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SHORT PAPER
EXPLORING CO-STUDIED MASSIVE OPEN ONLINE COURSE SUBJECTS VIA SOCIAL NETWORK ANALYSIS
Exploring Co-studied Massive Open Online
Course Subjects via Social Network Analysis
http://dx.doi.org/10.3991/ijet.v9i8.3581
K. Jordan
The Open University UK, Milton Keynes, United Kingdom
Abstract—Massive Open Online Courses (MOOCs) allow
students to study online courses without requiring previous
experience or qualifications. This offers students the free-
dom to study a wide variety of topics, freed from the curric-
ulum of a degree programme for example; however, it also
poses a challenge for students in terms of making connec-
tions between individual courses. This paper examines the
subjects which students at one MOOC platform (Coursera)
choose to study. It uses a social network analysis based
approach to create a network graph of co-studied subjects.
The resulting network demonstrates a good deal of overlap
between different disciplinary areas. Communities are iden-
tified within the graph and characterised. The results sug-
gests that MOOC students may not be seeking to replicate
degree-style courses in one specialist area, which may have
implications for the future moves toward ‘MOOCs for cred-
it’.
Index Terms—Curricula, Open education, Massive Open
Online Courses, MOOCs, Social network analysis.
I. INTRODUCTION
In the past two years, massive open online courses
(MOOCs) have entered the mainstream, attracting several
million students [1] and garnering intense media attention.
One of the key characteristics of massive open online
courses is the removal of entry pre-requisites to courses
[2], allowing students to formulate their own learning
pathways, free of the constraints of a modular degree
programme. This may be liberating but also potentially
problematic for students in order to determine how to fit
individual courses together into a coherent whole. Pro-
gress is being made on this issue, from bundling individu-
al courses together into ‘specializations’ at Coursera [3] to
moves to translate entire subject curricula into the MOOC
environment [4]. However, it is not necessarily safe to
assume that all MOOC students seek to replicate tradi-
tional degree courses in a single subject area through their
engagement with MOOCs.
This study seeks to explore the patterns in enrolment of
MOOC students on different courses, through social net-
work analysis of courses which Coursera students with
public profiles are enrolled upon. The key question is
when the entry pre-requisites for courses are removed, do
MOOC students stick to courses within a subject area or
develop new inter-disciplinary subject areas with their
studies?
II. METHODS
In order to explore which MOOC courses are studied
together, a social network analysis approach was taken.
Social network analysis conceptualizes individuals as
nodes, which will be connected by edges if a relationship
exists between two nodes [5, 6]. In applying this method-
ology to the question of co-studied MOOC subjects, dif-
ferent courses would be represented as nodes in a one-
mode network; an edge is then present between two nodes
if one student has enrolled on both courses. This is similar
to the approach taken in recommender systems based on
purchasing information (for example, book purchases via
Amazon [7]). An example of how this would be applied to
co-studied courses for three hypothetical students is
shown in Figure 1. In scaling up from this to the whole
sample, additional courses would be added and edges
weighted to reflect the number of students who had en-
rolled on pairs of courses.
Figure 1. An example of translating the subjects co-studied by three
hypothetical students into a social network graph representation.
Data was collected from public Coursera profiles,
which list the courses a student had enrolled in. Note that
profiles are not public by default; a student must actively
opt to make their profile public. As there is no facility
within the Coursera website to search for students’ pro-
files, the sample was identified by internet search. Public
profiles were found by searching Google for part of the
URL used by profiles, restricted to the Coursera site, using
the following search query: "user/i" site:coursera.org .
This yielded a total of 287 public profile pages as results.
Using public profiles was necessary as it is the only way
at present to find this type of data, although it does bring
limitations with it. Only a very small proportion of
Coursera users appear to have public profiles; at the time
of data collection (2nd August 2013), the Coursera web-
site stated a total of 4,262,759 students were registered
with the site; 287 public profiles represents a small minor-
ity. Students who chose to make their profiles public are
not necessarily representative of the whole student body,
as their reasons for opting to be public are unknown, and
might be self-selecting more active users. Having enrolled
on a course is not indicative of whether students actively
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SHORT PAPER
EXPLORING CO-STUDIED MASSIVE OPEN ONLINE COURSE SUBJECTS VIA SOCIAL NETWORK ANALYSIS
engaged with the course materials, although enrolled stu-
dent numbers is a good predictor of active users (with
50% of enrolled students typically becoming active users)
[1].
Since the Coursera Terms of Service prohibit use of
web scrapers [8], information about the number of courses
and topics a student is enrolled in were collected manually
and entered into a spreadsheet. Data was collected on 1st
August 2013. In instances where students were enrolled in
multiple iterations of the same course, this was only
counted as one course. Of the total 287 profiles, three
were excluded from further analysis as they belonged to
Coursera staff. Distribution of the number of courses
studied by the remaining 284 students is shown in Figure
2.
Given the distribution shown in Figure 1, students en-
rolled in more than 30 courses were excluded from further
analysis. Students who were enrolled in zero or a single
course were also excluded, as this is insufficient to be able
to create an edge in the network. As a result, 201 student
profiles were included in the final sample for constructing
the network graph. The lists of courses each student is
enrolled upon were then rearranged to make pairs of co-
studied courses; an undirected link between courses indi-
cating that one person signed up to both courses. Dupli-
cates were allowed in order for a weighed graph to be
produced. The spreadsheet was imported into Gephi [9] in
Figure 2. Histogram showing distribution of number of different
Coursera courses students are enrolled in.
order to visualise and explore the resulting network, which
comprised 301 courses (nodes), and 8175 edges. The
modularity algorithm was used in order to detect commu-
nities [10]. Categorical data relating to each course was
also added in terms of traditional subject classification, in
order to examine the extent to which emergent communi-
ties follow these classifications. Subject areas used were
as defined by the Coursera course list. Where a course fell
into multiple areas, a judgment was made as to the prima-
ry focus; those which fell into more than four areas were
classified as such.
Figure 3. Network graph of co-enrolled Coursera subjects, colour-coded according to community. Courses belonging to ‘Community 0’ as shown in
red; ‘Community 1’ in yellow; ‘Community 2’ in green; Community 3’ in blue; and Community 4’ in purple.
iJET ‒ Volume 9, Issue 8: "Learning in Networks", 2014
39
SHORT PAPER
EXPLORING CO-STUDIED MASSIVE OPEN ONLINE COURSE SUBJECTS VIA SOCIAL NETWORK ANALYSIS
III. RESULTS
A. Whole network and community structure
The network graph of co-enrolled subjects is shown in
Figure 3. There is a great deal of inter-connection in the
graph; distinct communities are not obviously present.
The community detection algorithm identified five com-
munities, and nodes and edges are colour-coded according
to the categories they were assigned to by the community
detection algorithm. Note that an interactive version of
Figure 3 (created using the SigmaExporter plugin for
Gephi [11]) can be found online at
http://www.katyjordan.com/MOOCnetwork/ . In order to
characterize the disciplinary make-up of the five commu-
nities identified within the network, the frequency of
courses in different subject areas in each community are
shown in Figure 4.
Figure 4. Bar charts showing the number of courses from each subject
area represented in each community of the network. Numbers and
colour-codes correspond to the communities illustrated in Figure 3.
Two of the communities (communities 0 and 1) are
dominated by Computer Science courses, but differ in
terms of the subjects these courses are co-studied with.
Community 1 represents a more exclusively Computer
Science subject community, while Community 0 is more
interdisciplinary, allying Computer Science with other
subjects, principally Economics and Finance, Statistics
and Data Analysis, and Information Technology and De-
sign. In contrast, Communities 3 and 4 are more strongly
represented by the Humanities. In Community 3, the Hu-
manities are allied with Social Sciences and Arts subjects,
while Community 4 combines Humanities with Business-
oriented subjects. Community 2 is the most interdiscipli-
nary community, with a wide range of subject areas across
the Natural and Physical Sciences represented and no
single dominant area emerging. Although this gives an
impression of the general focus of each community, it is
also important to note that a wide range of subjects are
present in every community to an extent.
B. Position of individual courses in the network
Basic social network analysis metrics were also used to
examine which individual courses occupy notable posi-
tions within the network structure. The metrics used in-
cluded weighted degree (which reflects the number of
times a particular course has been studied within the sam-
ple) and betweenness centrality (a measure which reflects
“the extent to which an individual node plays a ‘broker-
ing’ or ‘bridging’ role in a network [12, p.75]). The ten
courses with the greatest weighted degree are shown in
Table I, and those with the greatest betweenness centrality
shown in Table II.
There may be a relationship between weighted degree
and time, as the majority of courses in Table I first ran in
2011 or 2012, so were relatively early established courses.
It is logical that the earliest courses would have a higher
weighted degree, being active for a longer period of time
including a period when there were fewer courses to
choose from. The courses demonstrating the greatest be-
tweenness centrality (Table II), however, are notable for
including subjects which span disciplinary areas (for ex-
ample, Social Psychology and Startup Engineering) or are
transferable to a range of different settings (for example,
Think Again: How to Reason and Argue, and several data
analysis courses).
IV. DISCUSSION
In applying social network analysis to the MOOC
courses which are co-studied by students with public pro-
files at Coursera, this study has identified communities of
subjects which tend to be chosen together by students. In
contrast to formal education, MOOC students are not
restricted in their choice of courses according to a particu-
lar subjects’ syllabus. This is reflected in the network
graph, which shows a good deal of overlap between the
courses, and a broad range of subjects being present in all
of the communities identified, to an extent. This interdis-
ciplinarity character of the communities may be consid-
ered an example of how openness can lead to unusual
behavior, in contrast to the disciplinary organization of
formal Higher Education. This may pose a challenge for
moves towards gaining credit for MOOCs and students
who do not restrict their studies to a particular discipline.
Whether this matters or not in terms of what students seek
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SHORT PAPER
EXPLORING CO-STUDIED MASSIVE OPEN ONLINE COURSE SUBJECTS VIA SOCIAL NETWORK ANALYSIS
TABLE I.
COURSES WITH GREATEST WEIGHTED DEGREE VALUES IN THE NETWORK
Course Institution
Date course
first began
Weighted
degree
Machine Learning Stanford University 10/2011 618
Introduction to Data
Science
University of Wash-
ington
01/05/2013 593
Computing for Data
Analysis
Johns Hopkins Univer-
sity 24/09/2012 478
Startup Engineering Stanford University 17/06/2013 431
Data Analysis Johns Hopkins Univer-
sity
22/01/2013 399
Gamification
University of Pennsyl-
vania
27/08/2012 354
Functional Pro-
gramming Principles
in Scala
École Polytechnique
Fédérale de Lausanne 18/09/2012 349
An Introduction to
Interactive Pro-
gramming in Python
Rice University 15/10/2012 338
Algorithms Part I Princeton University 12/08/2012 326
Algorithms: Design
and Analysis Part 1
Stanford University 12/03/2012 319
TABLE II.
COURSES WITH GREATEST BETWEENNESS CENRTALITY VALUES IN THE
NETWORK
Course Institution
Date course
first began
Betweenness
centrality
Gamification University of Penn-
sylvania 27/08/2012 1993.6
Machine Learning
Stanford University
10/2011
1389.0
Think Again: How to
Reason and Argue
Duke University 26/11/2012 1319.5
Introduction to Data
Science
University of Wash-
ington 01/05/2013 1214.8
Startup Engineering
Stanford University
17/06/2013
1149.9
Computing for Data
Analysis
Johns Hopkins
University 24/09/2012 913.8
An Introduction to
Interactive Pro-
gramming in Python
Rice University 15/10/2012 908.52
Data Analysis Johns Hopkins
University 22/01/2013 859.5
Social Psychology
Wesleyan Universi-
ty
12/08/2013 808.0
Model Thinking University of Mich-
igan
20/02/2012 685.5
to gain from participating in MOOCs is a subject for fur-
ther research, and the implications in turn for formal cur-
riculum design is an open question.
A central subject area emerged within each community,
although this varied according to how broad it is in scope;
for example, Computer Science dominated in Community
1 (and Community 0, allied with other subjects), while
Community 2 represented the whole range of Natural and
Physical Sciences. It is not clear whether this represents a
shift in disciplinary boundaries, students’ priorities, or
reflects the types of subjects which lend themselves best
to learning in a MOOC context. A social network ap-
proach such as this could provide the basis for a recom-
mender system in order to assist students in finding their
learning pathway within new emerging interdisciplinary
areas. The relationship between the extent of interdiscipli-
narity in a students’ course choices and their likelihood of
completion may be an interesting contribution to the hotly
debated topic of MOOC completion rates.
This study has provided an insight into the emerging
communities of subjects between individual MOOC
courses, which had previously been unexplored. It is also
restricted to a single MOOC platform; the ways in which
students study across multiple MOOC platforms would
also be an interesting area for future research. The results
here only provide a snapshot of the emerging disciplinary
communities; in practice, the network of subjects is dy-
namic. As the courses with the highest degree (reflecting
those which the greatest number of students in sample
signed up to) are frequently the earliest established cours-
es at present, there is currently a skew toward these cours-
es, which may be responsible for the dominance of Com-
puter Science at present. As the number of courses availa-
ble continues to proliferate and the number of MOOC
students increases, it will be interesting to see how com-
munities evolve over time; a wider range of communities
is likely to emerge, but it remains to be seen whether these
will be interdisciplinary or return to traditional discipli-
nary areas.
REFERENCES
[1] K. Jordan, “Initial trends in enrolment and completion of Massive
Open Online Courses,” The International Review of Research in
Open and Distance Learning, 15(1), 133-160, 2014.
[2] J. Daniel, “Making sense of MOOCs: Musings in a maze of myth,
paradox and possibility,” Journal of Interactive Media in Educa-
tion, 2012.
[3] S. Kolowich, “Coursera will offer certificates for sequences of
MOOCs”, The Chronicle of Higher Education, 2014,
http://chronicle.com/blogs/wiredcampus/coursera-will-offer-certifi
cates-for-sequences-of-moocs/49581, retrieved 22nd January 2014.
[4] P. Hill, “Two MOOC curriculum announcements in one week”, e-
Literate Blog, 2013, http://mfeldstein.com/two-mooc-curriculum-
announcements-in-one-week/, retrieved 22nd January 2014.
[5] S. Wasserman and K. Faust, Social network analysis: Methods
and applications. Cambridge: Cambridge University Press, 1994.
http://dx.doi.org/10.1017/CBO9780511815478
[6] C. Kadushin, Understanding social networks: Theories, concepts,
and findings. Oxford: Oxford University Press, 2012.
[7] J. Leskovec, L. Adamic and B.A. Huberman, “The dynamics of
viral marketing”, ACM Transactions on the Web, 1(1), 1-39, 2007.
http://dx.doi.org/10.1145/1232722.1232727
[8] Coursera website, “Terms of use”, 2014. https://www.coursera.
org/about/terms, retrieved 22nd January 2014.
[9] M. Bastian, S. Heymann and M. Jacomy, “Gephi: An open source
software for exploring and manipulating networks”, International
AAAI Conference on Weblogs and Social Media, 2009.
[10] V.D. Blondel, J-L. Guillaume, R. Lambiotte, and E. Lefebvre,
“Fast unfolding of communities in large networks”, Journal of
Statistical Mechanics: Theory and Experiment, 2008, P1000,
2008.
[11] S.A. Hale, “Build your own interactive network”, Interactive
Visualizations blog, 2012, http://blogs.oii.ox.ac.uk/vis/?p=191, re-
trieved 29th January 2014.
[12] R. Ackland, Web Social Science: Concepts, data and tools for
Social Scientists in the digital age. London: SAGE, 2013.
AUTHORS
Katy Jordan is a doctoral student with the Institute of
Educational Technology at the Open University UK (e-
mail: katy.jordan@open.ac.uk).
Submitted, February, 24, 2014. Published as resubmitted by the authors
on May, 26, 2014.
iJET ‒ Volume 9, Issue 8: "Learning in Networks", 2014
41
