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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.

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Identifying students’ profiles for MOOCs – a social media analysis

  • 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.