On lurking: Multiple perspectives on lurking within an educational community

Abstract

Lurkers, or as defined in this research, Legitimate Peripheral Participants (LPPs), have been a fundamental aspect of online communities and more research is needed to better understand them. This paper, therefore, examines lurkers in a mixed-method study through the lenses of Transactional Distance, Interaction Types, and Self-Determination Theory and aims to identify their defining features. The findings show that the degree of engagement of any particular LPP is influenced by different aspects of distance and interaction. Time, as an external factor, and lack of interest, as an internal factor, emerge to be the most influential considerations; but a combination of these factors can also lead learners to be an LPP. Characteristic words to define LPPs seem to be ones that have positive connotations and indicate that LPPs learn through less active and visible methods than other learners.

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The Internet and Higher Education
Available online 14 November 2019, 100709
In Press, Journal Pre-proof
On lurking: Multiple perspectives on lurking within an educational
community
Aras Bozkurt , Apostolos Koutropoulos , Lenandlar Singh , Sarah Honeychurch
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https://doi.org/10.1016/j.iheduc.2019.100709
Highlights
Lurkers are invisible, silent learners on the peripherals of the networks.
Transactional distance can be in multiple forms.
Lurkers prefer vicarious interaction.
Combination of internal and external factors can lead learners to lurk.
Abstract
Lurkers, or as defined in this research, Legitimate Peripheral Participants (LPPs), have been a fundamental
aspect of online communities and more research is needed to better understand them. This paper,
therefore, examines lurkers in a mixed-method study through the lenses of Transactional Distance,
Interaction Types, and Self-Determination Theory and aims to identify their defining features. The
findings show that the degree of engagement of any particular LPP is influenced by different aspects of
distance and interaction. Time, as an external factor, and lack of interest, as an internal factor, emerge to be
the most influential considerations; but a combination of these factors can also lead learners to be an LPP.
Characteristic words to define LPPs seem to be ones that have positive connotations and indicate that LPPs
learn through less active and visible methods than other learners.
Keywords
MOOCs; Lurkers; Legitimate peripheral participants (LPPs); Transactional distance and
interaction; Self-determination theory
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Bozkurt, A., Koutropoulos, A., Singh, L., & Honeychurch, S. (2020). On Lurking: Multiple perspectives on lurking within an educational community. The Internet and
Higher Education, 44(2020), 100709. https://doi.org/10.1016/j.iheduc.2019.100709

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1. Introduction
"Lurkers are like stars; not always seen, but always there."
Learning is an essential skill that helps people to survive in their ecosystem. Individuals can learn to
improve themselves, make sense of the environment in which they exist, and better integrate themselves
into it. It is a complex process and has many forms. In spite of its vital significance, for a long while
engagement in learning has been associated with observable behaviours, such as raising one’s hand in class
or responding a thread in a discussion forum. This assumption has limited our understanding and has led
us to explore learning only as a visible and measurable phenomenon.
Online learning environments bring with them many new affordances and possibilities. They provide an
opportunity for researchers to revisit concepts of what it means to be a learner, and what it means to
engage in learning. In contrast to traditional definitions of learning, this study does not consider learning
only as it relates to a set of observable behaviours and rejects confining it into a single dimension.
Instead, this study suggests that learning can happen in many ways and that lurking is one way of learning
and interpreting our online and offline, virtual and face-to-face, worlds. Lurkers are often considered to be
an elusive group of participants because they are less visible than other learners and thus more difficult to
identify and track. Lurkers are, therefore, given many names, some of which are: free-riders, vicarious
learners, browsers, witness learners, read-only participants, non-public participants, observers, or invisible
learners (Honeychurch, Bozkurt, Singh, & Koutropoulos, 2017). Having reviewed the literature and found
various definitions that were both positive and negative, Edelmann (2013) suggests that an understanding
of lurkers is important lest there be “misunderstanding of the online environment” (p.647). This study is
an attempt at understanding the practice of lurking and lurkers themselves. It uses the term Legitimate
Peripheral Participants (LPPs) (Honeychurch et al., 2017; Lave & Wenger, 1991) which has a neutral
connotation and defines these learners’ characteristics from the perspective of an online learning network.
To this end, the main purpose of this research is to build on our past research and further examine LPPs in
online networked learning spaces through three lenses. This research uses Transactional Distance,
Interaction Types, and Self-Determination Theory to frame LPPs and seeks to answer the following
research questions:
How do LPPs perceive psychological, emotional, cognitive, and cultural distance? This research
question specifically investigates transactional distance.
How do LPPs interpret learner-learner, learner-facilitator, and learner-content, and learner-interface
interaction in an online learning ecology? This research question specifically investigates interaction
types.
What kind of factors drive participants to be LPPs? This research question relates to all three of the
proposed frameworks.
How do LPPs define lurking? This research question builds on our previous research.
2. Literature review
While there has been research literature involving the MOOC and Self Determination Theory (e.g., Beaven,
Codreanu, & Creuzé, 2014; Beaven, Hauck, Comas-Quinn, Lewis, & de los Arcos, 2014) these studies tend to
not focus on LPPs but rather on participants who are visibly active. The same is true for research that

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examines the MOOCs from an Interaction Types lens, and likewise research about MOOCs and
Transactional Distance. For this reason, we have not included this literature here.
In our previous research on LPPs within the community we are researching, we concluded that lurking is a
complex behaviour on the part of LPPs and that there is no single identifiable reason for choosing to be an
LPP (Honeychurch et al., 2017). Lurking is a concept that is hard to define because it is a complex set of
behaviors, which are dependent on context (Dennen, 2008). While lurking has been thought of as a
generally negative activity characterized by participant disconnection from the community, and
passive/silent behaviors, there is some evidence that community members lurk for “valid reasons”
(Nonnecke, Andrews, & Preece, 2006, p. 17); that lurkers are actually “actively evaluating perceived
community support” (Honeychurch, Bozkurt, Singh, & Koutropoulos, 2016; Yang, Li, & Huang, 2017, p.18)
and that learning actually takes place while engaging in what Dennen (2008) describes as “pedagogical
lurking” (p. 1624). An example of such pedagogical lurking taking place can be seen in Kop’s (2011) work in
MOOCs where participants express positive associations with regard to lurking and learning. Despite the
potential negative connotations, Soroka and Rafaeli (2006) indicate that “lurking is an integral and normal
part of Internet behavior” (p. 164), and what connects LPPs to other members of their online communities
is a shared interpersonal trait of curiosity (Schneider, Von Krogh, & JäGer, 2013).
The reasons for lurking, as mentioned above, are not easy to fully define. Nevertheless, researchers have
attempted to explain the various reasons for this online behavior. For example, in early research conducted
by Nonnecke and Preece (1999) that examined lurking behaviors of users of ICTs of the time period (e.g.,
BBS, MOO, newsgroups, discussion forums, etc.), they identified thirteen such factors that impacted a
particular user’s decision to be an LPP. These factors can be further organized into the broader categories
of personal and emotional, perceived value, community, and privacy and safety. Preece, Nonnecke, and
Andrews (2004) identified five factors that impacted lurking and participation: software usability, the
necessity of posting, being helpful by not posting, insufficient information about the group, group
dynamics/community fit for personal needs. Ridings, Gefen, and Arinze (2006) identified categories such as
psychological and trust as barriers to participation in online communities. Nonnecke et al. (2006) found
that community design, interaction, and membership were also reasons for participants not posting in an
online community. Finally, Sun, Rau, and Ma (2014) identified four categories: environmental, personal,
individual-group relationship, and security and privacy.
2.1. Theoretical backgrounds
Despite previous empirical attempts to understand lurkers, there is still much to discover about them, and
this research intends to contribute to the related literature by examining them through the lenses of three
theoretical perspectives. These are Transactional Distance, Interaction Types, and Self-determination
Theory:
2.1.1. Transactional distance
Transactional Distance is a theory developed by Moore (1983) in order to describe distance education as a
pedagogical concept, rather than as a geographical phenomenon. It describes the nature of interactions
between learners and teachers in terms of types of distance. Moore (1983) uses the concept of a transaction
(Dewey, 1938) in order to explain specific patterns of learner and teacher behaviour. Our study extends
Moore’s original model and seeks to explain LPP behaviour in terms of transactional distance by extending
Moore’s original categories to include emotional, cognitive and cultural distance as well as the original
category of psychological distance. Our intuitions, as participant-researchers in similar online learning
communities, were that these facets were likely to affect levels of engagement and were therefore designed
our research questions in order to investigate this. We define each of these as follows:

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Psychological Distance: this refers to the distance between an online learner, people and things that are
not in their physical vicinity. Typically, these distances can be of four types: temporal, spatial, social,
and hypothetical (Liberman, Trope, & Stephan, 2007). For the purposes of our study, we wanted to
separate psychological distance into two sub-categories of cognitive and emotional distance.
Cognitive Distance: by this, we mean psychological distance viewed solely as an intellectual distance.
We were interested in the way that learners related to the conceptual aspects of an online community or
its members and whether this would be a motivating factor for some learners.
Emotional Distance: by this, we mean the amount to which learners care personally about the content
of a community or are close to its members. This is similar to affective social distance (Bogardus, 1933),
but without the cultural aspect. We wondered if learners would join a course or community purely
because they felt emotionally close to a core participant or a member, regardless of the academic
content.
Cultural Distance: this refers to the differences in cultural values that exist between members of
different countries (Beugelsdijk, Kostova, Kunst, Spadafora, & van Essen, 2018). In this study, we
examined whether participants viewed others from different cultural contexts differently or whether
participation was affected by different cultural elements.
2.1.2. Interaction types
Interaction, in some form, is a prerequisite for deep and meaningful learning (Moore, 1983). Moore (1989)
defined three types of interaction that are necessary when learners are separated in time and space. These
are learner-learner, learner-instructor, and learner-content interaction. Moore views learner-content
interaction as a defining characteristic of education because without this interaction, according to Moore,
there cannot be education (Moore, 1989). Learner-instructor interaction is what is regarded as essential by
many educators, according to Moore, because it ties into the ability of the educator to stimulate curiosity
and motivation for learning (Moore, 1989). Finally, learner-learner interaction was a new concept at the
time of Moore’s original writings on the subject, however, this type of interaction has taken on additional
significance with the increased importance of social learning theories, with the most recent example being
connectivist (Siemens, 2004) approaches to learning. With a focus on the broad use of technology, Hillman,
Willis, and Gunawardena (1994) added another interaction type: learner-interface. These four factors were
confirmed by Chen (2001) in a study of online learners. In this study, learner-interface interaction refers to
components of online learning such as Social Network Sites (SNSs), and hashtags. This research adopts
these four types of interaction and seeks to explore how LPPs perceive and interpret their interaction
according to each.
2.1.3. Self-determination theory
Self-Determination Theory (SDT) is a theory of motivation that provides an explanation of intrinsic
motivation of human innate psychological needs and desires for autonomy, competence, and relatedness
(Deci & Ryan, 2000; Ryan & Deci, 2000). It focuses on motivation, distinct autonomous motivation and
controlled motivation (Deci & Ryan, 1985) and suggests that intrinsically motivated behaviours are typically
autonomous while extrinsically motivated behaviours are controlled. This framework is used here to
investigate the intrinsic and extrinsic factors that lead learners to be LPPs and attempts to explain each.
3. Methodology
3.1. Research model and design

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This research analyses LLPs from both quantitative and qualitative perspectives of online learning
communities. It, therefore, uses an explanatory sequential mixed methods design, which consists of
processes that include first collecting quantitative data and then collecting qualitative data (Creswell, 2012).
The justification for this design is that while we can derive a general picture from the quantitative analysis,
a more refined view and better explanation can be gained through qualitative analysis (Creswell, 2012). The
first phase of the research involves Social Network Analysis (SNA), which examines network data from both
a numeric and a visual perspective. The second phase used online surveys to collect data, and content
analysis to interpret textual data collected from these surveys. The overall research flow is depicted in Fig.
1.
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Fig. 1. The overall research flow.
3.2. Research context
CLMOOC (Connected Learning MOOC) ran from July 10 to August 13, 2017. This was the fifth iteration of
CLMOOC, which was originally a collaborative offering from the National Writing Project (NWP) network
(nwp.org) in 2013 and was never tied to any specific institution. CLMOOC was designed and facilitated by a
group of educators from NWP in order to support educators in experimenting with designing and learning
using the Connected Learning framework. This framework aims to support learning as an interest-driven,
production-centered activity in networked, peer-based, communities. The intention of CLMOOC was to
provide “an open, collaborative, knowledge-building learning and sharing experience” and “cultivate a
community of learners in creative, networked collaboration, centered around making”. In CLMOOC, rather
than instructors, there were “teams of facilitators, coaches, and make cycle leaders, as well as other
volunteers ... behind the scenes supporting CLMOOC and its community” (CLMOOC, 2017). Importantly,
the second ‘C’ in CLMOOC stands for ‘collaboration’ and not for ‘course’ (West-Puckett, Smith, Cantrill, &
Zamora, 2018).
3.3. Sampling
In order to sample CLMOOC participants, the 90-9-1 Rule was used (Nielsen, 2006). According to this rule,
90% of users are LPPs (they read but contribute little or no content of their own), 9% are contributors (they
participate from time to time), and the remaining 1% of users are heavy contributors: in other words, they
are leading participants and creators (they participate a lot and account for most contributions) in online

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communities (Fig. 2). These values are not exact cut-off points to classify participants but are very useful in
framing participation and in identifying research participants.
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Fig. 2. 90-9-1 Rule (Nielsen, 2006).
The first week of participation in CLMOOC (2017) was examined according to a metric of out-degree
centrality. Out-degree centrality refers to participants’ engagement and interaction and, in the scope of this
research, it is calculated according to the number of tweets created. The first week is of particular interest
as a sample because it included many participants, and thus can be considered as representative. In
addition, sampling the first week provided us with an opportunity to take it as a base and to examine
whether those in the 90% continued to be peripheral participants. The first week demonstrated that of the
136 participants, 97 were in the 90%, nine were in the 9%, and two were in the 1% (Fig. 3). A total of 28
participants were not included in research as they had in-degree values, but their out-degree values were
zero. This means that they were mentioned in tweets with the CLMOOC hashtag, but they did not respond
to these mentions. Therefore, they were not really participating. In our sample, 1% was responsible for
15%, 9% for 34%, and 90% for 51% of the out-degree values.
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Fig. 3. Distribution of CLMOOC participants according to their out-degree values.
3.4. Data collection procedures, tools, and analysis
There were two phases to the research process. The first phase used SNA for both data collection and
analysis. SNA is a technique used for tracking, visualising, and analysing networks (Hansen, Shneiderman,
& Smith, 2010) which can also be used to interpret structural patterns of social relationships (Scott, 1991).
In this phase, we analyzed network data that was collected by tracking the #CLMOOC hashtag on Twitter.
Each participant that used the CLMOOC hashtag was tracked and represented as a node, and their
interactions are represented as ties. Based on interactions tracked, local and global metrics of the network
is calculated (See Appendix 1 for a glossary of SNA terms). In order to gather network data, the researchers
used NodeXL, which is “an extendible toolkit for network overview, discovery and exploration
implemented as an add-in to Microsoft Excel” and can be used for “network overview, discovery and
exploration” (Smith et al., 2009, p. 255). NodeXL works with many social media platforms and crawls data
according to the permissions granted by APIs. The data crawled by NodeXL is then can be further analyzed
through global and local quantitative metrics and qualitative network sociograms.
In the first quantitative phase, the network structure was identified by tracking the #CLMOOC hashtag
through SNA techniques. In order to identify LPPs, each participant's out-degree values (which refers to the
level of engagement and activity) were calculated. Participants in the 90% of the network, in terms of their
out-degree values, were identified as being potential participants (LPPs) and were invited to participate in
an online survey. In addition to identifying and sampling potential LPPs, we monitored five weeks of
network interactions and tracked whether participants change their positions throughout the layers of the
network.
In the second qualitative phase, based on local and global network metrics, we visualised those CLMOOC
network and participants lying in the 1%, 9%, and 90% positions. The rationale and justification of this
classification is explained in the sampling section. Following this analysis, we investigated how LPPs build
connections in the network through SNA. After that, the online surveys were delivered to 77 participants
out of 97 with identifiable accounts through emails, direct messages, or mentions. A total of 23 participants
out of 77, falling within the 90% during the five weeks, responded to the survey (response rate was 29.3%)
(see Appendix 2 for online survey questions). Since the research questions were based on specific
theoretical and conceptual perspectives, the content analysis of online surveys used the survey questions
themselves as themes to be used in this analysis. While reporting the findings, the researchers provided
direct quotes to give a voice to the participants, to ensure transparency of the qualitative analysis, to enrich
the understanding of readers of this article and add their own interpretation, and most importantly to
increase the credibility of the research process.
4. Findings and discussion
The first phase of this research presents a network analysis of LPPs’ network positions. The second phase
provides a deeper explanation based on the survey responses from LPPs. These include topics such as
transactional distance, interaction, internal and external factors, and a definition of lurking behaviors.
4.1. The first phase
The first phase of the study was the calculation of SNA metrics of the CLMOOC network. Firstly, global
metrics (that is to say overall network metrics) were calculated in order to have a holistic view of the
network (Table 1). A total of 136 participants (nodes) were identified. There were 1497 interactions (ties)

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among these participants. Of all interactions, 385 were self-loops (participants only interacting with
themselves that can be observed in any network) which means that these interactions did not mention any
other node, but only used the CLMOOC hashtag. Only 6 participants were isolated nodes (i.e., those 6 out
of 136 did not connect to any other nodes). Taking 6 steps as a threshold for the distance (Milgram, 1967)
gave a maximum geodesic distance value of 5 and an average geodesic distance of 2.704 (which explains the
number of required steps for the shortest paths among any other nodes) and therefore indicates a tightly
connected network pattern. Graph density value can range between 0 and 1 and in the analysis of the
network, where the total number of nodes (n=136) and the total number of ties (n=1497), was 0.0228.
Further analysis demonstrated that the reciprocated node ratio was 0.423. Of the total number of 1497 ties,
741 ties (49.5%) were reciprocated while 756 ties (50.5%) were not reciprocated. According to Dunbar (1992),
the number of individuals who can establish a stable communication is 150 which is very close to the
number of the participants in our sample (n=136). Considering that all these interactions occurred among
participants who were separated in time and space; and keeping in mind that in Face-to-face settings
reciprocated interactions among 136 participants is not expected, graph density and reciprocated node
ratio metrics demonstrate that the sampled network demonstrated interaction in acceptable ranges.
Table 1. Overall global metrics for the CLMOOC network for the first week.
Graph type Directed
Nodes 136
Total ties 1497
Self-loops 385
Reciprocated node pair ratio 0.268
Reciprocated node ratio 0.423
Single-nodes 6
Maximum geodesic distance (diameter) 5
Average geodesic distance 2.704
Graph density 0.0228
Following this analysis, the local node metrics were calculated. In this step, the nodes were ranked from
highest to lowest according to their out-degree metrics. Those nodes with zero out-degree value (n=28)
were excluded from the sample since this means that even though some participants tried to interact with
them (because they have in-degree values), they did not respond to these efforts at all; thus their out-degree
value was calculated to zero. The main research sample (n=108) was then clustered according to the 90-9-1
Rule. Two participants were grouped in (1%), nine participants were grouped in 9%, and 97 participants
were grouped in 90%. The means of local metrics for these clusters are presented in Table 2.
Table 2. Means of the local metrics of the participants clustered according to the 90-9-1 rule.
Metric Value

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1% (n=2) 31 34 3530.978 0.047
9% (n=9) 15 18 1413.672 0.033
90% (n=97) 2 2 63.778 0.006
As can be seen in Table 2, when the in-degree and out-degree values of each group are compared, learners
in the 90% (LPPs) have limited participation. In terms of betweenness centrality, which refers to the ability
to link other participants, eigenvector centrality, and PageRank, which refers to participant prestige, we see
that in each instance, those in the 90% play a minor role. When compared, reciprocated node pair ratio
provides interesting findings. Accordingly, in addition to the distribution of the out-degree, reciprocated
interactions seem to be an indicator of the participants’ positions in the network. This further confirms
that interaction has a pivotal role and further justifies the theoretical lenses used in this study (e.g.,
Interaction Types and Transactional Distance). All in all, global metrics (Table 1) indicate that CLMOOC
was a tightly connected learning community with strong connections and interactions, and local metrics
(Table 2) demonstrate that the 90-9-1 Rule is a sound ground to classify participants as LPPs (90%),
moderately active participants (9%) and active participants (1%).
4.2. The second phase
In the second phase of the study, the CLMOOC network was visualized based on global and local metrics.
To see the overall network structure, a sociogram (network graph) was created using the Harel-Koren Fast
Multiscale layout algorithm (Harel & Koren, 2001). The tie colors, widths, and opacities are based on edge
weight values. The node sizes and layout order are based on out-degree values. Nodes in the 1% are marked
as black spheres, the 9% as blue spheres, the 90% as green spheres and those in the 0% are marked as grey
squares.
As can be seen in the sociogram for the first week, active participants (1%) sit at the very center of the
network and moderately active participants (9%) lie around them. However, LPPs (90%) are distant from
the center. When Fig. 4 is examined, it can be also seen that those who are close to the center are exposed
to much of the interaction, which can be considered important for any social learning. In this sense, it
seems to be important to understand how LPPs perceive distance, which kind of interactions they value
and what are the internal and external factors that make them LPPs.
90-9-1 In-degree Out-degree Betweenness centrality Eigenvector centrality

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Fig. 4. Distribution of participants according to 90-9-1 rule.
4.2.1. Transactional distance
In Transactional Distance Theory, distance refers to “the relationship of the two partners in the
educational enterprise” (Moore, 1983, p.155). In online learning networks, distance refers to more than just
physical distance and, therefore, this section investigates transactional distances from the perspectives of
psychological, emotional, cognitive, and cultural distances.
4.2.1.1. Psychological distance
The responses to the online survey indicate that the nature of an online learning environment, and thus
how it is perceived by learners, is a determinant of psychological distance. For instance, one of the
participants reported that “the conversations in a course or group and then the subtleties of how people treat each
other and that influence how I interact with them psychologically” (P2). In addition to beliefs formed by the
engaged learning community, there is another belief formed by the self itself: “Depending on my state of mind
(lots of anxiety going on right now), I am more or less likely to be actively involved in a community” (P22). That is,
psychological distance can stem from factors in the outer or the inner world of the participants. Overall,
the findings indicate that while psychological distance is an indicator of being an LPP, it is difficult to say
where, when, and why lurking starts and ends.
The literature also suggests that psychological distance is not one thing, but actually a sum of many
smaller components. Different component parts are time, space, social distance, and hypotheticality; and
they guide the prediction, evaluation, and behavior of the selves (Trope, Liberman, & Wakslak, 2007). As

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well as being an indicator of LPP, decreasing psychological distance would increase a sense of community
among learners (Rovai, 2002a), thus helping to form a less hierarchical learning community. Empirical
findings also show that psychological distance is a critical predictor of success in distance learning (Shin,
2003). The existing literature and this study’s findings thus support the thesis that psychological distance is
an indicator of being an LPP and having a sense of community in a learning environment.
4.2.1.2. Emotional distance
The survey responses suggest that when some participants feel and are exposed to emotional distance, they
“would drop out altogether rather than stay as an LPP” (P20). However, contrasting views were also articulated.
For instance, it was said that participants should not be present for emotional connection, but they should
expect to “learn, extend, or change the way I see things” (P2). another participant suggested that emotional
connections are something we hope for, but they are also something we can develop and use to fill the gaps
in emotional distance. Supporting this view, it is expressed that participants “would more likely reveal
[emotional] presence if [they] had strong feelings about topic” (P14). This means that while emotional distance is
an indicator of being an LPP, it is a dimension that is built upon and developed collectively. It also appears
that emotional connections provide for a virtuous cycle in minimizing distance: in other words, the more
people connect, and the more connected they feel, the more likely they are to be brought in from the
periphery. Even though the CLMOOC was not a traditional classroom environment per se, there are
potential connections here to the concept of social presence (Rourke, Anderson, Garrison, & Archer, 2001).
In learning environments, higher levels of social presence have been linked to more collegial learning
communities that encourage participation and interaction (Zhao, Sullivan, & Mellenius, 2014).
Emotional distance determines and shapes how people interact and communicate (Dede, 1996). In this
research, it can be seen to shape peripheral participation. In a worst-case scenario it could even induce
participants to drop out. Learning is a social process, and even in online learning communities, emotional
presence is a significant component of learning experiences (Cleveland-Innes & Campbell, 2012). Thus,
emotional engagement can be considered as an indicator of being an LPP.
4.2.1.3. Cognitive distance
The survey responses suggest that type of distance connects to issues of instructional design: more
explicitly, the process that learners go through in learning content that is available to them. Our research
participants reported that: “I definitely do not feel half as smart as most of the people engaged in the groups where I
am lurking. This definitely leads me to lurk more, speak less for fear of saying something stupid” (P15). Therefore, it
seems to be “Important for participants to communicate on the same level to get mutual benefits” (P11). The
participants’ responses reveal that when the content is just beyond their reach or does not meet with the
learners in an intermediate space, then a participant will become an LPP: they are still engaged and
working with the materials, but not in a visible manner.
Individuals differ in their cognitive processing styles and this affects their decision-making processes
(Robertson, 1985). Moreover, it is found that cognitive learning preferences of learners in distance
education affect dropout rates (Robertson, 1985). Accordingly, “online learners who have a stronger sense of
community and perceive greater cognitive learning should feel less isolated and have greater satisfaction
with their academic programs, thereby resulting in fewer dropouts” (Rovai, 2002b, p. 228). Cognitive
presence, along with social and teacher presence, is significant for the effectiveness of learning in online
communities (Garrison, Anderson, & Archer, 2000). This suggests that cognitive distance will influence a
decision to become LPP, or dropout entirely from the course or community.
4.2.1.4. Cultural distance

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In the previous sections, the findings demonstrated that psychological, emotional, and cognitive distances
affect participants’ decisions to be an LPP or dropout altogether. Cultural distance, however, seems to have
little effect. Moreover, participants’ attitudes toward cultural distance seemed to be positive. For instance,
by interpreting cultural diversity as richness, one of the participants stated that cultural distance is “only a
minor problem and more enriching” (P11). In a similar fashion another participant said that “cultural diversity
enhances the online experience” (P17). It seems that, as long as there are no communication barriers resulting
from cultural diversity, participants embrace it: “I tend to stay around if I am interested, even if the cultural frame
is distant - but linguistic difference prevents me from lurking” (P18).
4.2.2. Interaction types
4.2.2.1. Learner-learner interaction
The survey responses show that learner-learner interaction is important as long as it is “nurtured along” (P1)
which requires “equality and willingness on both sides to have an open discussion” (P6). It can be an important
motive for participation in a learning network: “when you lurk, you feel like you're not a ‘core member’, so having
someone like or reply to your comments is affirming” (P8). More importantly, such interaction “helps [them] to feel
engaged with the learning experience and makes [them] want to come back for more. The more [they] get to know
people, the more [they] want to know about them. And this is where the learning happens for [them]” (P15). Another
participant indicated that they “think of personal commentary, feedback, sharing, and reaction” (P2) emerging as
important interactions between participants, and at the same time a concern for others’ time emerged; for
instance, one participant indicated that they “worry that [they] would intruding on their space and time” if they
interacted more (P2).
4.2.2.2. Learner-facilitator interaction
Some responses suggested that learner-facilitator interaction is necessary to get “guidance, support,
encouragement when needed” (P1). If learners see or feel supportive efforts by facilitators, that would
“encourage [them] to join in” (P10) the conversations. Interaction with facilitators “is valued, especially if the
facilitator interacts with [them]” (P14). An interesting dynamic was also noted by one participant: “I feel like
facilitators can sometimes forget about those who are on the peripheral or figure they have made the decision to take
that role and thus they don't have to work as hard to get them involved” (P16). This seems to indicate that
facilitators have the power to engage LPPs to become more central members of the network, and that some
LPPs have come to expect that the facilitator will reach out to them instead of relying on self-
determination of the LPPs.
4.2.2.3. Learner-content interaction
The survey responses suggest that some learners are interested in the content of a course to “drive their
participation” (P13) and “such interaction is necessary to get “feedback from others” (P14), thus enabling
communication with others in the learning network. However, other responses differ, suggesting that
some LPPs find learner-content interaction enough for their learning experience. For instance, one
respondent wrote that “[participants] like being able to interact with content without the pressure of socializing” (P8)
and that they perceive this type of interaction as "the creativity and learning” (P5). This participant’s comment
seems to corroborate Anderson’s Equivalency Theorem where meaningful learning is supported as long as
one of the forms of interaction is at a high level (Anderson, 2003). It should be noted that, in his work,
Anderson only considers Moore’s original three forms of interaction. Some LPPs believe that they are
active participants by virtue of their interactions with content, but not with the other participants, saying
that they are usually: “interested in the content rather than personal interactions so want to learn but don't want to
necessarily have to deal with other people in order to do the learning.” (P16). Some LPPs say that learner-content
interaction is significant for them: “If I don't contribute to the content, then I am not contributing to the

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discussion” (P1). Some feel that learning without visible presence would not be a problem for them “…I'd be
happy just to learn from the materials in the course” (P6); others participate in such experiences knowing that
their interaction will be limited to content only: “I prefer courses where you are supposed to interact with the
content rather than just memorize it” (P20). This confirms that interaction can be in different forms, in
different quantities, sometimes not visible, and its presence can be limited to oneself only. In sum, the
findings indicate that interaction with content is valued by some LPPs.
4.2.2.4. Learner-interface interaction
This interaction type refers to interaction with learning materials such as websites, learning platforms,
mobile apps, hashtags, and so on. One of the responses indicates an important fact about lurking: “if the
interface reveals our presence, participants may not feel unacknowledged” (P13), that is if LPPs interact with
different interfaces, they are there and present even if their identifiable participation is lower than
expected, or lower than in relation to other participants. It was also noted that the interfaces should be
“simple enough for people [with lower digital literacy] to engage with” (P20). The platforms, or sites, used in a
community are an important factor that influences the level of participation, and whether or not someone
will be an LPP. As one of our participants noted, there are “multiple platforms, some seem more intimate others
more distant” (P13). The level of comfort with a particular platform influences “how much tuned in” to the
course an LPP will be (P12), hence, a specific platform determines the level of engagement in the
community and impacts decisions about LPP.
The literature suggested that interaction, in some form, is required for deep and meaningful learning
(Moore, 1983) and participation is an intrinsically visible part of learning (Wenger, 1998). Our findings
show that all four types of interaction provide nuanced reasons why some participants chose to be an LPP.
LPPs’ explanations for learner-learner, learner-facilitator, and learner-interface interactions show various
reasons for being an LPP, while the responses about learner-content interaction reveal some interesting
findings on both the reasons they chose to be an LPP and their learning experiences as LPPs.
Equivalency Theory (Simonson, 1999) explains that only one type of interaction can contribute to
educational effectiveness (Anderson, 2003). As illustrated above, this might explain why, in our study, LPPs
tended to prefer learner-content interaction and ignore other interaction types - i.e. they did so because
they were satisfied with this as a learning experience. Their behaviour can be described as vicarious
interaction (Kruh & Murphy, 1990) which “takes place when a student actively processes both sides of a
direct interaction between two other students or between another student and the instructor” (Sutton,
2000, p.4). Those who interact vicariously read, observe, and learn from interactions of others and do not
feel obliged to directly interact with those others. The literature also reports that some learners prefer such
online or distance learning experiences to conventional, face-to-face experiences because they want to be
more autonomous and independent without feeling compelled to socially interact with others (Daniel &
Marquis, 1988). In summary, it seems that LPPs are satisfied with learner-self interaction, which is
invisible, uncountable and vicarious in nature.
4.2.3. Internal and external factors of LPPs
To better understand what causes somebody to be an LPP, our research investigated internal and external
factors that might lead to this behavior. The findings demonstrated that time (n=12) is the main external
factor, followed by professional commitments (n=4), digital literacy (n=3), difficulties in multitasking (n=2),
family commitments (n=2), gatekeepers (n=1); lack of interest (n=6), lack of curiosity (n=4), self-efficacy (n=4),
easy connectivity (n=4), inability to tune in (n=3), lack of confidence (n=2), mood (n=1), anxiety (n=1), fear to
connect (n=1), and fear of failure (n=1) (Fig. 5). However, it should be noted that either just one or a
combination of these factors can lead learners to become an LPP. It could be that this list is not exhaustive,
and that factors other than those listed here might emerge in different networked learning spaces.

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Fig. 5. Internal and external factors leading a learner to be an LPP.
4.2.4. Defining LPP behaviour
In order to better identify the characteristics of LPPs, we asked research participants to provide us with
positive words that define LPPs actions as well as negative words they did not like but are nevertheless used
to define LPPs. We first defined the most apparent words for active participants, moderately active
participants, LPPs (lurkers), no-shows, and drop-ins (Table 3). We then compared both positive and
negative words reported by survey respondents. Next, we identified some words reported by research
participants such as “hiding”, “sneaking”, “creeping”, and “skulking” which are loaded terms and portray
LPPs in negative roles. From this, we determined that LPPs do not like the word lurking and found it “dirty”
(P24). We also excluded synonyms of these words that were given as positive representations of LPPs (e.g.,
soaking up, absorbing, etc.). In all, we prepared the following table that characterizes the engagement level
of participation with a special focus on LPPs actions.
Table 3. Characteristic words that define different levels of participation.

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Drop-ins: picking up, benefiting, visiting, looking up, stopping by
5. Conclusion and suggestions
In this paper, we have explored the perceptions of LPPs through the lenses of psychological, emotional,
cognitive, and cultural distances. The perceptions that learners have of these distances, and how they
perceive each one, factor into the decision that each learner makes as to how close to the core, or what
degree of engagement, will define their status as an LPP. Cognitive and emotional distance appear to have
more concrete connections to the decisions made by individual LPPs with prior connections to
individuals, or connections to knowledge types being flagged as important. The more connected an LPP is
to other people or the knowledge presented, the more they seem willing to move closer to the core. The
fewer the connections, the more they drift toward the periphery and become an LPP. Cultural distance
appeared to be minimal in CLMOOC which may be unsurprising, given that it began as a project to
support North American teachers, and thus began with a fairly homogenous group. In future research, it
would be interesting to examine LPP’s perceptions in a community that is not as established as CLMOOC
or that draws a wider variety of participants in order to examine whether cultural distance plays a role in
such networks and learner decisions in deciding their presence level in the course.
Learner-learner interaction plays a role in how learners feel about their learning experience. Increased
learner-learner interaction brings out feelings of actually being engaged in the learning process, even for
LPPs. As described in the findings of emotional distance, this is a type of virtuous circle: the more learners
interact and connect, the more they feel encouraged, interested, or internally compelled to participate.
There are also those who might be characterized as thoughtful lurkers who consider the interests of other
participants and do not want to impose their interest in additional interaction on them. While some view
learner-learner interaction as a way to ‘delurk’ and move from the periphery closer to the core, others view
learner-facilitator interaction as the way to accomplish this. There appears to be a fine balance for learner-
facilitator interaction: enough interaction to get participants going, but not too much so as to stifle
participant creativity. This fine balance coupled with differing perceptions of what learners think the right
balance is can make it difficult to get learner-facilitator interaction ‘just right’. Even if learner-facilitator
interaction is just right, there is something to be said about being interested in the content itself, and our
findings in learner-content interaction lead us to think that there is a contextual dependency on content
and how interesting it is to the learner. If a learner is not particularly interested in the weekly content,
information, or activity they may decide to be an LPP for a week and watch from the sidelines and engage
more visibly in subsequent weeks when the topic is more of interest. This, in the past, has been called the
dip-in/jump-out nature of MOOC participant interactions (de Waard et al., 2011). Finally, with regard to
learner-interface interaction, an unsurprising discovery is that the platform interface impacts decisions of
the degree of engagement of LPPs. The easier the platform is to navigate, the lower the barrier to
participation. We would, however, suggest that user experience is not necessarily the same for all users. In
our previous paper on LPPs in this community (Honeychurch et al., 2017) we discovered that different
participants preferred different social platforms: for example, one platform was seen as useful or engaging
by one LPP, but that same platform was not perceived to have the same value-positive attributes by another
Active participants:
creating, curating,
writing, actively
engaging,
welcoming
Active participants:
creating, curating,
writing, actively
engaging,
welcoming
Moderately active
participants:
contributing, helping,
developing,
commenting,
discussing
Moderately active
participants:
contributing, helping,
developing,
commenting,
discussing
Lurkers: watching, listening, reading, observing,
digesting, wondering, browsing, following, tracking,
quiet learning, vicariously participating, internally
engaging, invisibly engaging, introverted participating
Lurkers: watching, listening, reading, observing,
digesting, wondering, browsing, following, tracking,
quiet learning, vicariously participating, internally
engaging, invisibly engaging, introverted participating
No-shows:
waiting,
pausing, by
standing,
seeking
opportunity
No-shows:
waiting,
pausing, by
standing,
seeking
opportunity

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user. When considering Interaction Types, it may be useful to think of Anderson’s Interaction Equivalency
Theorem (2003). The idea of getting all interactions ‘just right’ for all learners in a course is most likely a
fool’s errand, however, if viewed from the perspective of Anderson’s theorem LPPs can have a meaningful
engagement with content, ideas, and others, if learner-content and learner-interface interactions are high
even if learner-learner and learner-facilitator are low.
Finally, in terms of external factors that impact the degree of engagement of LPPs we see lack of time and
other professional commitments being identified as the biggest of the barriers that influence an LPP’s
actions. Additionally, lack of interest and curiosity seems to be effective internal factors that influence an
LPP’s decision. We do see connections to different types of interactions and distances as well, but the
biggest barrier, time, is outside of the scope of these examination lenses.
Our research findings have some important implications for learning design practice. The biggest
implication of this research, in our view, is that learning designers should design by keeping not only
active participants in mind but also considering the potential lurker. Learning spaces should be provided
where one can lurk yet still be an effective learner. To be clear, we are not simply advocating for a self-paced
eLearning model where learners only interact with the content, rather we think that the learning design of
courses should provide affordances that enable guilt-free lurking for participants should they find
themselves in an LPP position. The learning design could also create an environment that provides for
easy points to dip in and out of the course so that learners can more easily modulate their modes of
engagement. For example, a MOOC that requires learners to join a group, and stay active within that group
for the duration of the MOOC, makes lurking potentially more appealing to people who want to engage
with certain aspects of the course. These learners might be interested in engaging more (becoming part of
the 9%) during specific times in the course, but an up-front commitment might be something that
prevents them from more targeted participation. It should be highlighted that our focus is on larger
environments that can sustain both a core of active participants that keep the community going, while
supporting the engagement of a larger LPP population. These principles of pedagogical designs that
provide that space for lurking and provide for better dip-in points in the course, may be applicable to the
designs of smaller learning environments, such as traditional college courses, but this is not our own
frame of reference.
There are several strands of future research that this paper could motivate. One would be to investigate in
more depth why some learners express the lack of sufficient time as a reason for not engaging. We have
hypothesized that using lack of time as their rationale for lurking is a culturally accepted way to save face
when an LPP decides to intentionally move to the periphery, or to drift into the periphery. This face-saving
mechanism could work both ways. If the lack of engagement is intentional it saves face for other
participants who are engaging and organizers who put the effort into organizing and facilitating and it
validates in a non-confrontational way that the activity is of value, but just not that particular LPP. If the
drift is unintentional (e.g., the LPP simply forgot), the lack of time argument is a face-saving mechanism
for the LPP themselves by not casting them in a potentially negative light within the community. Another
area of research interest would be to examine the user experience of participants from a holistic
perspective. We know from our previous research that a single platform will not satisfy all participants
(Honeychurch et al., 2017), hence LPPs in one platform may not be LPPs in another. Uncovering what
particular features and affordances platforms have, and how those impact the degree of engagement would
be helpful in the design and implementation of such learning communities.
Acknowledgements
Not applicable

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Funding
The research is supported by Anadolu University Scientific Research Projects Commission under the grant
no: 1805E123.
Ethics approval and consent to participate
Ethics approval was obtained from “College of Social Science Ethics Committee, University of Glasgow, No:
400150106”. Each research participant for online asynchronous interviews signed a consent form.
Appendix 1. Social network analysis glossary (Bozkurt et al., 2016).
Node: Nodes can also be called “vertices, agents, entities, actors or items and they may represent people
or social structures such as work groups, teams, organizations, institutions, states, or even countries”
(Bozkurt et al., 2016, p. 9).
Tie: They can “represent any form of relationship or connection that occurs through exchange or
interaction between two” (Bozkurt et al., 2016, p. 29) or among many nodes and can also be called “links,
edges, connections, arcs, and relationships and they may represent many different types of
relationships like proximity, collaborations, kinship, friendship, citations, hyperlinking, transactions,
and shared attributes” (Bozkurt et al., 2016, p. 9).
Degree/Degree Centrality: The metric refers to “total number of unique edges (in and out) that are
connected to a vertex [node]. When the graph is directed, degree metrics can be indegree (points
inward) or outdegree (points outward) (Bozkurt et al., 2016, p. 29). If the indegree refers node itself, it is
calleda self-loop.
Betweenness Centrality: The metric refers to “a node's ability to bridge different subnetworks in a
network. In other words, it is a measure of a node’s centrality in the network which is equal to the
number of shortest paths from all other vertices to all others that pass through that node” (Bozkurt et
al., 2016, p. 29).
Eigenvector Centrality/ Page Rank: The metric “indicates influence score for strategically connected
vertices which takes into consideration not only how many connections a vertex has, but also the degree
of the vertices that it is connected to. Similarly, The PageRank algorithm is a variant of eigenvector
centrality” (Bozkurt et al., 2016, p. 29).
Geodesic Distance: The metric which refers to “length of the shortest path between vertices” (Bozkurt et
al., 2016, p. 29).
Graph Density: The metric which “that measures the sum of edges divided by the total number of
possible edges and demonstrates the level of interconnectedness of the vertices” (Bozkurt et al., 2016, p.
29).
Reciprocated Node Pair Ratio: The metric indicates the degree of the mutual relationship between the
nodes.
Appendix 2. Online survey questions
(Participants accessed the survey questions only after agreeing to participate in the research and signing
consent form)

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Anderson, 2003
Beaven et al., 2014
Beaven et al., 2014
Beugelsdijk et al., 2018
1-When you act as a legitimate peripheral participant (LPP) in an online learning community (AKA: silent
learner, invisible learner, observer, browser, read-only participant, vicarious learner, free-rider, or witness
learner), how would the following distance types influence or affect your behaviour?
Psychological distance:
Emotional distance:
Cognitive distance:
Cultural distance:
2-When you act as a legitimate peripheral participant, what do you think of each type of interaction and
why?
Participant-participant interaction:
Participant-facilitator interaction:
Participant-content interaction:
Participant-Interface (technological medium, SNSs, hashtags etc.) interaction:
3-What internal factors drive you to be an LPP?
4-What external factors drive you to be an LPP?
5- Which words/verbs do you prefer to associate with the act of lurking?
Recommended articles Citing articles (0)
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Aras Bozkurt is a researcher in the Department of Distance Education at Anadolu University, Turkey and in the Department of
English Studies at University of South Africa. He has an MA and PhD in distance education. He conducts empirical studies on
online learning through resorting to critical theories including connectivism, rhizomatic learning and heutagogy. He is interested
in emerging research paradigms including social network analysis, sentiment analysis and data mining.
Apostolos Koutropoulos (“AK”) is the program manager for the online MA program in Applied Linguistics at the University of
Massachusetts Boston. He is also an adjunct faculty member of the Instructional Design MEd program. Over the last few years he
has participated in many massive online open courses (MOOCs) and has co-authored research papers with colleagues around the
globe. AK holds a BA in computer science, an MBA with a focus on human resources, an MSc in information technology, an MEd
in instructional design, and an MA in applied linguistics. He is currently an EdD student at Athabasca University. His research
interests include online learning, knowledge management, educational technology, linguistics, and games in education.
Lenandlar Singh is a Senior Lecturer in the Department of Computer Sciences at the University of Guyana. He has an MSc in
Internet Applications Development. His research interests and publications are mainly spans the following areas: Computer
Science Education, Learning Technologies and Mobile Learning, HCI, Computer Science and ICT4D. Over recent years he has co-
authored and published a number of papers with researchers from various universities through online collaboration.
Sarah Honeychurch is a Fellow in the Adam Smith Business School, University of Glasgow (Scotland), with twenty years
experience of teaching in higher education. She has a BA and MA in Philosophy from the University of Southampton, and is
currently studying for a part-time PhD in Education at the University of Glasgow investigating the effects of peer interaction on
learning. Sarah has published in peer reviewed journals and presented at regional and international conferences on various
subjects, including the Jigsaw Classroom and using Facebook to support virtual peer assessed learning (VPAL). She is also an
editor for the open, online, peer-reviewed journal Hybrid Pedagogy, and an active blogger and user of social media.
View Abstract
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