Characterizing Social Interactions in Online Social Networks:
The Case of University Students
M. E. Sousa-Vieira, J. C. L´opez-Ardao and M. Fern´andez-Veiga
Department of Telematics Engineering, University of Vigo, Spain
Keywords:
Online Social Networks, Collaborative Learning, Social Networks Analysis.
Abstract:
The widespread use of computing and communications technologies has enabled the popularity of social net-
works oriented to learn. In a previous work, we studied the nature and strength of associations between
undergraduate students of an introductory course on computer networks, using an online social network em-
bedded in a learning management system. With datasets from two offerings of the same course, we mined the
sequences of questions and answers posted by the students to identify structural properties of the social graph,
patterns of collaboration among students and factors influencing the final achievements, concluding that the
structural properties most correlated to the final academic results are robust measures of centrality (degree and
eigenvector), which are already detectable since the first weeks of the course. In this work, we apply SNA to
graduate engineering students enrolled in a master level course in computer networks. The results obtained
show that quality participation in the social activities appears to be correlated with the final outcome of the
course, and that good students tend to show denser egonetworks. Our analysis contributes to the understanding
of the role of social learning among highly educated students.
1 INTRODUCTION
Information technology is changing the ways we
learn. The widespread use of computing and com-
munications technologies has enabled the formation
of personalcommunications or online social networks
(OSNs), and it is behind the popularity of social net-
works oriented to learn. Social learning (Vassileva,
2008; Hart, 2011) emphasizes the role of knowl-
edge gained through social relationships (real or vir-
tual), that is, private conversations, public debates,
exchange of ideas, sharing knowledge, collaboration,
cooperation, etc., regardless these taking place be-
tween peers or with experts.
A properly designed software platform which in-
tegrates contents, users and educational experiences
is key for the effectiveness of any social learning en-
vironment (SLE). The popular learning management
systems (LMSs), e.g., Moodle, Claroline, Black-
board, cannot offer full functionality for embedding
OSN features like direct interaction among the stu-
dents, a reputation system, or the creation of infor-
mal learning activities. Consequently, some genuine
SLEs have been recently developed (Rodrigues et al.,
2011; Thoms, 2011; Sousa et al., 2016), with a focus
on collaborative work. Since these kind of learning
platforms collect a detailed record of each student’s
activity, a growing body of research aims to under-
stand to what extent the social interactions among the
students reinforce their learning process or improve
the quality of the learning outcomes.
This type of data has been used to analyze the in-
dividual behavior of users, potentially for identifying
the behavior patterns that lead to success in learn-
ing (Lykourentzou et al., 2009; Macfadyen and Daw-
son, 2010). In other studies, the datasets are mined to
quantify how the information flow shapes the learn-
ing results, e.g., to discover the most influential stu-
dents or to find out how collaboration among groups
of students arise, and the impact of relationships on
performance of learners. In other words, whether the
structure of the community to which a student belongs
while he/she is engaged in the SLE has any substan-
tial correlation on his/her performance. Thus, math-
ematical techniques from the field of social network
analysis (SNA) are being increasingly applied to dis-
entangle the relationships taking place among social
actors in a SLE, and for understanding the distinctive
patterns arising from these interactions. The study
proposed in (Dawson, 2008) addresses learning com-
munities from a social network perspective, includ-
ing what relations are evident in these communities,
188
Sousa-Vieira, M., López-Ardao, J. and Fernández-Veiga, M.
Characterizing Social Interactions in Online Social Networks: The Case of University Students.
DOI: 10.5220/0006292701880199
In Proceedings of the 9th International Conference on Computer Suppor ted Education (CSEDU 2017) - Volume 2, pages 188-199
ISBN: 978-989-758-240-0
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
how media affect online relationships formation and
what benefits can result from successfully maintain
learning networks. The work described in (Cadima
et al., 2012) analyses two distributed social learning
networks in order to understand how characteristics
of the social structure can enhance students’ success.
In (Hommes et al., 2012), authors study the influence
of social networks, motivation, social integration and
prior performance on learning, proposing degree cen-
trality as a key predictor for students learning. A
theoretical model is developed in (Chung and Pare-
des, 2015) to investigate the association between so-
cial network properties, content richness in academic
learning discourse and performance, concluding that
these factors cannot be discounted in the learning pro-
cess and must be accounted for in the learning design.
In (Gaggioli et al., 2015) authors investigate the rela-
tionship between social network indices, creative per-
formance and flow in blended teams. The results in-
dicate that social network indices, in particular those
measuring centralization and neighbors interactions,
can offer useful insight into the creative collaboration
process. Related to the role of course facilitators, the
study proposed in (Skrypnyk et al., 2015) shows that
the teaching function becomes distributed among in-
fluential actors in the network, both human and tech-
nological, but the official course teachers preserve a
high level of influence over the flow of information
in the investigated course. Finally, the aim of the
study proposed in (Eid and Al-Jabri, 2016) is to em-
pirically examine various categories of social network
sites use, showing that there are significant positive
relationships between them and students learning.
In a previous work (Sousa et al., 2015), we ap-
plied SNA techniques and tools to mine the data col-
lected through our software platform, SocialWire, in
two consecutive editions of an undergraduate course
on computer networks, for discovering what factors
or variables have measurable correlation with the per-
formance of the students: his/her level of participa-
tion in the system, his/her position (importance) in-
side the network graph or his/her neighborhood. We
concluded that the structural properties most corre-
lated to the final academic results are robust measures
of centrality (degree and eigenvector), which are al-
ready detectable since the first weeks of the course.
In this paper we report on a similar trial with students
engaged in a master’s degree in engineering.
The rest of the paper is organized as follows. In
Section 2 we give an overview of the core social
engine, and describe the general principles of our
learning-enhanced social platform. The methodol-
ogy employed in the master level course is reported
in Section 3. Section 4 contains the main results of
the data mining applied to the datasets. Finally, some
concluding remarks and guidelines for further work
are included in Section 5.
2 THE LEARNING PLATFORM
SocialWire (Sousa et al., 2016) is a SLE purposely
designed to provide a complete networked learning
paradigm, including features not available in other
SLEs. For instance, SocialWire uses games and social
meritocracy as conducting threads. The software plat-
form is based on ELGG, a popular engine for develop-
ing OSNs, and allows the creation, assessment and
reporting of a range of collaborative activities based
on social interactions among the students, offering a
reward mechanism by means of ranking and reputa-
tion.
The platform was developed upon four building
blocks:
The online social network. SocialWire leverages
on the core of ELGG for reusing the fundamental
elements of a generic OSN. Every group (class-
room group) defined in the system has its own
wall to maintain open communication among all
its members. The group can also use common
tools in the social web for its virtual classroom
activities: classroom blog, collaborative publish-
ing and document editing, creation of web pages,
social tagging, files repositories with hierarchi-
cal structure (including a viewer for images, au-
dio, video and the usual document formats), and
event calendars. All the activity unfolded in the
classroom gets eventually reflected on the pub-
lic wall, so it can be commented, highlighted or
voted. Sharing videos, uploading a file, save and
send a link are extremely simple actions which the
user can invoke through an user interface deliber-
ately similar to an OSN user interface. The user-
friendliness is higher, as a bonus, and the learning
curve of the platform itself is greatly softened.
The formal learning processes. To furnish So-
cialWire with the usual features of a LMS, we
have developed custom software modules that ex-
tend the bare OSN based on ELGG. Specifically,
there exist modules for proposing and submitting
tasks (either online or offline), for the creation
and assessment of quizzes and questionnaires, for
the creation and processing of forms or polls, for
building an e-portfolio, for designing rubrics for
evaluation, and more. Another software module
gives the teachers the possibility of structuring
the learning units in their courses, for instance
Characterizing Social Interactions in Online Social Networks: The Case of University Students
189
weekly, monthly, by topic,... and adding to each
unit as many resources as they like.
The informal learning processes. SocialWire
opens the possibility of carrying out other sort of
activities requiring a higher degree of social inter-
action. This is done by means of the questions and
contests modules. Besides the usual grading pro-
cedure used in formal courses (on a numeric scale
or by discrete levels), in SocialWire the students
can receive “points” or “marks” for their works.
The points accumulated along the course deter-
mine their position in the students’ ranking. This
ranking serves primarily to send signals to the stu-
dents about their relative performance, in a way
that directly stimulates comparisons and that au-
tomatically conveys the meaning of social reputa-
tion.
The collaborative work processes. Most of the
popular software platforms for collaborative work
fail to give real, effective support for working col-
laboratively. First, the users are not given a virtual
workspace where direct communication and shar-
ing between colleagues can happen, so they must
resort to external programs to solve this (or in ex-
treme cases, physical meetings). Secondly, teach-
ers are not provided with the opportunity to man-
age, coordinate, assess, evaluate, share or com-
municate with the workgroups. SocialWire does
permit subgroups, i.e., smaller groups within an
existing group. The instructors are in charge of
deciding how many groups will be created, their
sizes and their membership policies, if any is due.
Every activity supported by SocialWire can be as-
signed to a group or to an individual, and in the
former case any group member is entitled to par-
ticipate in the role of groups representative. Ad-
ditionally, every subgroup is internally a group
and has a private space so that their members and
the instructors can communicate.
3 APPLICATION
As explained in the previous Section, SocialWire pro-
vides a social networking platform for interaction be-
tween teachers and learners. The platform has been
conceived as a complementary tool to a traditional
course offering, so it provides two of the different
learning modes typically found in standard MOOCs:
video lectures/talks, assessments (in form of quizzes,
homework and exams), and social networking. So-
cialWire supports the last two modes, while the lec-
tures are still held in real classrooms.
The SocialWire platform has been used to teach
one master level course over two consecutive aca-
demic years, 2014/15 and 2015/16. This is an ad-
vanced course within the scope of the underlying
technologies in computer networks, continuing and
intensifying the introductory concepts studied in the
subjects of the degree. In both editions 16 students
followed the course. All had studied at least a sub-
ject in the degree following a methodology similar to
the one described in this paper. As to the students’
background, 9 in the first edition and 7 in the second
held an undergraduate degree related to computer net-
works.
The course has a weekly schedule that lasts 14
weeks. The activities are organized as follows:
Lectures/recitations, that mix the exposition of
the ideas, concepts, techniques and algorithms be-
longing to the lessons of the course with the reso-
lution of problems and theoretical questions in the
classroom.
Laboratory sessions, in small study groups. These
are complementary sessions where the students
design and analyze different network scenarios
and with different protocols, using the GNS3 em-
ulator.
Online activities (questions, tasks, tests, etc.), in
the virtual classroom.
Students and teachers belong to a single group in So-
cialWire, wherein general communication about the
topics covered takes place.
To encourage networked learning activities and
collaborative work, the teachers planned different ac-
tivities in SocialWire whereby the students may gain
points (the resulting ranking is made public to the
group):
Collaborative answering of questions. This ac-
tivity consist in posing and solving any question,
doubt or problem about the subject. The students
send their questions, and so do the instructors oc-
casionally. From the questions posed by the stu-
dents, each question aligned to the course objec-
tives and not repeated receives some points. The
answers to any question (not absolutely correct,
since the effort to participate and try to answer is
also valuable) get also some points, depending on
their quality and completeness and the difficulty
of the underlying question. Correct answers are
clearly marked, so that there is no misunderstand-
ing.
Tasks previous to the laboratory sessions. By
means of this activity the teachers successfully en-
courage the students to prepare the material cov-
ered in the laboratory sessions in advance.
CSEDU 2017 - 9th International Conference on Computer Supported Education
190
Tests previous to the midterm exams.
Face-to-face interaction (in the classroom and in
the laboratory session) is still the bulk of the course,
for a total of 40 hours. But the social networking ac-
tivities occupy a significant fraction of the indepen-
dent study time (an average of 10 hours). More im-
portantly, there is actually a connection between the
more formal face-to-face learning activities and the
online tasks, in that many discussions and homework
problems start in the classroom but take place further
through the online platform, and are finished there.
Though this subject may be passed with a single
final examination covering all the material, students
are encouraged to follow the continuous assessment
path.
In the two academic years, the weight of the con-
tinuous assessment was 50%, and the remaining 50%
is awarded as the result of a final exam held on two
different dates (January and July, non-exclusive). The
50% in the continuous assessment is split into a 30%
from two midterm exams and a 10% of the final grade
comes out from the game points gathered by engag-
ing in the social activities commented above, to in-
crease the level of participation. While it is true that
one point in the final grade might seem a too scarce
pay off for the best student, we believe it is impor-
tant that the full score is easily achievable by a sig-
nificant fraction of the class. Thus, in order to con-
vert the point marks into a grade, if P
av
and P
med
are the average and median number of game points
per student and P
max
is the maximum, we compute
M = min{P
av
,P
med
,P
max
/2}. In the conversion scale,
M represents 0.5 grade points, and every student hav-
ing at least 2M game points gets the full 1 grade
possible with this part. In doing so, we try to pre-
serve the incentive-driveneffect whereby the average-
performing student is still engaged and the best stu-
dents attain fair pay offs.
3.1 Activity in the Questions and
Answers Game
In the first edition of the course along the term the stu-
dents submitted 43 questions and 40 answers to the
platform. The quality of the answers was remarkable,
all got some game points and 18 were highlighted
by the teachers. Moreover, the teachers submitted 1
question, answered successfully by 8 students. In the
second edition of the course the students submitted
35 questions and 36 answers worthy of game points,
from which 6 were highlighted by the teachers. In
this case, the teachers submitted 3 questions along
the term, answered successfully by 8, 12 and 14 stu-
dents, respectively. As we can see in Figures 1 and 2
the activity is more concentrated around the second
midterm date (at the end of November) and one week
before the final exam (January 20 and December 17,
respectively).
In our datasets we recorded all the events tak-
ing place within the game: users who post questions,
users who answer each question and the valuations
they received. With these data points, we build social
graphs where two nodes (i.e., students) are connected
by an edge if one has given an answer to a question
posted by the other (notice that these graphs are di-
rected, since it is important to know who made the
question and who is answering it).
In Figures 3 and 4 every node is a student identi-
fied by his/her position in the ranking of game points
(the node with label 0 represents the teachers). The
light green points correspond to students that accom-
plished the subject in January, the dark green is for
students who passed in July, and the grey points are
for students who dropped off the course or failed the
subject in the end. The color in the answers (edges)
serves to classify them on the basis of the points re-
ceived (black means 0 points, blue 1 point, red 2
points, pink 3 points, orange 4 points and yellow 5
points).
In the graph of the first edition of the course we
can see that 14 of the 16 students followed the contin-
uous assessment path and took part in this activity. All
of them finally passed the course, 11 in January and
3 in July (only one of them, node 6, with prior ex-
posure to computer networks). Moreover, of the two
students not engaged in continuous assessment (nei-
ther of them with a computer networks background)
only one finally succeeded in the course.
In this edition, among the most active students in
this game are those who reach the highest positions
in the ranking, a fact suggesting that they were com-
petent in solving the rest of the online activities pro-
posed along the academic year. In the graphs, nodes
6 and 8 correspond to students with medium or high
performance in the online activities, having average
grades in the midterms but who had to improve their
grade in the finals in order to pass.
In the second edition of the course, all the students
participated in this activity, and all but one were able
to pass, 12 in the first call and 3 in the second one
(again only one of them, node 13, with a computer
networks background). Node 16 is a student without
previous specialization in computer networking who,
despite outstanding at this game, did not complete the
remaining online activities, so ended up relegated to
the last position in the ranking.
Finally, in Figures 5 and 6 we can see that in the
first edition of the course the students in the lowest
Characterizing Social Interactions in Online Social Networks: The Case of University Students
191
10 november
17 november
24 november
1 december
8 december
15 december
22 december
29 december
5 january
12 january
teachers question
students questions and answers (1 point)
students questions and answers (2 points)
students answers (0 points) students questions and answers (3 points)
students questions and answers (4 points)
students questions and answers (5 points)
Figure 1: Activity in the questions and answers game in the first edition of the course.
28 september
5 october
12 october
19 october
26 october
2 november
9 november
16 november
23 november
30 november
7 december
14 december
teachers question
students questions and answers (1 point)
students questions and answers (2 points)
students answers (0 points) students questions and answers (3 points)
students questions and answers (4 points)
students questions and answers (5 points)
Figure 2: Activity in the questions and answers game in the second edition of the course.
positions of the ranking concentrate the activity in two
weeks (two days in some cases). This fact suggests a
non-steady study of the subject along the term. The
same pattern is observed in students 5, 11 and 13 in
the second edition. All of them passed the subject in
July.
4 SOCIAL NETWORK ANALYSIS
In this Section we apply SNA techniques and tools to
mine the data collected. As we explained in the previ-
ous Section, we model the social relationships taking
place in the questions and answers game as directed
simple graphs, and aim to explain the basic structural
properties of such graphs as consequences of the so-
cial interactions among its agents. Formally, a graph
(N, g) consists of a set of nodes N = {1, 2,.. . ,n} and
a square matrix g, the adjacency matrix, where g
ij
represents the relation between nodes i and j. The
neighborhood of a node i is the set of nodes that i is
linked to, N
i
(g) = { j : g
ij
= 1 and/org
ji
= 1}. The de-
gree of a node d
i
(g) is the number of links that involve
that node. For undirected graphs, d
i
(g) = #N
i
(g). In
directed graphs, the in-degree d
in
i
= #{ j : g
ji
= 1} and
the out-degree d
out
i
= #{ j : g
ij
= 1} count how many
edges finish (respectively, start) at that node.
4.1 Graph-level Measures
In social network analysis, the static or dynamic struc-
tural characteristics of the graph reveal key aspects of
the collective and individual behavior of the agents.
Let us briefly report some of the typical descriptive
measures of a graph (Newman, 2010), and their val-
ues in our dataset.
CSEDU 2017 - 9th International Conference on Computer Supported Education
192
Figure 3: Interactions in the questions and answers game in
the first edition of the course.
Figure 4: Interactions in the questions and answers game in
the second edition of the course.
4.1.1 Density
The density of a graph keeps track of the relative frac-
tion of edges that exist (compared to the maximum
n
2
of a complete simple graph with n nodes). It is
simply the ratio between the number of edges and
the total number of possible edges, with values rang-
ing from 0 (sparsest) to 1 (densest). Our dataset is
dynamic, i.e., the social graph starts empty and the
links are established as a result of the information ex-
changes between pairs of agents. In Table 1 we show
the graph density values of the two editions of the
course are moderate (and smaller in the second edi-
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Figure 5: Activity per student in the questions and answers
game in the first edition of the course.
0
2
4
6
8
10
12
14
16
18
0 2 4 6 8 10 12
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Figure 6: Activity per student in the questions and answers
game in the second edition of the course.
tion that in the first). This is due to the nature of the
links: only a part of the students provide answers to
each question.
4.1.2 Global Centrality
Global centrality is a graph-level measure that gives
an idea about the dependency of the graph on the ac-
tivity of a small group of nodes. Its normalized values
range from 0 (even distribution of activity) to 1 (the
most centralized graph). It is based on the underlying
node-level centrality measures.
Many different measures of centrality have been
developed, that capture different features of nodes’s
position in a graph, the following ones being some of
the most commonly used:
Degree centrality: measures how connected a
node is, computing the (normalized) count of
neighbors to a node.
Betweenness centrality: tries to capture the im-
portance of a node in terms of its role in con-
necting other nodes, computing the ratio between
the number of shortest paths that a node lies on
Characterizing Social Interactions in Online Social Networks: The Case of University Students
193
Table 1: Density.
Academic year 2014/15 Academic year 2015/16
Answers 0.2197 0.1916
Questions and Answers 0.4561 0.3375
Table 2: Global centrality.
Academic year 2014/15 Academic year 2015/16
Degree
In-degree 0.3431 0.1511
Out-degree
Answers 0.3431 0.3644
Questions and Answers 0.3901 0.3291
Eigenvector
Directed
Answers 0.6614 0.5602
Questions and Answers 0.8493 0.7847
Undirected
Answers 0.6079 0.4758
Questions and Answers 0.7895 0.4862
Table 3: Reciprocity.
Academic year 2014/15 Academic year 2015/16
0.2585 0.1304
Table 4: Transitivity.
Academic year 2014/15 Academic year 2015/16
Global 0.3278 0.3156
Average 0.3131 0.3415
Table 5: Number of cliques of different sizes.
# Academic year 2014/15 Academic year 2015/16
2 29 38
3 12 19
4 1 2
Table 6: Assortativity.
Academic year 2014/15 Academic year 2015/16
Degree 0.1121 0.4153
Nominal (computer networks background) 0.1594 0.1135
and the total number of possible shortest paths be-
tween two nodes.
Closeness centrality: measures how easily a node
can reach other nodes, computing the inverse of
the average length of the shortest paths to all the
other nodes in the graph.
Eigenvector centrality: a measure based on the
premise that a node’s importance is determined by
how importantor influential its neighbors are. The
scores arise from a reciprocal process in which the
centrality of each node is proportional to the sum
of the centralities of the nodes it is connected.
In our context, degree and eigenvector centralities
seem good indicators of the students’ activity. Nev-
ertheless, closeness and betweenness centralities are
inconsequential for our purposes, since in the under-
lying graph the exchange of information is always di-
rect, without relays or intermediate nodes, between
the source agent and the destination agent.
For the case of degree centrality, we consider sep-
arately the in-degree centrality (the number of an-
swers a student receives), and two measures of the
out-degree centrality: the number of answers given by
a student and the number of questions proposed and
answers given by a student. The results in Table 2
show that the out-degree centrality values are moder-
ate and similar in both datasets, but the in-degree cen-
trality is smaller in the last dataset, indicating a more
homogeneous distribution of the questions submitted
and the answers received by the participants.
For the eigenvector centrality, we have tested dif-
ferent configurations of the graph built up from the
datasets. In the first, we remove the edges correspond-
CSEDU 2017 - 9th International Conference on Computer Supported Education
194
ing to questions posted by the student, and revert the
direction of the edges which model the answers. So,
in this case, an edge from node a to node b means that
student b has answered a question raised by a. We
apply this edge reversal operation to measure the cen-
trality of the students who answer some question, not
those who make the questions, because the eigenvec-
tor centrality measure is sensitive to the in-degrees of
nodes. Further, to understand the effect of mutual in-
teraction, we also consider an undirected version of
the latter graph. In the second configuration, we in-
clude explicitly the questions posed by each student in
the graph, by adding a self-edge in such cases. Again,
both the directed and the undirected versions of this
graph have been used to analyze the datasets.
The results in Table 2 show larger values of the
eigenvector centrality (for the directed as well as for
the undirected graphs) when the self-edges are con-
sidered, which is reasonable. The normalized central-
ity values are noticeable and higher in the first edition,
a hint of stronger centralization in the network, mean-
ing that not all nodes act as sources of information in
the same way.
4.2 Collaboration Among Groups of
Students
The social networking component of SocialWire
opens the door to collaboration among groups of stu-
dents. Therefore, we focus now on the discovery
of structural properties in the graph that reveal some
form of collaboration. Specifically, we analyze the
coefficients of reciprocity, transitivity and assortativ-
ity (or homophyly).
4.2.1 Reciprocity
Reciprocity accounts for the number of mutual ex-
changes of information in the graph, happening in the
form of request-response pattern. In mutual collabo-
ration either part poses a question and receives at least
one answer from the other part. In other words, this
entails the existence of the edges (a,b) and (b,a) si-
multaneously.
In our setting, reciprocity can be used to assess
the degree of mutual collaboration or trust between
two given students who have discovered each other
either randomly or by a previous request-response ex-
change. Table 3 lists the average reciprocity in the
networks. The small values obtained suggest that in
the social environment mutual collaboration is rare.
This is not surprising, after all, since this is a not iter-
ative activity more effective in the formation of com-
munities (three or more students) than in encouraging
strong mutual relationships.
4.2.2 Transitivity
A broader form of collaboration is transitivity (the
fraction of closed loops with three nodes in the graph,
sometimes also called the clustering coefficient). We
were also interested in detecting whether transitivity
is significant in the student network. Thus, the stan-
dard transitivity coefficient has been computed for the
two datasets, both the global transitivity coefficient
and the average value of the local (individual) transi-
tivity coefficients of the nodes. The results obtained
are shown in Table 4, and confirm that transitivity is
noticeable. However, this is not entirely unexpected,
since the social network fosters direct relationships
between the participants. There is no benefit in ac-
quiring or propagating information through a third
party, and the data are consistent with this observa-
tion. Consequently, both average and global transitiv-
ity are quite high.
4.2.3 Cliques
A clique is a maximal completely connected subgraph
of a given graph. So, a clique represents a strongly
tied subcommunitywhere each member interacts with
any other member. 3-cliques are the transitivity rela-
tions discussed in the last paragraph. Given the na-
ture of our datasets, though 3-cliques are likely, larger
cliques seem less probable. Table 5 lists the number
of cliques in the graphs by their size.
4.2.4 Assortativity
The assortativity coefficient measures the level of ho-
mophyly of the graph, based on some labeling as-
signed to the nodes. It is positive if similar nodes tend
to connect to each other, and negative otherwise.
As we can see in Table 6 we have measured the
degree assortativity and the case of nominal assor-
tativity where each student is labeled according the
computer networks background. For the nominal as-
sortativity we have obtained low values, many of them
negative, suggesting randomness in the relationships.
For the degree assortativity, the high negative value of
the second edition of the course suggests relationships
between the less and the most active students, as it is
desirable.
Characterizing Social Interactions in Online Social Networks: The Case of University Students
195
35 40 45 50
35 40 45 50
points
neighbors average points
5.0 5.5 6.0 6.5 7.0 7.5 8.0
5.2 5.4 5.6 5.8 6.0
final grade
neighbors average final grade
Figure 7: Neighborhood composition vs. students’ performance in the first edition of the course (points -top- and final grades
-bottom-).
45 50 55
45 50 55
points
neighbors average points
4 5 6 7
3.5 4.0 4.5 5.0 5.5 6.0
final grade
neighbors average final grade
Figure 8: Neighborhood composition vs. students’ performance in the second edition of the course (points -top- and final
grades -bottom-).
4.3 Relationships between
Neighborhoods’ Composition and
Students’ Performance
Finally, we are interested in measuring to what ex-
tent the social peers (i.e., his/her neighborhood in the
social graph) influence the student’s performance at
the end of the course. A reasonable conjecture would
suggest that information exchange with other good
students improves the insights and the learning pace
gained by the followers, but this should be confirmed
by the data, especially after having checked that the
assortativity in the graph is low.
To that end, because the small sample sizes are not
suitable to obtain accurate enough correlation mea-
sures, we have represented the students’ performance
vs. the average performance of their neighborhood.
As we can see in Figures 7 and 8, there is no clear
evidence that a student’s performance has a signifi-
cant influence on that of their neighbors. This is partly
because the dataset is small, but the main reason is the
design of the assessment: the main part of the final
grade still comes from traditional evaluation activi-
ties, not from the online participation.
Finally, Figures 9 and 10 show the egonetworks
of some of the students of each edition that are rep-
resentative of different patterns of activity. We see
that in the first edition good students tend to show
denser egonetworks. Nevertheless, in the second edi-
tion, the egonetworks are always quite dense for the
reason that the relationships between the less and the
more active students are more likely.
In Figure 9, node 1 is the most active students in
the online activities and node 5 correspondsto the stu-
dent with higher final grade in the subject. Nodes
6 and 7 are in the middle of the ranking (both with
the same number of points): the first one is a student
with a computer networks background that passed in
July, whereas the second one is a student without
previous specialization in computer networking that
passed in January due to the fact that he obtained bet-
ter results in the middle and final exams. Node 11 is
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Figure 9: Egonetworks in the first edition of the course (nodes 1, 5, 6, 7, 11 and 14).
Characterizing Social Interactions in Online Social Networks: The Case of University Students
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Figure 10: Egonetworks in the second edition of the course (nodes 1, 6, 5, 13, 15 and 16).
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a good student with computer networks background
and medium performance in the online activities. Fi-
nally, node 14 represents the less active student in the
online activities of those that followed the continuous
assessment in this edition.
In Figure 10, node 1 is the student with more
points and higher final grade in the subject, and node
6 is the second high performing student. Nodes 5 and
13 are two of the student that concentrate the activity
in few days: the first one is a student without previous
specialization in computer networks, whereas the sec-
ond one is a student with a computer networks back-
ground. Both passed the subject in July. Node 15 is
a good student with computer networks background
and medium performance in the online activities. Fi-
nally, node 16 is the student who, despite outstanding
at this game, did not complete the remaining online
activities, so ended up relegated to the last position in
the ranking.
5 CONCLUSIONS
In this work, we studied the nature and strength of
associations between students using an online social
network embedded in a learning management system.
With datasets from two offerings of the same course,
we mined the sequences of questions and answers
posted by the students to identify 1) structural prop-
erties of the social graph; 2) patterns of collaboration
among groups of students; 3) factors influencing (or
not) the final achievements of students. Though the
dataset is small, we found that quality participation
in the online activities appears to be correlated with
the final outcome of the course, and that good stu-
dents tend to show denser egonetworks. These find-
ings can help instructors to early detect and classify
the students’ ability, contributing to a better under-
standing of the learning experience and possibly to an
enhanced design of the academic activities.
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