Personalisation of MOOCs
The State of the Art
Ayşe Saliha Sunar
, Nor Aniza Abdullah
, Su White
and Hugh C. Davis
Electronics and Computer Science, University of Southampton, University Road, Southampton, U.K.
System and Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia
Keywords: Personalisation, Adaptive Online Learning, Connectivism, MOOCs.
Abstract: Researchers in the field of educational technology are paying huge attention to the widespread adoption of
Massive Open Online Courses (MOOCs) in the study of learning online. MOOCs are discussed in many
angles including pedagogy, learning sustainability, and business model. However, there are very few
discussions around MOOCs personalisation. In this paper, it is aimed to examine and analyse the literature
on personalisation of MOOCs to identify the needs, the current states and efforts to personalise learning in
MOOCs. The findings denote that the pedagogical design of MOOCs is currently insufficient due to
massive and geographically dispersed learners with diverse educational backgrounds, learning requirements
and motivations. Many believe that personalisation could address this lacking in MOOCs. Among the most
popular services being proposed or implemented in the literature are personalised learning path,
personalised assessment and feedback, personalised forum thread and recommendation service for related
learning materials or learning tasks.
Massive Open Online Courses (MOOCs) is an
emerging area in technology-enhanced learning
(Jona and Naidu, 2014). Even the first MOOCs
course, Connectivism and Connective Knowledge
08 (CCK08), has attracted thousands of learners. It
should be noted here, this online course was not
announced as a “massive open online course”, the
term “massive open online course” was first
introduced in 2008 by Dave Cormier to describe
George Siemens and Stephen Downes’ CCK08
online course (McAuley et al., 2010). The first
MOOCs course was based on connectivism theory
that addresses issues about connecting people and
resources to construct knowledge. It emphasises the
importance of providing social platforms to learners
to support their interactions with the course content,
rather than just transmitting knowledge to them
(Siemens, 2005). This kind of MOOCs is later
known as cMOOCs.
In 2011, Sebastian Thrun designed a MOOCs
course on Artificial Intelligence at Stanford
University. Pedagogically, this MOOCs course was
different from the first MOOCs. It is more teacher-
centric in which learning goals and learning plans
were predefined for potential learners. This kind of
MOOCs is named as xMOOCs, and it is based on
the behaviourist learning theory (Daniel, 2012).
Even though MOOCs is relatively a new trend in
technology-enhanced learning, concerns on teaching
and learning with MOOCs are still the same with
those on online education (Hollands and Tirthali,
2014; Shaw, 2012), for instance, how can MOOCs
be pedagogically efficient to address different needs
of its learners? Research attempts to address this
issue are discussed further in Section 3. One
proposed study is to provide MOOCs
personalisation through educational data mining in
order to improve learning experience in MOOCs. In
this paper, the state of the art of personalisation in
MOOCs based on a study on the related literatures is
presented. The methodology is presented in Section
2. Analysis and findings are reported in Section 3 in
order to identify the aspects of MOOCs’s
personalisation that are commonly addressed by
researchers and those that are still not sufficiently
look into. The existing personalisation approaches
and report of the critical reviews on them are further
investigated in the sub sections of Section 3. Based
on the findings, suggestions on ways to improve the
delivery of personalised learning in MOOCs are
provided in Section 4. Section 5 concludes the study
Sunar A., Abdullah N., White S. and C. Davis H..
Personalisation of MOOCs - The State of the Art.
DOI: 10.5220/0005445200880097
In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 88-97
ISBN: 978-989-758-107-6
2015 SCITEPRESS (Science and Technology Publications, Lda.)
and presents suggestions for future work.
In order to review the literature, similar
methodology used by Liyanagunawardena et al.
(2013) and Yousef et al. (2014)'s researches is
applied. The articles between 2011 and 2014 (by
November, 30) are searched by the keywords
“MOOCs personalisation” and “adaptive MOOCs”
on several academic databases, Google Scholar, The
British Journal of Educational Technology,
American Journal of Distance Education, Journal of
Online Learning and Technology, ISI Web of
Knowledge and IEEEXplore. The reason of
choosing this particular time period is that 2011 is
the year in which both xMOOCs and cMOOCs have
been discussed (Daniel, 2012) and MOOCs has
become rapidly and widely used in online learning
as reported in (Liyanagunawardena et al., 2013). Not
only peer-reviewed articles were analysed in this
paper, but also the grey literature, for example
institutional reports were also searched and
Table 1: The result of the search by the keyword “MOOCs
Year Search result Relevant
Google Scholar
2011 17 0
2012 29 1
2013 313 11
2014 427 14
The British Journal of Educational Technology
2011 0 0
2012 0 0
2013 0 0
2014 1 0
American Journal of Distance Education
2011 0 0
2012 0 0
2013 0 0
2014 4 0
Journal of Online Learning and Teaching
2011 0 0
2012 0 0
2013 0 0
2014 0 0
ISI Web of Knowledge
2011 0 0
2012 0 0
2013 4 1*
2014 0 0
Table 1: The result of the search by the keyword “MOOCs
personalisation” (cont.).
2011 0 0
2012 0 0
2013 0 0
2014 0 0
Table 1 and 2 illustrate the number of papers that
have been retrieved, along with the number of
relevant papers to the personalisation of MOOCs
over the years based on the searched keywords
“MOOCs personalisation” and “adaptive MOOCs”,
respectively. While the year 2012 is called and
referred many times as “the year of the MOOC”,
personalisation of MOOCs has been on the rise since
2013 (The New York Times, November 2, 2012:
Table 2: The result of the search by the keyword “adaptive
Year Search result Relevant
Google Scholar
2011 19 0
2012 72 1*
2013 422 18***
2014 623 17***
The British Journal of Educational Technology
2011 0 0
2012 0 0
2013 3 0
2014 1 0
American Journal of Distance Education
2011 0 0
2012 0 0
2013 0 0
2014 2 0
Journal of Online Learning and Teaching
2011 0 0
2012 0 0
2013 0 0
2014 0 0
ISI Web of Knowledge
2011 0 0
2012 0 0
2013 3 1*
2014 4 0
2011 0 0
2012 0 0
2013 1 1*
2014 5 3**
* 1 same result with the other search.
** 2 results of them are the same with the other search.
*** 8 results of them are the same with the other search.
Figure 1 clearly illustrates that the amount of
attention for personalised learning in MOOCs is
drastically increased in the last two years.
Even though, the number of search results is over
600 papers (see Table 2), relevant papers are only a
few among them (40 papers in total). Papers on
studies regarding adaptive online education systems,
and other issues related to MOOCs are also retrieved
along with papers on mass personalisation in
MOOCs with these keywords. However, the relevant
papers only indicate studies that are based on mass
Figure 1: The total number of papers and relevant papers
by the searches for the keywords “MOOCs
personalisation” and “adaptive MOOCs”.
This study only considers the relevant papers for
analysis. The analysis is organised according to the
purposes and scoped of the studies, and the
personalisation or adaptation techniques used.
Once the redundant papers are eliminated from the
collection of relevant papers, it is observed that
some papers rhetorically indicate needs for
personalisation in MOOCs while some others
attempt to develop personalisation services in
MOOCs. Therefore, the relevant papers are clustered
into three categories in this study:
1. NEEDS: Represents the ‘Need for
personalisation in MOOCs’. This category of
research papers indicates the need or
opportunity for MOOCs personalisation.
They mainly report findings that lead to the
need for personalised learning in MOOCs.
However, the papers in this category do not
propose any project, framework or system for
designing or implementing personalisation in
2. PROPOSALS: Represents the ‘Plan to
implement personalisation in MOOCs’. This
category of research papers expresses ideas
and proposals for personalisation projects in
MOOCs. However, the plans for the intended
personalisation systems have not yet been
3. IMPLEMENTATIONS: Represents the
attempts for ‘Personalisation Service in
MOOCs’. This category of papers expresses
partly or fully implemented and experimented
proposals for personalisation in MOOCs.
However, majority of researches in this
category are in progression state with no
definitive outcome yet.
Figure 2 illustrates the number of papers in each
category over the years. The figure denotes that only
one paper emphasises the need for personalisation in
MOOCs in 2012 while 2013 is the year with the
highest number of papers (13) calling for
personalisation. In 2013, there are 5 descriptive
papers on proposals for personalisation in MOOCs
but only 3 papers proposed partly or fully
personalised MOOCs functions in MOOCs learning
environment. Generally, the number of papers in
categories of Proposals and Implementations
increases in 2014 after the call for adaptive MOOCs
in the previous year. The results show that there is a
rapid growing of interest towards personalised and
adaptive learning in MOOCs. Additionally, it is
predicted that there will be more implemented and
fully experimented studies in the coming years.
Figure 2: The number of papers in each category over the
3.1 Needs
Fasimpaur (2013), Freeman and Hancock (2013),
Godwin-Jones (2014), and Harman and Koohang
(2013) indicate that a huge amount of human data
can be collected through MOOCs. The availability
of the big data in MOOCs, and tools to perform
learning analytics would make it possible for a
personalised system to predict learners’ learning
behaviours and preferences in order to deliver
personalised learning and assistance to MOOCs
learners. Shaw also (2012) points out that this pool
of human data could be used to create a human
model in intelligent tutoring system (ITS) for
MOOCs. Similarly, Yates (2013) and Knox (2014)
highlight that data mining and data analytics for
prediction could make MOOCs adaptive. Slightly on
a different note, Kay et al. (2013) predict that
educational data mining and learning analytics
should be applied for MOOCs’s social network
analysis to enable personalised learning in MOOCs.
Kalz (2014) further supports the argument by
highlighting that these techniques could make
MOOCs a more suitable technology to support
lifelong learners.
The importance of offering personalised learning
in MOOCs is further expressed by the following
researchers. For instance, Amo (2013) believes that
MOOCs should offer student-centred learning for
effective and quality education in order to meet each
individual learner’s learning expectations in
MOOCs. However, she emphasises that current
pedagogy and design of MOOCs is not enough to
improve students’ outcomes. As there are many
exciting and available pedagogies in technology
enhanced learning such as peer assistance and
assessments, social networking, and gamification,
the author suggests for the incorporation of these
pedagogies into MOOCs. This can be accomplished
through the use of learning analytics and continuous
monitoring of students’ interactions so that
automated assessment with instant feedback can be
personalised to every student to improve quality
learning in MOOCs.
McLoughlin (2013) and Knox et al. (2014) also
address the current inefficiency of learners’
feedbacks in MOOCs. They point out that MOOCs
environment is convenient for offering personalised
contents and feedbacks to learners based on their
learning goals. This is because MOOCs provides
learning flexibility and sense of independence
between learners and teachers which are important
when implementing personalisation in technology-
enhanced learning.
Additionally, Kalz and Specht (2013) point out
that the current MOOCs design does not consider
the diversity of its learners. The authors suggest that
building sub groups that share similar attitudes and
interests could be a solution. The authors further
indicate that the heterogeneity problem in MOOCs
community is akin to the problem of learning
network. The authors describe learning network as a
connection of humans, actors, agents, institutions
and learning resources organised for a learning
program/course. To deal with diversity in learning
networks, several services for learner support in
learning networks should be utilised, such as
placement support service (navigation support), a
recommender service, and knowledge matchmaking
service. By using these intelligent personalisation
techniques, different needs and interests among
diverse learners community in MOOCs can be
addressed. To further support the importance of
addressing diversity among learners, Cavanaugh
(2013) whose work focuses on MOOCs assessments
for credits for the post secondary education, states
that personalised learning pathways for learners
could help them build their capabilities to obtain
Kizilcec et al. (2013) are concerned with low
completion rate in MOOCs. Therefore, they have
conducted a study to examine patterns of learners’
engagement and disengagement with the MOOCs
course, and consecutively they have suggested for
MOOCs to offer adaptive content or assistance to
learners according to their needs. Their suggestion
is further supported by Martin et al. (2013) who
believe that learning in MOOCs can be encouraged
by providing predefined personal path and super
badges that indicate the competence level of each
individual learner.
On the other hand, Aoki (2013) and Stine (2013)
focus on business model for MOOCs. While Stine
(2013) indicates mass personalisation can have a
positive business impact to MOOCs, Aoki (2013)
points out that MOOCs is representing a new
business model. Aoki (2013) states that content
providers for lectures, assessments/accreditation and
tutorial supports will eventually be separately
established and organised. The author presumes that
the learners’ data will be shared among separate
organisations to enable personalisation in MOOCs.
Despite the apparent needs for personalised
learning in MOOCs, Kay et al. (2013) point out that
the existing MOOCs courses are not even half way
through in implementing personalisation.
Nevertheless, without personalisation, learners may
reduce their participations and eventually drop out
from a MOOCs’s course, which is one of the biggest
concerns of MOOCs (Stevanović, 2014).
Noteworthy that even though, there is nonexistence
of personalisation practice on the existing MOOCs
platforms, Hollands and Tirthali (2014) point out
that MOOCs still present the term POOC
“Personalised Open Online Course” into their full
report. It is also stated that the success of MOOCs
will depend on how much the learning process is
3.2 Proposals
The literature that is considered under this category
mainly involves project launches which are funded
for the aim of personalising online education for
masses, projects’ proposals for implementing
personalisation in the existing non-personalised
MOOCs, and conceptual research frameworks.
Most of the research works are driven by
concerns over the inefficiency of MOOCs design,
delivery, and assessments. For instance, Daradoumis
et al. (2013) and Bassi et al. (2014) voice their
concerns in several different research papers.
According to the authors, as most of MOOCs
courses are not learner-centric, and they provide
same content for all learners, the effectiveness of the
tutoring is generally poor, feedbacks are insufficient
and peer-based evaluation is usually unprofessional.
To address these deficiencies, the authors propose an
agent-based framework for MOOCs. Agents collect
data and analyse them according to several
perspectives including educational goal, pedagogical
preferences, time management and so forth. The
analysed data is used by other agents for content
customisation, tutoring feedback, system-learner
alert as well as assessing and monitoring learners’
learning progress in MOOCs. The authors indicate
that intelligent agents could also be used for
reducing fraud and cheating during online tests.
Broun et al. (2014b) propose a personalisation
component which will be integrated to the existing
EMMA platform. EMMA platform is a MOOC
platform delivering courses in different languages
from different European Universities; therefore,
learners may be overwhelmed with huge number of
courses and language choices. Through this
personalisation component, EMMA aims to provide
personalised feedback and individualised learning
paths to support learners to achieve their learning
De Maio et al. (2014) believe that learners’
engagement with the video lecture materials in
MOOCs as passive. To improve learners’
engagement with MOOCs, the authors propose a
methodology to support learners to navigate the
fragments of one or more videos lectures so that
learners could connect their goals and prior
knowledge with the key concept of the lectures. The
authors use taxonomy building for constructing a
knowledge model for the concepts of lectures. The
main idea is to enable inter-linking between different
MOOCs courses and navigate learners to related
ones. However, this part of the research has not been
Similarly, Wilkowski et al. (2014) have
conducted an analysis on learners’ goals and their
achievements on the tested skills and activities by
executing “Mapping with Google” course in
MOOCs. Each learner was asked to complete a
questionnaire about their learning goals to join the
course and their previous experiences with the
Google map. The authors then compared learners
learning goals with their behaviours in the course
(i.e. watched videos, completed activities), and
found out that their behaviours were very much
determined by their goal. Therefore, the authors
conclude that the course delivery could be
personalised based on learners’ goals. Their
proposed system could be adapted to learner’s
requirements in two ways. First is to ask for
learners’ goals prior to delivering personalised
learning pathway to each of them. Secondly, to have
learners select the course elements such as some
video lectures and assessments from a list for a
customised course.
Fasihuddin et al. (2014) propose an approach for
personalised learning experience in MOOCs based
on learners’ learning styles. The authors define the
kind of material that should be included in the
lecture for a particular learning style. For example,
while visual learning objects should be accessible
for visual learners, such need is not a necessity for
verbal learners. However, this is an ongoing research
and a prototype is still not yet completed.
Elkherj and Freund (2014) have developed an
adaptive hint system for the undergraduate online
course “Introduction to probability and Statistics” on
the Webwork, which is a platform for managing
homework assignments in mathematics. This course
was attended by 176 students and hints were written
by the tutor each time learners made a mistake or
failed a test. The authors express that the need for
manual labour for analysing learners’ failure and
writing helpful hints makes the system inconvenient
for MOOCs. Therefore, they propose some possible
approaches that could address this problem. The first
is for students to hints to their peers. Secondly,
create hint libraries. Finally, use machine-learning
techniques to map students’ mistakes with hints and
consecutively send the most relevant hint to them.
Brouns et al. (2014a) propose ECO sMOOC for
the EU-funded project called Elearning,
Communication and Open-data: Massive Mobile,
Ubiquitous and Open Learning (ECO). sMOOC
refers to being a social-based MOOCs which is
accessible from different types of social media and
mobile devices. Learning is executed devices
through content contextualisation based on learners’
interactions and participations in the course using
mobile and gamification approaches. The ECO
sMOOC environment is described as learner-centric
approach, which is adaptable to learners’ intention.
However, the project is in the very early stage, and
any real experience with it has not yet available.
Bain et al. (2013) propose AMOOC (Accessible
Open Online Course) movement to make MOOC
courses more accessible for learners with
disabilities. The paper focuses on delivering course
content in appropriate forms for disable learners.
They also mention that the system will be conducted
using Adaptive Mobile Online Learning (AMOL)
for adapting coursework to each learner’s learning
Collet (2013) proposes POEM (Personalised
Open Education for the Masses) platform project for
designing personalised learning management system
(LMS) for massive learning. The author believes
that personalisation of massive education is only
possible with intelligent ICT (Information and
Computing Technology) platforms. In POEM, visual
and dynamic Knowledge Maps of domains for each
course are constructed to provide different possible
learning paths to learners. POEM will also provide
inter-tutorship and automatic assessments. Apart
from that, the system will ask learners to post new
questions or new contents to the platform.
Bansal (2013) and Birari (2014) have utilised the
concept of ITS for personalising learning
experiences with MOOCs from different
perspectives. Bansal (2013) focuses on providing
recommendations for learners to do additional
learning activities to improve their lack of
knowledge on a particular topic. In order to model
learners’ knowledge, the author uses the fuzzy
cognitive map. On the other hand, Birari (2014)
models learners’ cognitive state by Bayesian
network so that adaptive testing and adaptive
guidance can be delivered to learners.
Slightly on a different note, Blanco et al. (2013)
has identified three weaknesses in MOOCs: high
dropout rate, lack of cooperative activities among
learners, and poor continuity of learning
communities when a MOOCs course ends.
According to the authors’ definition, learning
community includes activities, resources, and similar
groups. To improve learning experiences in
MOOCs, the authors have outlined the components
of learning community that should be personalised
based on learners’ learning goals, previous
knowledge, etc. These personalisation inputs are
captured and diagnosed through initial assessments.
Similarly, Zhuhadar and Butterfield (2014) point
out that providing a singular curriculum to a diverse
MOOCs community has caused low completion
rates in MOOCs. To address this problem, the
authors propose Personalised Open Collaborative
Courses (POCCs) which tracks learners’ attitude
during the course and delivers the personalised
content based on learners’ activities and their prior-
knowledge. In order to achieve this goal, the authors
examine sub communities in MOOCs to design a
personalised social recommender system.
3.3 Implementations
Research works reported in this category provide a
more concrete evidence of approaches towards
implementing personalisation in MOOCs, such as
early stage experimental results, a system framework
or results of system performance tests. This category
considers either partly or fully implemented
personalised systems that may have performed some
kind of testing on either system performance or
student performance. Noteworthy that majority of
the systems have not yet completed their final
evaluations, and the projects are still ongoing.
An algorithm of an adaptive study planner for
MOOCs learners, targeted to novice learners in
MOOCs is presented by Alario-Hoyos et al. (2014)
and Gutiérrez-Rojas et al. (2014a). The adaptive
planner creates a personalised study schedule for
each learner based on their priority of the course,
available time slot and the course requirements.
However, this system has not yet been evaluated.
Burgos and Corbí (2014) present a rule-based
technology-enhanced learning recommendation
model in order to improve users’ performance in
MOOCs and other Open Educational Resources
(OERs). The model tracks learners’ performances
and their interactions with the lectures. It
consecutively map the related data according to the
tutor’s rules for recommendation such as minimum
number of required activity in a lecture and
minimum score on a given test. Based on the results
of rules mapping, a recommendation is made. If a
learner satisfies the tutor’s rule to be successful, then
the learner gets positive comment such as “Well
done!” and gets recommendation for the subsequent
tasks. Otherwise, the system gives alert feedback to
the learner to request support from the online tutor
and peers, and locks any further activities.
Ketamo (2014) utilises ITS technologies for
providing recommendations to support learners’
cognitive progress and motivation in MOOCs. The
content that will be provided to learners is defined as
semantic network. This approach requires a learner
to complete and succeed relevant test on a learning
concept prior to recommending the next related
learning concepts. According to the preliminary
evaluation results, learners’ performances were
improved when using the recommendation service.
However, a considerable portion of learners was still
not motivated to learn, and eventually dropped the
Shatnawi et al. (2014a, 2014b) propose system
architecture for providing personalised feedback to
learners in MOOCs by using text-mining technique.
Since the course creators are not able to provide
timely feedback due to massive number of learners,
the authors propose a method for providing
automatic content related feedback by using domain
ontology, machine learning, and natural language
processing. When a learner writes a post, the system
will determine its type, whether it is a question, a
comment, or a feedback, and organised it into a
suitable domain under the related topic in a
repository. If a learner posts a question, the system
will automatically search the repository and returns
semantically relevant information or personalised
feedback to the learner.
Sonwalker (2013) proposes an adaptive MOOC
that offers adapted learning contents based on
learning styles with the concern of pedagogical
effectiveness of MOOCs. The author proposes the
learning cube that illustrates organisation of learning
objects developed in text, graphics, audio, video,
animations, and simulations according to different
learning styles. In this study, learners’ learning style
is diagnosed via a diagnostic test as suggested by
Blanco et al. (2013). The performance test result is
Yang et al. (2014) propose a personalised
support on MOOCs discussion forums for helping
learners to reach the topics in which they are
interested. The authors use both collaborative and
content filtering techniques to capture the most
relevant forum threads. Their system performance
test results show that the system performance of the
proposed personalisation model is satisfactory,
however, learners’ satisfaction test has not yet
Some researchers modify existing personalised
technology-enhanced learning systems for MOOCs
courses. For example Miranda et al. (2013)’s work
aims to provide a pedagogy-based guide for items
assessment based on the ontological relations
between learning subjects in the lectures which are
defined by the course creator. According to a
learner’s assessment’s score, a personalised learning
pathway is constructed for the learner. Similarly,
Henning et al. (2014) also adapt an existing
technology-enhanced learning system into MOOCs.
The system supports learners through personalised
navigation based on their learning performances and
the association between learning subjects.
Result from the analysis of the needs related
literature shows that the pedagogical design of
MOOCs is insufficient, therefore, educational data
mining should be applied to provide personalised
services such as personalised learning pathways,
personalised assessments, adaptive feedbacks, and
recommender services. To address the needs for
personalisation in MOOCs, researches in category
Proposals and category Implementations have
proposed several outlines, frameworks, and projects’
proposals, as well as prototypes for implementing
personalisation and adaptation in MOOCs.
For instance, Kalz and Specht (2013) and
Kizilcec et al. (2013) from category Needs suggest
to cluster MOOCs’s learners for personalisation. The
suggestion was implemented by Blanco et al. (2013),
Fasihuddin (2014) and Sonwalker (2014) in which
they applied a diagnostic test at the beginning of the
course to understand which group (i.e. learning
style) a learner belongs to. However, this method is
based on learners’ participations in the diagnostic
test, and majority of learners are not interested in
doing tests. Realising this problem, Zhuhadar and
Butterfield (2014) have suggested using some social
networking analysis (SNA) techniques to diagnose
learners and automatically cluster them according to
the most suited sub community in MOOCs based on
their activities. Even though this method does not
need learner’s self-statement, a learner is required to
participate in the course’s lectures and activities
until the system can gather sufficient information
about the learner in order to determine a suitable
cluster for the learner.
Another example is by the work of Shaw (2012)
who believes that the application of ITS technique
can actualise mass personalisation in MOOCs. The
belief was translated by Bansal (2013), Bariri (2014)
and Ketamo (2014) who implemented ITS
techniques in MOOCs for personalising contents,
learning pathways, and providing recommendations.
Note that even though Yang et al. (2014) and
Brouns et al. (2014a) did consider the social feature
of MOOCs, for example they personalise online
forum threads to learners based on their forum
activities and peers connections, they did not build a
personalised learning network in MOOCs or social
network analysis for improving learning networks as
suggested by Kalz and Specht (2013) and Kay et al.
(2013). Therefore, continuity problem of learning
communities identified by Blanco et al. (2013)
remains unsolved.
In conclusion, this literature survey has
demonstrated that there is a growing trend of
researchers embarking in the possibility of
implementing personalisation and adaptation in
MOOCs in order to improve users’ engagements,
hence reduce MOOCs’ drop-out rate problem. The
trend is mainly motivated by the fact that MOOCs’s
learning has the potential to spark demands for
personalised learning due to its massive and
geographically dispersed learners with diverse
background. In addition to that, MOOCs
environment does provide the basic requirements for
personalised learning such as the availability of huge
learners’ data, flexible learning, and learner-teacher
independence. Our categorisation of the literature
identified three distinct types of papers. 1) These
concerned with the need or motivation for
personalisation in MOOCs. 2) Outlines of plans or
proposals for implementing personalisation in
MOOCS. 3) Accounts and evaluations of the
implementation of personalisation services in
MOOC. We found that data mining techniques are
often used to exploit huge learners’ data in MOOCs,
and majority of the studies are concerned on the
pedagogical design issues. Therefore, many
researchers have proposed solutions based on
personalisation and adaptation techniques such as
personalised learning pathways and personalised
feedback. However, there is not yet any tangible
research that focuses on building personalised
learning networks even though the need has been
identified by Kalz and Specht (2013), Kay et al.
(2013) and Blanco et al. (2013). It is expected that
this issue will gain more attention in the nearest
Alario-Hoyos, C., Leony, D., Estévez-Ayres, I., Pérez-
Sanagustín, M., Gutiérrez-Rojas, I., and Kloos, C. D.
(2014). Adaptive planner for facilitating the
management of tasks in MOOCs. In Proceedings of
the V Congreso Internacional sobre Calidad y
Accesibilidad de la Formación Virtual, CAFVIR 2014,
Antigua Guatemala, Guatemala, pp. 517-522.
Amo, D. (2013, November). MOOCs: experimental
approaches for quality in pedagogical and design
fundamentals. In Proceedings of the First
International Conference on Technological Ecosystem
for Enhancing Multiculturality, pp. 219-223, ACM.
Aoki, K. (2013) Paradoxes between Personalisation and
Massification: The Future of Education, Conference
Proceedings 2013. 3
Bain, K., Chan, B., and Bates, L. (2013). AMOOC:
Improving Access to MOOCs using Speech
Recognition. Retrieved from http://
Bansal, N. (2013). Adaptive recommendation system for
MOOC (Doctoral dissertation, Indian Institute of
Technology, Bombay).
Bassi, R., Daradoumis, T., Xhafa, F., Caballé, S., and
Sula, A. (2014). Software Agents in Large Scale Open
Elearning: A Critical Component for the Future of
Massive Online Courses (MOOCs). SINCOS 2014,
Salerno, Italy. September 10-12, 2014. In proceedings
of the Sixth IEEE International Conference on
Intelligent Networking and Collaborative Systems, pp.
184-188. IEEE Computer Society.
Birari, N. (2014). Intelligent Tutoring System using
Computerised Adaptive testing and interaction logs
for MOOCs (Doctoral dissertation, Indian Institute of
Technology, Bombay).
Blanco, Á. F., García-Peñalvo, F. J., and Sein-Echaluce,
M. (2013, November). A methodology proposal for
developing adaptive cMOOC. In Proceedings of the
First International Conference on Technological
Ecosystem for Enhancing Multiculturality, pp. 553-
558, ACM.
Brouns, F., Mota, J., Morgado, L., Jansen, D., Fano, S.,
Silva, A. and Teixeira, A. (2014a). A networked
learning framework for effective MOOC design: the
ECO project approach. In A. M. Teixeira, & A. Szücs
(Eds.), 8th EDEN Research Workshop. Challenges for
Research into Open & Distance Learning: Doing
Things Better: Doing Better Things, pp. 161-171.
Budapest, Hungary: EDEN. Oxford, United Kingdom.
Brouns, F., Tammets, K., and Padrón-Nápoles, C. L.
(2014b). How can the EMMA approach to learning
analytics improve employability?. Retrieved from
Burgos, D., and Corbí, A. (2014). A recommendation
model on personalised learning to improve the user’s
performance and interaction in MOOCs and OERs.
UNESCO Institute for Information Technologies in
Education. IITE 2014 International Conference. Oct
14th-15th, 2014. Moscow, Russia.
Cavanaugh, J. (2013). The Coming Personalization of
Postsecondary Education Competencies. CAEL 2013
Forum & News: Competency-Based Education, pp. 2-
Collet, P. (2013). POEM (Personalised Open Education
for the Masses). Retrieved from http://
Daniel, J. (2012). Making sense of MOOCs: Musings in a
maze of myth, paradox and possibility. Journal of
Interactive Media in Education, 3.
Daradoumis, T., Bassi, R., Xhafa, F., and Caballé, S.
(2013, October). A review on massive elearning
(MOOC) design, delivery and assessment. In P2P,
Parallel, Grid, Cloud and Internet Computing
(3PGCIC), pp. 208-213. IEEE.
De Maio, C., Loia, V., Mangione, G. R., and Orciuoli, F.
(2014). Automatic Generation of SKOS Taxonomies
for Generating Topic-Based User Interfaces in
MOOCs. In Open Learning and Teaching in
Educational Communities, pp. 398-403, Springer
International Publishing.
Elkherj, M. and Freund, Y. (2014, March). A system for
sending the right hint at the right time. In Proceedings
of the first ACM conference on Learning@ scale
conference: pp. 219-220. ACM.
Fasihuddin, H. A., Skinner, G. D., and Athauda, R. I.
(2014) Personalizing Open Learning Environments
through the adaptation to Learning Styles. ICITA
2014, 9
International Conference on Information
Technology and Applications, Sydney, Australia, July
2014. ISBN: 978-0-9803267-6-5.
Fasimpaur, K. (2013). Massive and Open. Learning and
Leading with Technology March/April 2013, pp. 12-
Freeman, M. A. R. K., and Hancock, P. H. I. L. (2013).
Milking MOOCs: Towards the right blend in
accounting education. Academic Leadership Series, 4,
Godwin-Jones, R. (2014). Emerging Technologies Global
Reach and Local Practice: the Promise of MOOCs.
Announcements & Call for Papers, 5.
Gutiérrez-Rojas, I., Alario-Hoyos, C., Pérez-Sanagustín,
M., Leony, D., and Delgado-Kloos, C. (2014a).
Scaffolding Self-learning in MOOCs. Proceedings of
the Second MOOC European Stakeholders Summit,
EMOOCs, 43-49.
Gutiérrez-Rojas, I., Leony, D., Alario-Hoyos, C., Pérez-
Sanagustín, M., and Delgado-Kloos, C. (2014b)
Towards an Outcome-based Discovery and Filtering of
MOOCs using moocrank.
Harman, K., and Koohang, A. (2013) MOOC 2050: A
Futuristic Tour. Issues in Information Systems, 14 (2),
pp. 346-352.
Henning, P. A., Heberle, F., Streicher, A., Zielinski, A.,
Swertz, C., Bock, J., and Zander, S. (2014).
Personalized Web Learning: Merging Open
Educational Resources into Adaptive Courses for
Higher Education. Personalization Approaches in
Learning Environments, 55.
Hollands, F. M. and Tirthali, D. (2014, May). MOOCs:
Expectations and reality. Full report. Center for
Benefit-Cost Studies of Education, Teachers College
Columbia University. Retrieved from http:// content/uploads/2014/05/
Jona, K., and Naidu, S. (2014). MOOCs: Emerging
Research. Distance Education, 35(2), pp.141-144.
Kalz, M. (2014) (in press). Lifelong Learning and its
support with new technologies. In Smelser, N. J. and
Baltes P. B. (Eds.). International Encyclopaedia of the
Social and Behavioral Sciences. Pergamon: Oxford.
Kalz, M., and Specht, M. (2013). If MOOCs are the
answer-did we ask the right questions. Implications for
the design of large-scale open online courses.
Maastricht School of Management in its series
Working Papers, 2013/25.
Kay, J., Reimann, P., Diebold, E., and Kummerfeld, B.
(2013). MOOCs: So Many Learners, So Much
Potential... IEEE Intelligent Systems, 28(3), pp. 70-77.
Ketamo, H. (2014, June). Learning Fingerprint: Adaptive
Tutoring for MOOCs. In World Conference on
Educational Multimedia, Hypermedia and
Telecommunications, Vol. 2014, No. 1, pp. 2458-
Kizilcec, R. F., Piech, C., and Schneider, E. (2013, April).
Deconstructing Disengagement: Analyzing Learner
Subpopulations In Massive Open Online Courses. In
Proceedings of the third international conference on
learning analytics and knowledge: pp. 170-179. ACM.
Knox, J. (2014). From MOOCs to Learning Analytics:
Scratching the surface of the 'visual'. eLearn,
2014(11), 3.
Knox, J., Ross, J., Sinclair, C., Macleod, H., and Bayne, S.
(2014). MOOC Feedback: Pleasing All the People?.
Invasion of the MOOCs, 98.
Liyanagunawardena, T. R., Adams, A. A., and Williams,
S. A. (2013) MOOCs: A systematic study of the
published literature 2008-2012. The International
Review of Research in Open and Distance Learning,
14(3), pp. 202-227.
Martín, S., Peire, J., and Castro, M. (2013) Proyecto
WePrendo. Retrieved from
McAuley, A., Stewart, B., Siemens, G., & Cormier, D.
(2010). The MOOC model for digital practice.
University of Prince Edward Island, 33.
McLoughlin, C. E. (2013, June). The pedagogy of
personalised learning: exemplars, MOOCS and related
learning theories. In World Conference on Educational
Multimedia, Hypermedia and Telecommunications,
Vol. 2013, No. 1, pp. 266-270.
Miranda, S., Mangione, G. R., Orciuoli, F., Gaeta, M., and
Loia, V. (2013, October). Automatic generation of
assessment objects and Remedial Works for MOOCs.
In Information Technology Based Higher Education
and Training (ITHET), pp. 1-8. IEEE.
Shatnawi, S., Gaber, M. M., and Cocea, M. (2014a). Text
stream mining for Massive Open Online Courses:
review and perspectives. Systems Science & Control
Engineering: An Open Access Journal, 2(1), pp. 664-
Shatnawi, S., Gaber, M. M., and Cocea, M. (2014b).
Automatic content related feedback for MOOCs based
on course domain ontology. In Intelligent Data
Engineering and Automated Learning–IDEAL 2014:
pp. 27-35. Springer International Publishing.
Shaw, C. (2012). Intelligent Tutors and Personalized
Education. Retrieved from
Siemens, G. (2005). Connectivism: A learning theory for
the digital age. International journal of instructional
technology and distance learning, 2(1), pp. 3-10.
Sonwalkar, N. (2013, September). The First Adaptive
MOOC: A Case Study on Pedagogy Framework and
Scalable Cloud Architecture—Part I. In MOOCs
Forum, Vol. 1, No. P, pp. 22-29.
Stevanović, N. (2014) Effects Of Motivation On
Performance Of Students In MOOC. SINTEZA 2014
Internet and Education. DOI:10.15308: pp. 418-422.
Stine, J. K. (2013, April). MOOCs and executive
education. In Presented at the Directors Conference.
Retrieved from
Wilkowski, J., Deutsch, A., and Russell, D. M. (2014,
March). Student skill and goal achievement in the
mapping with google MOOC. In Proceedings of the
first ACM conference on Learning@ scale conference,
pp. 3-10. ACM.
Yates, R. (2013). Educational Technologies to Support
New Directions in Teaching Practice. International
Journal of Information & Education Technology, 3(6).
Yang, D., Piergallini, M., Howley, I., and Rose, C. (2014).
Forum thread recommendation for massive open
online courses. In Proceedings of 7th International
Conference on Educational Data Mining.
Yousef, A. M. F., Chatti, M. A., Schroeder, U., Wosnitza,
M., and Jakobs, H. (2014): MOOCs—a review of the
state-of-the-art. Proceedings of the CSEDU 2014
conference, 3, pp. 9-20.
Zhuhadar, L., and Butterfield, J. (2014). Analyzing
Students Logs in Open Online Courses Using SNA
Techniques. The 20th Americas Conference on
Information Systems, AMCIS 2014, Savannah,
Georgia, USA, August 7-9, 2014.