This document analyzes survey responses from 632 students in Romania about their participation in Massive Open Online Courses (MOOCs). The author uses complex network analysis to model the students as a graph based on their shared traits and reasons for engaging with MOOCs. Six distinct profiles of students who participate in MOOCs are identified from the graph's community structure. The profiles differ based on gender and traits like certification goals, recognition priorities, and motivation types. The analysis provides insight into how students relate to online education and how MOOC design could be optimized for different profiles.

1. In the digital age of the Internet,
the abilities of people to share
information, collaborate with
others, or work from a distance
have created a synergy that is
shaping educational systems as
well. Massive Online Open
Courses (MOOCs) are one of the
trending game changers of
formal, institutionalized
education, and students are
joining the trend with increasing
excitement. Currently,
engineering is working together
with academia to increase the
number of available open
educational resources and
broaden the coverage of MOOCs
worldwide. Yet, we make a step
further and combine complex
network analysis and sociology to
model and analyze the emerging
profiles of the new digital student.
As such, we have used an online
questionnaire to gather detailed
opinion from 632 students from
Romania regarding the
advantages, disadvantages and
reasons to choose MOOCs.
Based on their expressed
opinion, we create two graph
models of compatibility based on
key individual traits, and find six
distinct student profiles in terms
of engagement in MOOCs, and
seven profiles for non-
participants. Furthermore, we
discuss these profiles and explain
the implications, limitations and
perspectives of this study. We
consider our findings an
important milestone both in
understanding the needs of future
modern students, and in
optimizing the way MOOCs are
developed to serve the
challenges in education.
Identifying students’ profiles for MOOCs – a social media analysis
Alexandru Topirceanu1
; Gabriela Grosseck2
1
Politehnica University Timisoara, Romania, 2
West University of Timisoara, Romania
Out of the plethora of answers, we select the
criteria used as input for building the graph. The
input parameters are used for
classification/clustering, leaving all other answers
as output/descriptive parameters. Input has 7
parameters based on course elements such as
participation, costs, finalization, certification,
knowledge, gender. Output consists of 10
advantages and 10 disadvantages of MOOCs, plus
basic information.
In this section we detect, analyze and describe the
student profiles, as they emerge as communities.
To that end, we analyze the graph centrality
distributions, community structure, and
visualizations obtained by applying complex
network analysis on the MOOC participation
networks. In Figure 2 we showcase the relevant
communities.
We have obtained valuable perspectives on
how students relate to online education, and
rely on MOOCs and open educational
resources. The data analysis results define 6
profiles (network communities) for students
which have participated in MOOCs.
Thus, we found that only 2/3 of students
finished the course, and less than 1/3 obtained
a certificate. Almost half of the students
participated in an online course that was not a
MOOC, and the six obtained profiles are
mainly representative for bachelor students
(87% of respondents). We determined one
profile representative for averagely interested
students who are willing to try online education
and get certified (Proactive profile); this
suggests a relative openness to this type of
education, yet the results recommend that
existing courses should try to focus a bit more
on developing skills needed in daily life, rather
than professional ones. The other five profiles
are differentiated by gender, and are more
specific.
Differentiating from the average male student
pattern, we find two profiles: the first (Dreamer
profile) is characterized by disinterest in the
lack of academic recognition and
trustworthiness, and we consider these
students to be more inclined towards abstract
studies, use of gamification, individuality, and
to be driven by instrinsic motivation. The
second (Strategist profile) features the exact
opposite type of students: who care about
recognition, lack of formal requirements, and
are discouraged by automated verification. We
consider these students to be more inclined
towards debates, social activity, group projects,
and to be driven more by extrinsic motivation.
Differentiating from the average female
student, we find three profiles: the first (Realist
profile) is concerned about the higher drop-out
rate and lack of academic recognition, without
finalizing their courses; we consider these
students to maintain a realist view of their
education, yet they are less inclined towards
achieving their inner goals, and may be
motivated by informal learning habits, uses of
gamification, group activity, and are mostly
motived extrinsically. The second profile
(Novice) is similar to the Dreamer profile, in the
sense that they are not discouraged by the lack
of formal recognition, yet they are more
reluctant to achieve personal goals. The third
profile (Achiever) consists of females who want
to be certified, and their progress recognized
by academia; these students are empowered
by social activity and recognition, and must be
given clear requirements and tasks.
The survey for gathering our dataset is built
using Google Forms and consists of 69
questions from which we extract the following
noteworthy data:
•Demographics: Gender, age, university,
faculty, specialization, study year,
•Participation in past MOOCs: duration,
language, finalization and certificate
attainment,
•(optional) Reasons for not participating in
MOOCs,
•Advantages and disadvantages of the
MOOCs,
•Interests in a future MOOC.
After collecting the data, we create a
compatibility graph of students, similar to the
state-of-the-art methodology. What differs in
the current approach is that the bipartite graph
we start from is based not on social
collaboration or physical resemblance, but on
common educational and individual aspects of
each student. The reason for creating such an
innovative graph is that individual personality
patterns are more relevant than physical or
social personal features in the context of
academic participation.
We consider the student nodes N in our graph
G = {N, E}, and place the links E based on
compatibility. Particularly, compatibility is
defined as the number of common individual
traits two students share in common. The more
traits two nodes have in common, the greater
the weight of the link between them. This
methodology is depicted in Figure 1.
As a novel analytical approach we rely on
network science to go beyond the perspective
of a classical statistical framework. More
specifically, the methodological novelty for this
study consists of clustering students based on
their expressed reasons to participate or avoid
online courses, by modeling students in a
complex network where edges between them
are formed by overlapping compatibility.
Literature presents numerous examples of
successful network modeling for other social
media data.
The goal of this study is to offer insight over
student’s knowledge and participation in
MOOCs, and use a dataset of survey
responses to define profiles for students
engaging in online educational activities. Using
our defined profiles we consider that a more
personalized educational experience may be
automatically offered to each individual student
using an online learning framework. Our
proposed survey extracts valuable information
regarding the advantages, disadvantages of
participation in MOOCs, as well as
expectations and reasons for not participating,
all seen from the perspective of students.
INTRODUCTION
METHODOLOGY
Topirceanu, A., & Udrescu, M. (2015, September). FMNet: Physical Trait Patterns in the
Fashion World. In Network Intelligence Conference (ENIC), 2015 Second European (pp.
25-32). IEEE.
Gallos, Lazaros K and Potiguar, Fabricio Q and Andrade Jr, José S and Makse, Hernan A,
"Imdb network revisited: unveiling fractal and modular properties from a typical small-world
network", PloS one (2013), e66443.
Suciu, L., Cristescu, C., Topîrceanu, A., Udrescu, L., Udrescu, M., Buda, V., & Tomescu, M. C.
(2015). Evaluation of patients diagnosed with essential arterial hypertension through
network analysis. Irish Journal of Medical Science (1971-), 1-9.
DISCUSSION & CONCLUSIONS
RESULTS
REFERENCES
ABSTRACT
Gabriela Grosseck
West University of Timisoara
Email: gabriela.grosseck@e-uvt.ro
Phone: +40-256-592320
Website: novamooc.uvt.ro
CONTACT
(a)
5 out of 7 filter
93.52 average
degree
7716 edges
3 communities
(b)
6 out of 7 filter
15.0 average
degree
1238 edges
6 communities
(c)
7 out of 7 filter
1.98 average
degree
164 edges
83 communities
Figure 1. The empirical edge filtering process: low filtering (≤ 5 common
traits) leaves too many edges in the graph, while high filtering (≥ 7
common traits) leaves too few edges. The result is too few, respectively
too many communities. The optimal edge filtering threshold is 6 out of 7,
with a clear community structure, that is representative for further analysis.
Figure 2. Detailed view of G1, consisting of students which
participated in MOOCs. The large panel shows the six forming
communities of students (profiles) and the smaller panels show
nodes colored by different binary metrics regarding the online
course. Green nodes are students who positively answered
questions, and red nodes represent negative answers.