Conference PaperPDF Available

Socio-Temporal Dynamics in Peer Interaction Events


Abstract and Figures

Asynchronous online discussions are broadly used to support peer interaction in online and hybrid courses. In this paper, we argue that the analysis of online peer interactions would benefit from the focus on relational events that are temporal and occur due to a range of factors. To demonstrate the possibility, we applied Relational Event Modeling (REM) to a dataset from online discussions in seven online classes. Informed by a conceptual model of social interaction in online discussions, this modeling included (a) a learner attribute capturing aspects of temporal participation, (b) social dynamics factors such as preferential attachment and reciprocity, and (c) turn- by-turn sequential patterns. Results showed that learner activity and familiarity from recent interactions affected their propensity to form ties. Turn-by-turn sequential patterns, that capture individual posting in bursts, explain how two-star network patterns form. Since two-star network patterns could further facilitate small group formation in the network, we expected the models to also capture communication in triads (i.e. triadic closure). Yet, models, devoid of the content of exchanges, did not capture the social dynamics well, and failed to predict patterns for communication across triads. By bringing in discourse features, future work can investigate the role of knowledge building behaviours in triadic closure of digital networks. This study contributes fresh insights into social interaction in online discussions, calls for attention to micro-level temporal patterns, and motivates future work to scaffold learner participation in similar contexts.
Content may be subject to copyright.
Socio-Temporal Dynamics in Peer Interaction Events
Bodong Chen
College of Education and Human Development
University of Minnesota–Twin Cities
Minneapolis, USA
Oleksandra Poquet
Centre for Change and Complexity in Learning (C3L)
University of South Australia
Adelaide, SA, Australia
Asynchronous online discussions are broadly used to support peer
interaction in online and hybrid courses. In this paper, we argue
that the analysis of online peer interactions would benet from the
focus on relational events that are temporal and occur due to a range
of factors. To demonstrate the possibility, we applied Relational
Event Modeling (REM) to a dataset from online discussions in seven
online classes. Informed by a conceptual model of social interaction
in online discussions, this modeling included (a) a learner attribute
capturing aspects of temporal participation, (b) social dynamics
factors such as preferential attachment and reciprocity, and (c) turn-
by-turn sequential patterns. Results showed that learner activity
and familiarity from recent interactions aected their propensity to
form ties. Turn-by-turn sequential patterns, that capture individual
posting in bursts, explain how two-star network patterns form.
Since two-star network patterns could further facilitate small group
formation in the network, we expected the models to also capture
communication in triads (i.e. triadic closure). Yet, models, devoid of
the content of exchanges, did not capture the social dynamics well,
and failed to predict patterns for communication across triads. By
bringing in discourse features, future work can investigate the role
of knowledge building behaviours in triadic closure of digital net-
works. This study contributes fresh insights into social interaction
in online discussions, calls for attention to micro-level temporal
patterns, and motivates future work to scaold learner participation
in similar contexts.
Human-centered computing Social network analysis
Web-based interaction
Information systems
Social net-
working sites;Clustering.
Relational event modelling, temporality, digital peer networks
ACM Reference Format:
Bodong Chen and Oleksandra Poquet. 2020. Socio-Temporal Dynamics in
Peer Interaction Events. In Proceedings of the 10th International Conference
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for prot or commercial advantage and that copies bear this notice and the full citation
on the rst page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specic permission
and/or a fee. Request permissions from
LAK ’20, March 23–27, 2020, Frankfurt, Germany
©2020 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-7712-6/20/03. . . $15.00
on Learning Analytics and Knowledge (LAK ’20), March 23–27, 2020, Frank-
furt, Germany. ACM, New York, NY, USA, 6 pages.
Online learning frequently appears in the strategic plan of educa-
tional institutions. In higher education, an increasing number of
universities and colleges are putting their degrees online, while
hybrid courses have become the “new normal”. In K-12, the applica-
tion of online learning is also growing, not only to facilitate student
learning but also to enable richer teacher professional development
In online and hybrid learning experiences, asynchronous online
discussion is broadly used to support learners’ social participa-
tion and peer interaction. Online learning environments are often
equipped with asynchronous discussion forums. Instructional de-
signs also tend to incorporate online discussions to facilitate various
learning activities, such as reection on readings, question and an-
swers, and peer dialogues [
]. To support productive discussion
experiences, pedagogical strategies are proposed such as incorpo-
rating questioning strategies [
], engaging students in role-taking
], and inviting learners to take responsibility in identifying key
or promising ideas in discussion threads [
]. Technological in-
novations are also proposed to promote active participation and
deep engagement in online discussions. For instance, new graphi-
cal interfaces are invented to better support purposeful attention
to existing posts in online discussions[
]; social network sites,
such as Facebook and Edmodo, are adopted in both formal and
informal learning settings to foster peer connectedness and collabo-
ration [
]. Empirical studies in various contexts have reported
a positive association between discussion participation and learn-
ing performance [
], showing the promise of sustaining
eective discussions for learning.
In spite of prior work in these areas, the fundamental social
mechanisms that drive peer interaction in asynchronous online
discussions remain under-examined. Extant work rarely addresses
peer interaction in online discussions as a complex phenomenon
shaped by a range of factors related to learner attributes, social
dynamics, pedagogical designs, and technological constraints. This
study attempts to bridge the knowledge gap by tackling the fol-
lowing overarching question: What are the mechanisms of social
interaction in asynchronous online discussions? To this end, we
applied relational event modeling (REM)—a powerful network mod-
elling approach—to a rich dataset comprising seven online classes.
In the following sections, we rst construct a conceptual model of
social interaction in online discussions. We then introduce the REM
study and report its ndings. Finally, conceptual, pedagogical, and
methodological implications are discussed.
LAK ’20, March 23–27, 2020, Frankfurt, Germany Bodong Chen and Oleksandra Poquet
Conceptually, we posit that social interactions in online forums
need to be examined as events instead of states. Current research
is predominately focused on examining the state of “social rela-
tions” as static relationship networks. We argue for the need to
unpack the process in which a series of events engender a partic-
ular state. For an event, e.g., A posts a comment on a post made
by B, we need to recognize a “conceptual leap” often made by re-
searchers from such a socio-technical interaction (that involves
people, ideas, and technology) to an interpersonal interaction (i.e.,
A->B). This conceptual leap, often done unintentionally, has im-
portant consequences for our understanding of online discussions.
One signicant consequence is we lose sight of the nuanced context
in which an event occurs, as well as the unfolding events come
to shape future events. Therefore, we propose to investigate in-
teractions in online discussions as temporal networks that unfold
temporally through participation that focus on relational events in-
stead of states engendered by these events. To conceptually ground
this study, in this section we outline dierent factors that could
impact the formation of such temporal activity-driven networks in
online discussions.
2.1 Learner Heterogeneity in Posting Activity
Learners bring unique understandings, habits, and dispositions
into online discussions. Learners may set their own participation
goals based on personal interests and aspirations. They may have a
unique schedule of participation. Some would prefer more “listen-
ing” before posting ideas.
The analysis of relational events needs to nd ways to account
for learner heterogeneity. For example, to capture the intensity
of learners’ posting activity, one common approach is to count
the number of posts contributed by each learner. The timing and
spacing of each learner’s engagement can also be characterized
using log data [
]. In an early work, Macfadyen and Dawson [
showed a strong association between the number of discussion
replies posted and learner nal grade (r =.94, p<.0001). Joksimovic
et al. [
] note the number of posts as the most common measure
of social learning in MOOCs. Although studies have reported di-
verse evidence around the strength of relationship between learner
posting activity and grades, forum posts are commonly included in
modelling learner engagement and predicting completion [14].
2.2 Pedagogical Practices
Online discussions are not created equal. Research shows factors
such as discussion guidelines, deadlines, feedback, and instructor
presence aect the resulting discourse [
]. Types of discussion activ-
ities, such as discussing course readings, sharing new stories, peer
feedback, and building group knowledge, are all of educational value
and present in online and hybrid courses. The discussion guidelines
would naturally shape the nature and intensity of discussion. So
does the instructor’s decision to participate in the discussion [8].
Among these factors, time, in particular, shapes the learner’s
perception, experience, understanding of time online, and hereby
one’s online participation [
]. There is a delicate dance between the
instructor’s temporal expectations (e.g., participation deadlines) and
learner heterogeneity in individual posting that would contribute
to social structures in an online discussion space. Recent work
highlights the association between learners’ social positions and
their timing behaviors in online discussions; in particular, learners
organize themselves around the pedagogical structure of deadlines,
leading to varied conditions for social encountering and positioning
2.3 Social Dynamics
For a given pedagogical condition, there are a range of sociological
factors that could inuence social interaction. For example, while
instructor presence may set a general tone for class dialogues [
at a micro-level being the instructor poses superior pressure that
attracts student attention and interactions. Empirical studies, how-
ever, have arrived at mixed ndings, with some ndings showing
the instructor’s posts did not show higher probabilities of being
replied [
]. Peer pressure can have a positive impact on participa-
tion intent as well [
]. Even when the instructor sets participation
goals, active or “super” posters in a class could generate peer pres-
sure on a learner’s participation. Like in other social environments,
being popular (e.g., receiving more replies) helps attract even more
attention [
]; this eect of preferential attachment is observed in
scientic collaboration networks and social network sites [18, 19].
Reciprocity is also found to play a role in online discussions based
on quantitative and qualitative accounts [
]. These sociologi-
cal factors contribute to one’s social or relational capital in online
discussion environments, which in turn shapes forthcoming inter-
Figure 1: Conceptual model of social interaction in online
2.4 Technological Features
Besides individual, pedagogical, and social conditions, technolog-
ical congurations of online discussions are too often neglected
in research reports. As a matter of fact, technological features of
forums shape a learner’s interaction with technology and peers [
For example, when discussion posts are presented as a dynamic
hyperbolic tree instead of a discussion thread, students are found
purposefully selecting which discussion threads to read [
]. When
Socio-Temporal Dynamics in Peer Interaction Events LAK ’20, March 23–27, 2020, Frankfurt, Germany
online discussions happen in specialty software designed by re-
searchers, we need to be transparent about “pedagogical biases”
embodied in technological features [
]. When general purpose
tools such as Facebook and Twitter are used, we need to be clear
about how their technological features are mobilized to serve ed-
ucational purposes [
]. In addition, we need to interrogate ways
in which technological features and algorithmic decisions (e.g.,
sorting algorithms) implicitly shape interaction patterns in online
discussions [4].
Taken together, social participation and interaction in asynchro-
nous online discussions is a complex phenomenon shaped by vari-
ous factors. Figure 1 illustrates a conceptual model we developed to
guide our investigation of contributing factors. This model intends
to help us operationalize our study and is by no means comprehen-
sive. Cross-cutting factors may also exist and interact with factors
elaborated above.
To examine social interaction in online discussions as networks un-
folding temporally, we adopted Relational Event Modelling (REM),
a framework that allows researchers to understand how networked
structures emerge from relational events and shape subsequent
interactions [
]. We applied REM to a secondary dataset from an
online discussion environment.
3.1 Description of Data
3.1.1 Context. The study was situated in seven online courses at a
large public university in the United States. Six courses were about
business management, information technologies, and ethics.
3.1.2 The Discussion Environment. All courses incorporated Yel-
lowdig, a discussion environment for educational use, as a primary
medium for peer interaction. Students in all classes participated
weekly on Yellowdig to discuss readings and share ideas. Dier-
ent courses have set dierent expectations of participation. For
example, one course required students to minimally contribute one
post in response to instructor prompts and comment on two posts
each week; another course asked students to generally share news
articles related to the course content and comment on each other
to earn up to 10 points each week.
3.1.3 Data Structure. The discussion environment Yellowdig re-
sembles social network sites like Facebook. Students could initiate
a post, known as a pin, that can be commented on or reacted to by
members of the class. Students could also mention each other in
a post or comment. Like most social network sites, Yellowdig was
also implemented with a news feed powered by a sorting algorithm,
which placed posts with the most recent activities on the top. As
such, technological features in Yellowdig are designed to promote
social interaction.
Interaction data from Yellowdig, including 6,354 discussion ac-
tions (e.g., making a pin, commenting on a pin, reacting to a pin)
from a total of 281 students, were the primary data source. Course
materials, including the syllabus and weekly announcements, were
gathered to contextualize the interpretation.
3.2 Data Analyses
3.2.1 Profiling Learners on Timing. Given that no demographic
information, performance, or discourse about the learners was
available for this study, modelling had very little information about
the learners. To this end, we adopted an approach similar to that
in Park et al. [
] where nite mixture models [
] were used to
group learners based on the total of their logs per each day of
the week. These latent classes captured how much platform use
learner engaged in on each given weekday. The latent classes, i.e.
those interpreted as time-consistent posters and last-minute posters,
were regressed to the volume of platform-use-logs, to help interpret
the clusters. Information about the timing of activity was used as
controls in the nal REM model to improve the estimation of the
relationship between posting events and emergent social dynamics.
Clustered logs included diverse granular behaviors, namely: cre-
ating a pin (making an initial thematic post), visiting a pin, editing
a pin, editing a comment, liking a comment, ‘loving’ a pin, deleting
a pin, as well as creating and voting a pin.
We do not include any statistics related to the timing proles,
as this paper’s scope is limited to presenting the results of the
modelling, and timing proles were used only to capture similarity
in synchronous patterns of the learners reported in the nal REM
models for each course.
3.2.2 Relational Event Modelling. To address the major research
question about potential contributing factors, we applied a novel
network analysis approach named Relational Event Modeling (REM)
]. Detailed explanations of REM can be found elsewhere [
Formally, Butts and Marcum [
], expressed a relational event model
λaAtθ=(exp(θτu(s(a),r(a),c(a),Xa,At)) i f a A(At)
0otherwise (1)
represents the hazard of potential event aat time tgiven
history At; whereas a vector of real-valued parameters is
; and
uis a vector of statistics, such as the sender, receiver, covariates,
prior event history (p.8).
Put simply, a relational event is dened as a discrete event gen-
erated by a social actor and directed toward one or more targets”
, p. 159]. For example, Student A replies to B at time t1 is one
relational event a1, and B responds to A at a later time t2 is a newer
relational event that might have been triggered by a1. The cen-
tral goal of REM is to “understand how past interactions aect
the emergence of future interactions” [
, p. 533], based on a set of
derived statistics about (a) past relational events (e.g., frequency of
previous A
B events), (b) actor attributes (e.g., gender, roles), and
(c) exogenous contextual factors (e.g., friendship). Take the statistic
of reciprocity, for example. It indicates the likelihood of Student B
replying to A if A has recently commented on B. Statistics as such
capture both social and sequential information and are also referred
to as Sequential Structural Signatures (SSSs) of group dynamics
]. In this study, we trained multiple relational event models in-
corporating a range of SSSs, which could potentially shape peer
interaction in our research context. We tted these models follow-
ing a forward selection strategy and evaluated model adequacy
LAK ’20, March 23–27, 2020, Frankfurt, Germany Bodong Chen and Oleksandra Poquet
Table 1: Relational event modelling of interactive activity that comprised: created_pin, created_comment, like_pin,
liked_comment, love_pin, unliked_comment events.
Course 1 Course 2 Course 3 Course 4 Course 5 Course 6 Course 7
Node & Dyad Attributes
Sending (Time Consistent (TC)) 0.01 (0.1) 0.4 (0.1) *** 0.3 (0.1) 0.5 (0.2) *** 0.34 (0.1) * 0.2 (0.1)* 0.2 (0.1) .
Receiving TC 0.23 (0.1) * 0.02 (0.1) 0.4 (0.1) ** -0.42 (0.2) * -0.2 (0.2) 0.1 (0.1) 0.1 (0.1)
Homophily TC Controlled for
Sending Teacher 0.8 (0.2) *** 0.7 (0.2) *** 0.4 (0.2) 0.5 (0.2)** 1 (0.2) *** -0.8 (0.4)* 0.1 (0.3)
Receiving Teacher -2.1 (0.4) *** -2 (0.3) *** -0.8 (0.2) ** -0.5 (0.2) * -0.7 (0.3) * 0.3 (0.2) . 0.7 (0.2) ***
Degree (NTDegRec) 9.6 12.6 (0.8)*** 11.3 (1.4) *** 11.5 (1) *** 7.9 (1.3)*** 14 (1) *** 14 (1.2) ***
Reciprocity (RRecSnd) 0.47 (0.1)** 1.04 (0.1) *** 0.8 (0.1) *** 0.6 (0.2) ** 0.44 (0.2) . 0.3 (0.2) . 0.56 (0.2)***
Familiarity (RSndSnd) 1.06 (0.1)*** 2.42 (0.1) *** 2.2 (0.1) *** 2.3 (0.1)*** 1.6 (0.2) *** 2 (0.11) *** 1.29 (0.12)***
Closure (ISPSnd) 0.04 (0.01)*** 0.06 (0.02)*** -0.06 (0.1) ** 0.03 (0.01)* 0.03 (0.4) 0.01 (0.01) 0.03 (0.08)***
Immedaite Eects
Reciprocity (AB-BA) -0.33 (0.4) 2.3 (0.4)*** 1.8 (0.4) ** 1.5 (0.6)* 0.99 (0.8) 0.9 (1) 2 (0.5)***
Pay-it-forward (AB-BY) -2.4 (0.3)*** 0.9 (0.2) *** 0.83 (0.27) ** 0.1 (0.3) -0.1 (0.4) -0.1 (0.3) 0.56 (0.2)***
Activity bursts (AB-AY) 1.22 (0.1)*** 4.5 (0.1) *** 3.58 (0.1) *** 3.4 (0.1)*** 2.74 (0.2) *** 3.8 (0.1) *** 3.8 (0.1) ***
Popularity (AB-XB) -0.48 (0.1)*** 2.6 (0.1) *** 1.6 (0.2) *** 1.1 (0.2)*** 1 (0.2) *** 1.7 (0.1) *** 1.4 (0.2)***
AIC Null 6897 34130 6213 7616 3503 13053 12042
BIC Null 6897 34130 6213 7616 3503 13053 12042
AIC Final 5610 27201 4916 6286 3003 10756 9875
BIC Final 65610 27274 4969 6342 3051 10818 9936
Accuracy 0.72 0.68 0.78 0.69 0.67 0.68 0.67
based on the AIC (Akaike information criterion) and BIC (Bayesian
information criterion) scores [
]. A maximum likelihood estimate
(MLE) in a model provides an indication of the odds of a relational
event given the specied conditions.
4.1 Social and Temporal Dynamics
Outputs of the relational event models are reported in Table 1.
The rst set of hypotheses related to the propensity of individual-
level attributes to aect tie formation. Specically, we examined if
learners with time-consistent patterns of logging into the platform
to post were also more likely to send or receive ties (Hypotheses
1a and 1b). We observed that those learners who engaged with the
platform on the same weekdays with eort spread out across several
days (i.e. time-consistent) showed a positive tendency to send ties
to other learners. A signicantly positive propensity for these more
self-regulated learners to send ties was observed in three out of
seven models. These learners were also more likely to receive ties -
with positive and signicant coecients in two models. It is worth
noting that the sending and receiving dynamics for time-consistent
posters was not co-occurrent, i.e. we did not observe it in the same
courses. Generally, this suggests that timing of posting is a factor in
how networked structures of digital learning communication form.
Another set of hypothesis related to the role of the instructor
in how communication evolves (Hypotheses 1c and 1d). In four out
of seven models, the instructor was more likely to send ties. In six
out of seven models, the instructor was less likely to receive ties.
This suggests that instructors were present in the communication,
but their involvement was likely to facilitate inter-peer behaviors,
rather than instructor-learner interactions.
In terms of the structural elements, we modelled series of dier-
ent hypotheses.
H2a. Learner total activity aected future incoming ties. This hy-
pothesis was conrmed as positive and signicant in six out of
seven models, with large coecients for all courses. That is, the
more active the learner was, the more likely she was to receive
replies with the course progression.
H2b. Networks are described by temporal reciprocity, i.e. fraction
of ties from A to B, aected future sending ties of B to A. This hypoth-
esis was conrmed as positive and signicant in four out of seven
models. That is, the more replies a learner sent to the same person,
the higher the likelihood that this person would also be replying
with the progression of time.
H2c. Familiarity aects future tie formation, i.e. recency of sending
from A to B, aects tie formation from A to B. Recency of sending
ties was found to aect tie-sending in the future through positively
signicant coecient in all seven models. That is, learners were
more likely to reply to someone in the future, if they had engaged
with the person in a discussion previously.
H2d. Two learners who reply to the same third learner will form a
tie, i.e. closure hypothesis. Although closure was signicant in four
models, the coecients were low, suggesting that closure was not
captured well.
Finally, the third set of hypotheses examined diverse patterns of
sequential micro-level patterns. The eects related to immediacy in
learner interactions improved the model t signicantly across all
the courses. Immediate reciprocity, i.e. B would respond to A right
after A would send a tie to B, was conrmed through positive and
signicant coecients in four models (Hypothesis 3a). Immediate
two-path feature (A sending ties to B, B sending ties to C right
afterwards) that hypothesized that learners were likely to engage
if they received a reply, produced mixed results (Hypothesis 3b).
Socio-Temporal Dynamics in Peer Interaction Events LAK ’20, March 23–27, 2020, Frankfurt, Germany
Overall the coecients were low, and positive or negative across
the models. Perhaps one of the strongest immediacy parameters
was that of activity bursts (Hypothesis 3c). Sending ties consequently
to dierent learners (A to B, A to C) in a two-star pattern showed
large coecients that were positive and signicant in all seven
models. This reects the nature of posting behaviour as a part of
assignment - when learners respond to several learners as they log
into the platform. Finally, popularity, or more precisely, sequential
incoming replies, modeled as a temporal in-degree 2-star (A to B, C
to B) showed positive and signicant coecients across all models,
except one (Hypothesis 3d).
4.2 Goodness of Fit
Although all the nal REM models (tted iteratively to capture
degree, reciprocity, closure, as well as exogenous factors, and imme-
diacy patterns) showed improvement in AIC from the null model
(reported in the Results, Table 1), the accuracy of prediction var-
ied. We examined what events (posting activity) the models were
failing to predict. From the plots of surprising events (i.e. events
that the models did not predict well), it appeared that closure was
not explained by the closure patterns included in the model. In
other words, discussion online was resulting in patterns of triads
that were not explained by any of the parameters included in the
models. Although we controlled for possible homophily through
attributes of learner activity and learner timing, the models still
did not capture clustering well. One reason for this could be that
these models exclusively used log data and timestamps from the
discussion forums. We suggest that the closure in the network is
subject to the text posted by learners that mediates these peer in-
teractions. It is also plausible that the closure resulted from visiting
someone’s pin that was found attractive. Perhaps, interviews with
students and collection of self-reports around their reactions to
posted discourse can help shed light on the potential dynamics.
The study is framed from the perspective that interactions in digital
learning settings would benet from focusing on relational events
rather than relational states. To demonstrate the possibility, we
applied Relational Event Modeling (REM) to a dataset from seven
online classes. In the modelling, we included a learner attribute of
temporal participation, alongside socio-temporal factors such as
preferential attachment and reciprocity formed through timing or
sequences of peer interaction. We found that temporality adds a new
dimension to the analyses of peer interactions. Social dynamics and
temporal dynamics in these networks are intertwined as showed
by improvement in the models when adding immediacy eects.
Activity is heavily driving network formation, whereas social eects
of reciprocity and closure are rather low.
Approaching digital peer interactions as relational events high-
lights that peer event networks are formed by learner activity.
Teacher presence was observed as signicant across most courses,
but it appears not to impact learner reciprocal activity towards the
instructor. Instead, tie formation between the learners seems to
result from rather random processes, such as familiarity based on
recency and co-occurrence. We have not observed the eect from
homophily based on timing controls.
REMs also reveal that temporal behaviors at the dyadic level may
emulate structures similar to social dynamics (i.e. transitivity, two-
paths, or two-stars). Yet, failure of the model to predict clustering
in the network suggests that these patterns only look like social
dynamics, without necessarily representing it.
Empirical ndings from this study are of scholarly and practical
signicance. Compared to earlier studies applying REM in educa-
tional contexts, we theorised dierent processes of network forma-
tion in online discussions, applied REM to a unique context, and
combined temporal and social dimensions of network formation. To
better understand underlying social dynamics in such text-mediated
peer interaction events, we suggest that future work should focus
on the role of knowledge construction behaviors in relation to
triadic closure patterns and homophily. It may be plausible that
learners exchange interactions based on demographic variables
underpinning their identities. However, it is more likely that the
complementarity of learner communication strategies drives the
interpersonal aspects of their interaction. Future work can also
explore comparing and combining REM with discourse analyses.
This study contributes fresh insights into social interaction in
online discussions, calls for attention to micro-level temporal pat-
terns, and motivates future work to scaold learner participation in
similar contexts. Results suggest that including information about
temporality of peer interaction events is relevant. However, further
information is required to better understand what mediates these
interaction events.
Reuven Aviv and Gilad Ravid. 2005. Reciprocity analysis of online learning
networks. Journal of Asynchronous Learning Networks 9, 4 (2005), 3–13.
Carter T Butts. 2008. A Relational Event Framework for Social Action. Sociological
Methodology 38, 1 (2008), 155–200.
Carter T Butts and Christopher Steven Marcum. 2017. A relational event approach
to modeling behavioral dynamics. In Group Processes. Springer, 51–92.
Bodong Chen and Tianhui Huang. 2019. It is about timing: Network prestige in
asynchronous online discussions. Journal of Computer Assisted Learning (2019).
Bodong Chen, Marlene Scardamalia, and Carl Bereiter. 2015. Advancing
knowledge-building discourse through judgments of promising ideas. Interna-
tional Journal of Computer-Supported Collaborative Learning 10, 4 (2015), 345–366.
Wing Sum Cheung, Khe Foon Hew, and Connie Siew Ling Ng. 2008. Toward an
understanding of why students contribute in asynchronous online discussions.
Journal of Educational Computing Research 38, 1 (2008), 29–50.
Partha Deb and Pravin K Trivedi. 2013. Finite mixture for panels with xed
eects. Journal of Econometric Methods 2, 1 (2013), 35–51.
Vanessa Paz Dennen*. 2005. From message posting to learning dialogues: Factors
aecting learner participation in asynchronous discussion. Distance Education
26, 1 (2005), 127–148.
Mark Guzdial and Jennifer Turns. 2000. Eective discussion through a computer-
mediated anchored forum. The journal of the learning sciences 9, 4 (2000), 437–469.
Jim Hewitt and Clare Brett. 2011. Engaging learners in the identication of
key ideas in complex online discussions.. In Connecting Computer-Supported
Collaborative Learning to Policy and Practice (CSCL2011 Conference Proceedings).
I Jo, Yeonjeong Park, and H Lee. 2017. Three interaction patterns on asynchronous
online discussion behaviours: A methodological comparison. Journal of Computer
Assisted Learning 33, 2 (2017), 106–122.
Srećko Joksimović, Dragan Gašević, Vitomir Kovanović, Bernhard E Riecke, and
Marek Hatala. 2015. Social presence in online discussions as a process predictor
of academic performance. Journal of Computer Assisted Learning 31, 6 (2015),
Srećko Joksimović, Oleksandra Poquet, Vitomir Kovanović, Nia Dowell, Caitlin
Mills, Dragan Gašević, Shane Dawson, Arthur C Graesser, and Christopher
Brooks. 2018. How do we model learning at scale? A systematic review of
research on MOOCs. Review of Educational Research 88, 1 (2018), 43–86.
René F Kizilcec and Emily Schneider. 2015. Motivation as a lens to understand
online learners: Towarddata-driven design with the OLEI scale. ACMTransactions
on Computer-Human Interaction (TOCHI) 22, 2 (2015), 6.
LAK ’20, March 23–27, 2020, Frankfurt, Germany Bodong Chen and Oleksandra Poquet
Contractor Noshir Leenders, Roger and Leslie DeChurch. 2016. Once upon a
time: Understanding team processes as relational event networks. Organizational
Psychology Review 6, 1 (2016), 92–115.
Leah P Macfadyen and Shane Dawson. 2012. Numbers are not enough. Why
e-learning analytics failed to inform an institutional strategic plan. Journal of
Educational Technology & Society 15, 3 (2012), 149–163.
Farshid Marbouti and Alyssa Friend Wise. 2016. Starburst: a new graphical
interface to support purposeful attention to others’ posts in online discussions.
Educational Technology Research and Development 64, 1 (2016), 87–113.
Alan Mislove, Hema Swetha Koppula, Krishna P Gummadi, Peter Druschel, and
Bobby Bhattacharjee. 2008. Growth of the ickr social network. In Proceedings of
the rst workshop on Online social networks. ACM, 25–30.
Mark EJ Newman. 2001. Clustering and preferential attachment in growing
networks. Physical review E 64, 2 (2001), 025102.
Murat Oztok, Lesley Wilton, Kyungmee Lee, Daniel Zingaro, Kim Mackinnon,
Alexandra Makos, Krystle Phirangee, Clare Brett, and Jim Hewitt. 2014. Polysyn-
chronous: dialogic construction of time in online learning. E-learning and digital
media 11, 2 (2014), 154–161.
Jihyun Park, Renzhe Yu, Fernando Rodriguez, Rachel Baker, Padhraic Smyth, and
Mark Warschauer. 2018. Understanding Student Procrastination via Mixture
Models. International Educational Data Mining Society (2018).
Peter Ractham, Laddawan Kaewkitipong, and Daniel Firpo. 2012. The use of Face-
book in an introductory MIS course: Social constructivist learning environment.
Decision Sciences Journal of Innovative Education 10, 2 (2012), 165–188.
Jeremy Riel, Kimberly A Lawless, and Scott W Brown. 2018. Timing Matters:
Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of
Work in Self-Paced Online Teacher Professional Development Courses. Journal
of Learning Analytics 5, 1 (2018), 25–40.
Christina Sadowski, Mika Pediaditis, and Robert Townsend. 2017. University
students’ perceptions of social networking sites (SNSs) in their educational expe-
riences at a regional Australian university. Australasian Journal of Educational
Technology 33, 5 (2017).
Marlene Scardamalia and Carl Bereiter. 2008. Pedagogical biases in educational
technologies. Educational Technology (2008), 3–11.
Luis Vaquero and Manuel Cebrian. 2013. The rich club phenomenon in the
classroom. Scientic reports 3 (2013), 1174.
C-H Wang. 2005. Questioning skills facilitate online synchronous discussions.
Journal of Computer Assisted Learning 21, 4 (2005), 303–313.
Alyssa Wise and Ming Ming. Chiu. 2011. Knowledge construction patterns in
online conversation: A statistical discourse analysis of a role-based discussion
forum.. In Connecting Computer-Supported Collaborative Learning to Policy and
Practice (CSCL2011 Conference Proceedings). 64–71.
Samantha Won, Michael Evans, and Lixiao Huang. 2017. Engagement and knowl-
edge building in an afterschool STEM Club: analyzing youth and facilitator
posting behavior on a social networking site. Learning, Media and Technology 42,
3 (2017), 331–356.
Xue Yang, Yan Li, Chuan-Hoo Tan, and Hock-Hai Teo. 2007. Students’ partic-
ipation intention in an online discussion forum: Why is computer-mediated
interaction attractive? Information & Management 44, 5 (2007), 456–466.
Binbin Zheng and Mark Warschauer. 2015. Participation, interaction, and aca-
demic achievement in an online discussion environment. Computers & Education
84 (2015), 78–89.
Daniel Zingaro and Murat Oztok. 2012. Interaction in an asynchronous online
course: A synthesis of quantitative predictors. Journal of Asynchronous Learning
Networks 16, 4 (2012), 71–82.
... Lower-right: A two-mode network representing relational states among students and annotation threads generated by relational events so far times throughout a semester. To examine the mediated process of social interaction in web annotation, we need to model relational events in the annotation process rather than collapsing a series of events into relational states between actors/students (Chen & Poquet, 2020;Butts et al., 2023). ...
... Semantic cohesion of the thread was overall a stronger predictor of future relational events in the web annotation context. This finding represents a major step forward from earlier studies that rely on analysis of static social networks of students (Chen, 2019;Chan & Pow, 2020), or studies that considered event-level factors but did not capture features of mediational artifacts (Vu et al., 2015;Chen & Poquet, 2020). This study also treats semantic features as temporal constructs, extending prior work that uses static semantic features to describe online posts Raković et al., 2020) to reflect the evolving context of online discourse. ...
... This work builds on and extends a long-term interest in applying social network analysis to online discussion data (Ye & Pennisi, 2022). REM is appropriate for analyzing student interactions because it operates at the event level instead of prematurely collapsing relational events to interpersonal relationships (Chen & Poquet, 2020). In the context of web annotation specifically, by applying REM to two-mode networks of students and annotations, we retained annotation artifacts in the network analysis, making it possible to investigate roles played by artifacts in mediated social interaction. ...
Full-text available
Web annotation environments are widely used in education based on the premise that student interaction in these environments benefits individual and group learning. However, there is little research on factors driving student interaction in web annotation activities. In this study we asked: What dynamics could explain social interaction among students in web annotation activities in college classrooms? Recognizing the mediated nature of social interaction in web annotation, we hypothesized that student interaction is driven by multiple factors including previous relational events and semantic features of annotation content. Following a novel network analysis method named relational event modeling, we analyzed a rich dataset from four online classes. Results indicated annotation popularity was initially predictive of student replies, meaning popular annotation threads were more likely to attract new replies. However, this effect nearly diminished when adding thread-level semantic cohesion to the model, indicating an significant role played by semantic cohesion in attracting new responses. This paper makes important progress towards modeling social interaction in digital environments as a dynamic, mediated phenomenon. This study contributes empirical insights into web annotation and calls for future work to investigate social interaction as a dynamic network phenomenon.
... Recently, efforts have been directed to using learners' data to understand learning as a dynamic and complex process, i.e., understanding the temporal nature of learning that includes changes, phases and sequences as well as the complex interactions between learners, learning resources, and environments [10]. Such an approach has emerged to address one of the shortcomings of using the data in "aggregate", i.e., counts of static discrete events with no connection to time or temporality [11][12][13][14]. ...
... For instance, temporal networks could enable the tracing of the flow of ideas, information, knowledge building as well as the diffusion of knowledge [11]. Temporal networks can be also used to unveil the temporal aspects of how students form ties, how they interact and how they build learning communities [14]. The role of temporal networks extends to other social phenomena such as socially and co-regulated learning and, in fact, seems much better suited than the commonly used methods (e.g., sequence mining). ...
... Additionally, they found that reachability -the temporal reach and range of influence of nodes-had slightly higher correlation coefficients than static centrality measures. Chen and Poquet [14] applied Relational Event Modeling (REM) to analyze peer interactions in online learning taking into account the temporal aspects thereof and not only the relational. They found that interactions between the learners seems to result from rather random processes, such as familiarity based on recency and co-occurrence, rather than homophily. ...
Conference Paper
Full-text available
Research on online learning has benefited from intensive data collection to understand students' online behavior and performance. Several learning analytics techniques have been operationalized to examine the temporal nature of learning that includes changes, phases, and sequences of students' online actions. Moreover, to account for the relational nature of learning, researchers have harnessed the power of network analysis to model the relational dimensions of data, mapping connections between learners and resources, and discovering interacting communities. However, prior research has rarely combined the two aspects (temporal and relational), but rather most researchers rely on aggregate networks where the time dimension has been ignored. To combine both these aspects, temporal networks provide a rich framework of statistical and visualization techniques that allow to fully understand, for instance, the evolution and building up of learning communities, the sequence of co-construction of knowledge, the flow of information, and the building of social capital, to name a few examples. Since temporal networks have been rarely used in educational research, with this study, we aim to provide an introduction to this method, with an emphasis on the differences with conventional static networks. We explain the basics of temporal networks, the different subtypes thereof, and the measures that can be taken, as well as examples from the few existing prior works.
... Capturing temporality is essential for generating meaningful learning analytics as learning is a continuous process that occurs over time [25], especially in collaborative and social learning contexts, where the temporal changes in social ties and interpersonal relationships are influential to learning outcomes [44]. Therefore, empowering SNA with the ability to capture and analyse the temporality of homophily remains a gap in existing learning analytics and network research [5]. ...
... An average social participation level was calculated for each quadrant, resulting in four clustering features for each student (e.g., Q1, Q2, Q3, and Q4), which correspond to students' social participation patterns over the eight weeks. For example, students' social participation levels in the first six reading sessions (1)(2)(3)(4)(5)(6), captured in each session, were averaged to compute a value representing their social participation level during Q1. This procedure was performed to calculate a value for each of the four quadrants and each student. ...
... Research questions asked at a network level can describe network structures and mechanisms generating them (e.g. [4,29,60]). This becomes useful because a network structure can serve as group-level indicator caused by a specific pedagogical and technological setting [5,42] or as a signal of desired outcomes, such as team's performance [43]. ...
... Second, statistical modelling in LA has only recently started to explicitly include temporal aspects of learner activity in socio-technical networks and overall participation levels at the node level (e.g. [4]). Otherwise, researchers used ERGMs to model forum communication as a network of binary ties between the learners, not as a network of valued ties (e.g. ...
Full-text available
Network analysis, a suite of techniques to quantify relations, is among core methods in learning analytics (LA). However, insights derived from the application of network analysis in LA have been disjointed and difficult to synthesize. We suggest that such is due to the naïve adoption of network analysis method into the methodologies of measuring and modelling interpersonal activity in digital learning. This chapter describes the diversity of empirical research using network analysis as a cacophony of network approaches. Focusing on LA studies that evaluate social behavior of individuals or model networks, the chapter exemplifies aspects of the analytical process that require rigor, justification, and alignment to overcome the cacophony of empirical findings. The chapter argues that the clarity of network definitions, hypotheses about network formation, and examination of the validity of individual-level measures are essential for coherent empirical insights and indicators. Future work should also make effort to model the temporal nature , multiplex ties, and dynamic interaction between the levels where interpersonal interactions unfold.
... In parallel to these more conventional applications, recent work has started to extend inquiry that is typical of social science and complex network research to educational settings, particularly those that are digitally mediated. Examples include research focused on network mechanisms (i.e., why digital networks form [37], (identification of network measurements that properly account for time in relational processes [38], and network approaches for the analysis of multivariate psychological survey data [39], [40]. Routinely collected digital data of student location, such as WiFi, have also been analyzed via network approaches to understand student collocation in face-to-face settings in relation to performance [41]. ...
... Network inference methods go beyond describing the network and offer a powerful analytical framework for causal inference and theory formulation that can help understand the processes behind network generation while accounting for the complex dependencies between network elements [106], [113]. In doing so, it allows us to explain why a phenomenon occurs, i.e., why a student chose to engage in an interaction, why and how a collaborative group formed, or why there is an association between an element of discourse and another [37]. Given the complexity, the relational nature, and the multiple dependencies between learners and learning processes, network inference methods seem to offer a muchneeded solution that could advance our understanding of learning [114]. ...
Full-text available
For over five decades, researchers have used network analysis to understand educational contexts, spanning diverse disciplines and thematic areas. The wealth of traditions and insights accumulated through these interdisciplinary efforts is a challenge to synthesize with a traditional systematic review. To overcome this difficulty in reviewing 1791 articles researching the intersection of networks and education, this study combined a scientometric approach with a more qualitative analysis of metadata, such as keywords and authors. Our analysis shows rapidly growing research that employs network analysis in educational contexts. This research output is produced by researchers in a small number of developed countries. The field has grown more recently, through the surge in the popularity of data-driven methods, the adoption of social media, and themes as teacher professional development and the now-declining MOOC research. Our analysis suggests that research combining networks and educational phenomena continues to lack an academic home, as well as remains dominated by descriptive network methods that depict phenomena such as interpersonal friendship or patterns of discourse-based collaboration.
... Therefore, time has become a quintessential aspect in several learning theories, frameworks and methodological approaches to learning [3][4][5]. Modeling learning as a temporal and relational process is, nevertheless, both natural, timely and more tethered to reality [4,6]. Traditionally, relations have been modeled with Social Network Analysis (SNA) and temporal events have been modeled with sequence analysis or process mining [3,7].Yet, researchers have rarely combined the two aspects (the temporal and relational aspects) in an analytics framework [1]. ...
Full-text available
Learning involves relations, interactions and connections between learners, teachers and the world at large. Such interactions are essentially temporal and unfold in time. Yet, researchers have rarely combined the two aspects (the temporal and relational aspects) in an analytics framework. Temporal networks allow modeling of the temporal learning processes i.e., the emergence and flow of activities, communities, and social processes through fine-grained dynamic analysis. This can provide insights into phenomena like knowledge co-construction, information flow, and relationship building. This chapter introduces the basic concepts of temporal networks, their types and techniques. A detailed guide of temporal network analysis is introduced in this chapter, that starts with building the network, visualization, mathematical analysis on the node and graph level. The analysis is performed with a real-world dataset. The discussion chapter offers some extra resources for interested users who want to expand their knowledge of the technique.
... In contrast, interaction networks from digital data do not represent social relationships although some of the ties may potentially correspond to the relationship ties. Chen and Poquet (2020) argued that ties in social networks are constructed from the perceptions of a 'state' as to whether relationship between two people is perceived by either of them as real. Such ties are conceptually different from the ties in learner networks inferred from digital trace data, constructed from learner activity, representing 'events' of what has happened online. ...
Full-text available
Interpersonal online interactions are key to digital learning pedagogies and student experiences. Researchers use learner log and text data collected by technologies that mediate learner interactions online to provide indicators about interpersonal interactions. However, analytical approaches used to derive these indicators face conceptual, methodological, and practical challenges. Existing analytical approaches are not well aligned with the theories of digital learning, lack rigor, and are not easily replicable. To address these challenges, we put forward a multi-level framework linking indicators of individual posting with group-level communication and emergent relational structures. We exemplify the use of the framework by analyzing twenty online and blended courses. Empirical insights demonstrate how indicators at these three levels relate to each other and to potential instructor decisions. Our conclusion highlights current gaps in the framework and the areas for future work.
... Nevertheless, to the best of our knowledge, in contemporary literature there are no attempts that utilize LA and EDM driven methods in operationalizing and measuring dimensions of openmindedness or it's subdomains of curiosity and tolerance. This remains a gap in LA and EDM research and is an area of investigation that could be promoted through various means of discourse analysis or social and network analysis (Chen & Poquet, 2020;Kovanović et al., 2017;Poquet & de Laat, 2021). ...
Full-text available
Educational research is increasingly implementing and studying new approaches for assessing attributes that go beyond conventional assessments of students’ cognitive ability. Despite decades of research, there remains a lack of consensus in describing these skills or attributes, variously termed “non-cognitive skills”, “21st century competencies”, “personal qualities”, “social and emotional learning skills”, and “soft/core skills”. Regardless, these skills and qualities reflect dimensions of learning that are broader than conventional curriculum knowledge. The importance of such skills has been well established in contemporary literature as highly relevant for success in school, university, the workplace, and engaged citizenship more broadly. The relatively new fields of learning analytics and educational data mining have introduced numerous novel methodologies to education research. This work has served to advance assessment models for social and emotional learning skills. Building on one of the most referenced social and emotional learning frameworks, this chapter provides a comprehensive overview of learning analytics methods for measuring skills such as creativity, critical thinking, or emotional regulation, among others. We recognize that the potential of learning analytics to measure SEL is largely under-utilized and pose possible ways to advance work in this domain.KeywordsMeasuring skillsLearning analyticsSocial and emotional learningFrameworksPsychometrics and learning analytics
Full-text available
Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.
Full-text available
FULL TEXT: ABSTRACT: Network analysis has contributed to the emergence of learning analytics. In this editorial, we briefly introduce network science as a field and situate it within learning analytics. Drawing on the Learning Analytics Cycle, we highlight that effective application of network science methods in learning analytics involves critical considerations of learning processes, data, methods and metrics, and interventions, as well as ethics and value systems surrounding these areas. Careful work must meaningfully situate network methods and interventions within the theoretical assumptions explaining learning, as well as within pedagogical and technological factors shaping learning processes. The five empirical papers in the special section demonstrate diverse applications of network analysis, and the invited commentaries from cognitive network science and physics education research further discuss potential synergies between learning analytics and other sister fields with a shared interest in leveraging network science. We conclude by discussing opportunities to strengthen the rigour of network-based learning analytics projects, expand current work into nascent areas, and achieve more impact by holistically addressing the full cycle of learning analytics.
Full-text available
Lay Description What is already known about this topic: Social interaction is important for learning, and asynchronous online discussions are broadly used to support social learning. Teaching practices and strategies have been developed to support student participation in online discussions. In some contexts, participation intensity predicts learning performance. However, researchers have documented a participation gap in online discussions. What this paper adds: We applied prestige, a sociological term, to a study of the participation gap in an online undergraduate class and revealed varied levels of prestige among students. Lower prestige students were not inactive; rather, they initiated an equivalent volume of connections with peers but were often ignored. The timing of discussion participation can be a probable contributor to the gap, as lower prestige students' participation was less timely and more compressed together. Implications for practice and/or policy: Pedagogical supports are needed to help students develop advanced self‐regulation skills conducive to productive discussion participation. The design of online discussion environments and analytics tools needs to consider students' discussion dispositions and behaviours.
Conference Paper
Full-text available
Time management is crucial to success in online courses in which students can schedule their learning on a flexible basis. Procrastination is largely viewed as a failure of time management and has been linked to poorer outcomes for students. Past research has quantified the extent of students' procrastination by defining single measures directly from raw logs of student activity. In this work, we use a probabilistic mixture model to allow different types of behavioral patterns to naturally emerge from clickstream data and analyze the resulting patterns in the context of procrastination. Moreover , we extend our analysis to include measures of student regularity-how consistent the procrastinating behaviors are-and construct a composite Time Management Score (TM). Our results show that mixture modeling is able to unveil latent types of behavior, each of which is associated with a level of procrastination and its regularity. Overall, students identified as non-procrastinators tend to perform significantly better. Within non-procrastinators, higher levels of regularity signify better performance, while this may be the opposite for procrastinators.
Full-text available
One feature of self-paced online courses is greater learner control over the timing of their work in a course. However, the greater timing flexibility that learners enjoy in such environments may play a different role in the learning process than has been previously observed in formal online or face-to-face courses. As such, the study of work timing merits further investigation. Toward this goal, this study forwards two measures that represent the timing of coursework: 1) the timing index, or the degree to which a participant completes 50% of their work, and 2) the spacing count, the frequency of work performed across the course timeframe. In this study, the authors demonstrate the use of these measures from a data set of 42 U.S. middle-school teachers who participated in a self-paced, online professional development course to support teacher implementation of a new blended-learning curriculum. Using the two measures, the authors identify timing behaviours of participants and examine the effects that timing has on teacher self-efficacy after completing the course. The two measures and visualizations demonstrated in this paper yield useful individual-level variables for course timing that can be used for further study on the effects on learning outcomes.
Full-text available
Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics) and learning outcomes (i.e., academic, social, and affective). The proposed model affords further inter-study comparisons as well as comparative studies with more traditional education models.
Full-text available
This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).
Full-text available
Higher education institutions, and the way education is delivered and supported, are being transformed by digital technologies. Internationally, institutions are increasingly incorporating online technologies into delivery frameworks and administration – both through internal learning management systems (LMS) and external social networking sites (SNSs). This study aims to explore how higher education students in a regional Australian dual-sector institute use and manage SNS for personal and study-related activities and their perceptions of how this impacts their educational experiences. This mixed-methods study involved a quantitative and qualitative survey of 355 vocational training and higher education students and in-depth focus groups with ten higher education students. Four key themes were identified through thematic analysis: SNS as a tool for fostering peer connectedness with fellow students; deliberate and distinct variation between personal and educational use of SNS; resistance to external SNS within education settings; and, need for a balance between digital and face-to-face learning and connectedness. Implications for curriculum design and delivery, and development of support for students in diverse learning contexts, are considered.
Full-text available
Social networking sites (SNSs) are popular technologies used frequently among youth for recreational purposes. Increasing attention has been paid to the use of SNSs in educational settings as a way to engage youth interest and encourage academically productive discussion. Potential affordances of using SNSs for education include knowledge building, collaborative communities, and the ability to document and share processes and designs. In this study, the SNS, Edmodo, is examined as an educational tool used with Studio STEM. Results indicated that youth appropriated Edmodo to exhibit engagement and articulate knowledge through reciting facts, acknowledging learning, and documenting progress with the guidance of instructors and facilitators. Based on results, we suggest that efforts to include SNSs in integrative science, technology, engineering, and mathematics programming for youth prioritize consistent monitoring and guidance by supportive and more knowledgeable others as this serves to develop community and encourage youth engagement.
An asynchronous online discussion (AOD) is one format of instructional methods that facilitate student-centered learning. In the wealth of AOD research, this study evaluated how students' behavior on AOD influences their academic outcomes. This case study compared the differential analytic methods including web log mining, social network analysis and content analysis which were selected by three interaction patterns: person to system (P2S), person to person (P2P) and person to content (P2C) interaction. Forty-three undergraduate students participated in an online discussion forum for 12 weeks. Multiple regression analyses with the predictor variables from P2S, P2P and P2C and with a criterion variable of a final grade indicated several interesting findings. For P2S analysis, visits on board (VOB) had a significant variable to predict final grades. Also, the result of P2P analysis proved that in-degree and out-degree centrality predicted final grades. The P2C results based on cognitive presence represent that students' messages were mostly affiliated to the exploration and integration levels and also predicted the final grades. This study ultimately demonstrated the effectiveness of using multiple analytic methodologies to address and facilitate students' participation at AOD.