge community in an educational context? To
answer this research question, we have used the
genre-theory introduced by Naaman et al (2010). Due
to the differences in the two WKCs, we identified
different interaction patterns. In the first case –
Community of Learning Innovation – the participants
all have a common interest: improving their online
teaching competencies. In a MOOC, the participants
have already learned the necessary skills to deliver
online teaching, but in the WKC-environment
discussions about the topic continued. In the second
case – Community of Linguistic Innovation –
individuals took part in an independent WKC with the
central topic of ‘improving English teaching’. In this
WKC, the participants discussed the topic from
various perspectives and at different levels of
knowledge. Despite the large differences between the
two WKCs, there are also some similarities. In both
WKCs, we identified a remarkably similar
development of phases. In the first phase, individuals
introduced themselves. In the second phase, the
individuals were more confident in sharing external
information and in the third phase, individuals felt
confident enough to share their opinions. In this
phase, a form of friendship could be identified that
was only minor in nature, but nonetheless, it is
indicative of the success. The members dared to
express their opinions openly– be it online and
anonymously in the – communities. Since the size of
the Community of Linguistic Innovation is
continuously increasing, each new participant
introduced themselves in contrast to the Community
of Learning Innovation were the majority of members
registered at the same time when the MOOC started.
Especially the first members introduced themselves,
but this trend gradually decreased.
To conclude, this research improves our
knowledge about WKCs in general and gives insight
into the sociological development of WKCs
described with the genres labelled in the two-mode
social graphs. The success of a WKC depends on the
individual willingness to create a sense of group
feeling (or community feeling). Each individual must
feel confident to add relevance to the community
before the individual and other members can benefit
from it. One of the activities which stimulates the
individuals willingness to share information is by first
letting them introduce themselves to the other
members. After a relatively short time frame, the
members share the more formal information and after
a couple of weeks they also share their opinions about
the information others give and share more
opinionated information/knowledge. Awareness of
these stages and the related patterns increases the
chance to successfully develop web-based knowledge
communities.
One of the limitations in this study is the genre
determination, since some interactions fit multiple
genres. If for example someone asks “Do you also
think that English should be the global language?”,
this statement can be judged as a question, but also as
an opinion. In such cases, we have labelled it as a
question. Since we have chosen to connect one genre
per interaction. In upcoming studies we recommend
to use multiple genres per interaction.
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