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Learning Analytics and MOOCs


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There are new discoveries in the field of educational technologies in the 21st century, which we can also call the age of technology. Learning Analytics (LA) has given itself an important research field in the area of Technology Enhanced Learning. It offers analysis, benchmarking, review and development techniques for example in online learning platforms such as those who host Massive Open Online Course (MOOC). MOOCs are online courses addressing a large learning community. Among these participants, large data is obtained from the group with age, gender, psychology, community and educational level differences. These data are gold mines for Learning Analytics. This paper examines the methods, benefits and challenges of applying Learning Analytics in MOOCs based on a literature review. The methods that can be applied with the literature review and the application of the methods are explained. Challenges and benefits and the place of learning analytics in MOOCs are explained. The useful methods of Learning Analytics in MOOCs are described in this study. With the literature review, it indicates: Data mining, statistics and mathematics, Text Mining, Semantics-Linguistics Analysis, visualization, Social network analysis and Gamification areas are implementing Learning Analytics in MOOCs allied with benefits and challenges.
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Draft originally published in: İnan E., Ebner M. (2020) Learning Analytics and MOOCs. In: Zaphiris P.,
Ioannou A. (eds) Learning and Collaboration Technologies. Designing, Developing and Deploying Learn-
ing Experiences. HCII 2020. Lecture Notes in Computer Science, vol 12205. Springer, Cham. pp. 241-254
Learning Analytics and MOOCs
Ebru İnan 1 and Martin Ebner 2
1 Educational Technology, Graz University of Technology, Graz, Austria;
2 Educational Technology, Graz University of Technology, Graz, Austria:
Abstract. There are new discoveries in the field of educational technologies in
the 21st century, which we can also call the age of technology. Learning Analyt-
ics (LA) has given itself an important research field in the area of Technology
Enhanced Learning. It offers analysis, benchmarking, review and development
techniques for example in online learning platforms such as those who host Mas-
sive Open Online Course (MOOC). MOOCs are online courses addressing a
large learning community. Among these participants, large data is obtained from
the group with age, gender, psychology, community and educational level differ-
ences. These data are gold mines for Learning Analytics. This paper examines
the methods, benefits and challenges of applying Learning Analytics in MOOCs
based on a literature review. The methods that can be applied with the literature
review and the application of the methods are explained. Challenges and benefits
and the place of learning analytics in MOOCs are explained. The useful methods
of Learning Analytics in MOOCs are described in this study. With the literature
review, it indicates: Data mining, statistics and mathematics, Text Mining, Se-
mantics-Linguistics Analysis, visualization, Social network analysis and Gami-
fication areas are implementing Learning Analytics in MOOCs allied with bene-
fits and challenges.
Keywords: Educational Technology; Learning Analytics; MOOCs.
1 Introduction
Learning Analytics is a research discipline that is rapidly developing, aims to analyze
and optimize learning and learning environments [1]. E-Learning Platforms such as
Learning Management Systems (e.g. Moodle) or MOOC-platforms (e.g. edx, Udacity,
Coursera etc.) have become frequently preferred tools by educators in high capacity
classes. These platforms, which are used with the purpose of supporting learning, in-
corporating technology into education, introducing information to students, and im-
proving the quality of education, need various analyzes to monitor the availability of
the system and the student's performance change. In this way, the concept of learning
analytics has emerged along with Technology-Enhanced Learning (TEL) [2].
In our age, the concept of educational technologies is becoming widespread. Teachers
and educators need to be aware of the importance of using technologies and tools to
increase student’s motivation and ensure their participation. New teaching skills in this
all-global education transformations should be adopted and implemented [3].
In this study scope, it is aimed to present an overview of existing learning analytics
concepts and methods strongly focused in conjunction with Massive Open Online
Courses(MOOCs). At the end of the study, a literature review about the concepts of
MOOCs and learning analytics is going to be compiled and the future tools’ function-
ality of learning analytics in MOOCs is going to be presented.
2 Introduction to Learning Analytics and MOOCs
Learning analytics term, initially stated in LAK 2011: 1st International Conference
Learning Analytics and Knowledge and used as a base by Society of Learning Analytics
(SoLAr) is defined as “the measurement, collection, analysis and reporting of data
about learners and their contexts, for purposes of understanding and optimizing learn-
ing and the environment in which it occurs” [4].
Learning is a social process that includes interactions with students, teachers, instruc-
tors and others. The instructors make an effort in their learning design to maximize
these interactions. All these preparations bring many questions as following: “How ef-
fective was the lecture? Did it meet the needs of the students? How can it be improved?
Is an update required in learning design?” etc. Large data are obtained with approaches
towards answering these questions. All these data to be used in developing teaching
and learning are statistically evaluated and analyzed with Learning analytics [5].
The first learning analytics conference that took place in Banff City, Canada in 2011,
brought together researchers from around the world to explore and develop learning
analytics [6]. Since 2011, there are education and learning platforms on the basis of
Learning analytics, with unprecedented development. Although it is a young area, there
are close relationships between Learning Analytics and Web Analytics, Educational
Data Mining (EDM) and Academic Analysis [7].
Place Fig 1 here
Fig. 1. Learning Analytics, Educational Data Mining, Web Analytics and Academic
Analytics [7].
The concept of distance education, which is method “old concept”, has undergone great
progress along with today's Internet and technology. By this means of distance educa-
tion, access information gets easy for users. Learners are positioned as persons who
access, produce, shape and analyze information individually. As access to information
becomes easier, the change to educate many people at once applied. Thus, the occur-
rence of the model called Massive Open Online Course or MOOCs was supported.
The term MOOCs has been first appeared in 2008 by Stephen Downes and George
Siemens. Subsequently, in 2011, the training videos of Stanford University professors
were published on free of charge online learning platforms. Same year, MOOCs ex-
ploded around the world; Later, Coursera, Udacity, Udemy, MITx platforms were es-
tablished. Thus showed great improvement towards [8].
In general, MOOCs support models, which are free learning and independent from time
and space. It also examines and follows the success and failure of the students. Moreo-
ver, it provides at least free accessibility for all participants.
Massive Open Online Course can be explained with the words of concepts in its name.
1-Massive: Massive means huge and gigantic. A large number of students participated
in the MOOCs is symbolized with this letter. Opened training can be held easily with
the participation of 100 people or with the participation of 100000 people. In addition
to the size of the number of students, with the conception of massive, outside the cam-
pus boundaries, the concept of class global class is also defined. At this juncture, learner
variations, educational material and transportation network size come into prominence
2-Open: This term symbolizes the openness to each individual with access to the Inter-
net. This is the most distinctive feature of MOOC-based education. Learners are free to
join or not to join, interact, analyze in the subjects and getting the education they would
like to learn. There are few to no prerequisites necessary to attend courses. There are
target groups addressed by courses. These prerequisites are only available in some ar-
eas, which require expertise. MOOCs comprise word of open by being mostly free of
charge [10].
3-Online: In the MOOCs model, management, data system and education are offered
entirely online. There is no physical place. All educations are actualized in the courses
which are accessible online and offered on the web [7].
4-Course: With this word, it is stated that the contents of the lectures are structured with
pedagogical approaches within the educational plan done by instructional designers.
3 Research Methods
This study aims to describe the methods, benefits and challenges of using and imple-
menting Learning Analytics in evaluating data of MOOCs. In order to support, the ef-
fective application of learning analytics in MOOCs, further clarification is required.
The trends of Learning Analytics in MOOCs will be examined and will be a guide for
future research. This research is supported by the literature review examining keywords
such as Learning Analytics and MOOCs.
It includes combinations of search terms deemed appropriate for the potential of the
study during the search. The search string used are "Learning Analytics" and "MOOCs"
and "Massive Open Online Course" and "Learning Analytics - MOOCs". After speci-
fying these search string, search queries were conducted in selected databases between
2009-2019 dates. A research, investigation and screening series, which started with
TUBibliothek Library of University search, was conducted. Afterwards, full-text search
of that search strings such as Learning Analytics and MOOCs were done in large data-
bases offering academic publications of Semantic Scholar, Web of Science, Google
Scholar, Research Gate, Scopus and Dergipark which is the academic field of research
that provides access to information in Turkey. In this way on this study has been inten-
sified with a comprehensive literature review to identify methods, benefits and chal-
lenges of Learning Analytics in MOOCs.
3.1 Data Collection Method
The data collection method used within the scope of the article is a thematic literature
review. The themes are defined from the revised literature and are based learning ana-
lytics on the achievements of MOOCs, which have been built over the last decade and
related to education, enabling a better theoretical understanding. The literature re-
viewed and discussed is comprehensive, the method for successful implementation is
based on relevance, taking into account the main purpose of identifying benefits and
The titles, abstracts, keywords of the articles as a Search String was searched in that
literature databases. A temporal filter was applied from 2009 to 2019 since Learning
Analytics is a relatively new field that emerged back in recent years. This search data
collection resulted in a total number of papers as shown in Table I.
Table 1. Literature Review on Databases
Place Table 1 here
As a result, as a part of this review, it was referenced a total of 49 articles, which were
fully read in the research process and cited in the context of the review of this study
4 Results of the Literature Review: Learning Analytics &
Data Science is a field that allows creating knowledge from data in various shapes that
are processed or unprocessed [11]. Learning Analytics is an Educational Data Imple-
mentation Science. It is based on the fundamentals of learning analytics, computer sci-
ence, statistical calculations, data mining, machine learning, and human-computer in-
teraction [12]. MOOCs are a mine to carry out learning analytics because all the behav-
iors that occur in MOOCs are much higher and higher than the amount of behavior
recorded in traditional education [13].
If we explain it in detail, Learning Analytics provides an opportunity to examine student
behaviors in MOOCs and to maintain the positive effects of the instructors' behaviors
on learners or to clarify their concerns. In this research, methods, benefits and chal-
lenges of Learning Analytics and MOOCs are examined as well as their limitations.
Learning Analytics, which contains different disciplines such as Computer Science,
Statistics, Psychology and Education, has not only technical and statistical analysis
methods but also different analysis methods.
To expand research on MOOCs, Learning Analytics methods described by Khalil et al.
have been studied. In MOOCs which is one of the online learning courses, learning
analytics is carried out by such methods: data mining technique, statistics and mathe-
matics, Text Mining, Semantics-Linguistics Analysis, visualization, Social network
analysis and Gamification [14].
4.1 Data Categories
4.1.1 Data Mining
It is a method in which uses data to explore student’s learning ways that have not been
found before. It aims to bring on understanding with the available data. Emotions of
students and their effects on the performance of the processes they perform in the sys-
tem can be researched with data mining in MOOCs [15].
Data mining in e-learning is conducted with Classification and Regression Approaches.
Future behaviors and attitudes in MOOCs can be designated by analyzing and pointing
former data. Collected answers of questions in Classification are always “true” or
“false” while they are represented as digits in Regression. The correct classification
ensures obtaining correct results from data [16].
Also, Data Mining can be executed on MOOCs due to it is able to be included in e-
learning environments. It supports to improve MOOCs environments. In the scope of
this method, significant information is procured in MOOCs by using Prediction, Clas-
sification, Association Rule in Mining, Cluster and Fuzzy Logic Techniques [17][18].
4.1.2 Statistical and Mathematical
Statistical and Mathematical methods are used to observe the relationship between par-
ticipation rates and achievements by analyzing students' educational backgrounds. With
this method based on numerical data, it is also possible to inform the instructors on
increasing the student motivation, by determining the students with low participation
in education [19].
There are data about students’ behaviors and actions on MOOCs. Data such as online
durations, number of being active, visited pages, percentage of reading and watched
materials are stored on the system. The results are obtained with Statistical and Mathe-
matics methods such as Average, Mean and Standard Deviation [20].
Furthermore, this method is used with some data mining techniques while it is applied.
A method named Markov Chain, which is a statistical, and mathematics method has
been also used on examining students’ behaviors [21].
4.1.3 Text mining- Semantics-Linguistics Analysis
Data from user-generated content, discussion sections on forums and blogs are inter-
preted through Text mining- Semantics-Linguistics Analysis. In the examination of ed-
ucation dropout in MOOC, research and analysis that will take place in the forum and
discussion sections can reveal a real result [22].
While this method is implemented, diverse techniques are used. Summarization Tech-
nique on summarizing by reducing the length properly, Categorization Technique on
classifying technique-documents, Retrievals Technique on obtaining the valuable in-
formation inside the text, Extract Technique on picking over information and Cluster
Technique on collecting and stacking documents besides analyzing texts are utilized
4.1.4 Visualization
Another applicable method to the MOOCs is the Visualization method. With this
method, the visualization of all the data obtained enables the user and researchers to
obtain meaningful information and make analyzes through MOOCs the training per-
formed [24].
Visualization is one of the key points about applying e-learning objects for data in ed-
ucation and MOOCs. The power of visualizing data comes from the enhancement of
data’s processability and enabling explain with occurred arguments [25]. This tech-
nique is used for doing proper learning, develop brainstorming and creativity. The fol-
lowings can be shown as examples for Visualization Technique; Mind map, Concept
Map, Cognitive Map, Radial Tree, Semantic Map, Rhizome, Visual Metaphor, Tree
Structure, Argument Map and Social Map [26].
4.1.5 Social Network Analysis
Social network analysis now occupies a significant place among the Learning analytics
methods. It is a method used by many researchers to understand and optimize the learn-
ing environment. Social network analysis provides an in-depth analysis of network
structures and participates in training engagement interactions [27].
Social Network Analysis is implemented to interpret and analyze relations among the
found data in works as well as interactions in communication tools [28]. It is a technique
that is continuously growing and developing new methods and approaches. On imple-
menting this technique, Practices such as Block Modelling and Equivalence, Signed
Graphs, Structural Balance and Analysis, and Dynamic Network Analysis are utilized
4.1.6 Gamification
Gamification method increases student motivation, learning platform usage and partic-
ipation in courses and makes learning entertaining. The practice of gamification in
MOOCs enables students and participants to achieve success an immersive experience
with high motivation and high participation [30].
There are differences in participation and training completion levels in MOOCs and
online learning environments. Students' motivation with simple game features and their
commitment to learning processes are increasing. By the help of the secret relationship
between computers and games, it is advantageous to use the gamification method in
online learning platforms [31]. Learning for learners is available by making transactions
in processes. Learners gain ability to manage their time and speed while improving
their ability to follow. In this process, learners can experience sense of wonder, mys-
tery, fun, excitement and sadness when they are learning [32]. The most used gamifi-
cation methods in education are defined as Visual Status, Social Engagement, Freedom
of Choice, freedom to fail, Rapid Feedback and Progress [33].
4.2 Current Situations
Considering all these methods, it would not be wrong to state that the application of
Learning Analytics on MOOCs has benefits and difficulties. MOOC platforms and sim-
ilar online learning platforms are data stores. It is a data mine to perform learning ana-
lytics in MOOCs with mouse clicks, forums and discussion activities, on-the-fly and
post-test performances, entry frequency and active time, time spent on work and as-
signments, and video interactions. In this way, direct intervention can be provided to
the success of the student and the quality of education [34].
Table 2. 6 Areas of Learning Analytics and MOOCs
6 Areas of Learning Analytics and MOOCs
Data Mining
It is an observation oriented learning
analytics technique that contributes
to student interaction and teaching
models design[35].
Association rule
in mining
Fuzzy Logic
Statistical and
It is the analysis and interpretation
of quantitative data to reach infor-
Standard devio-
Markov Chain
Text mining- Se-
tics Analysis
It is a method of reaching useful in-
formation by discovering and defi-
ning meaningful ones from the texts
on the system[36].
It is a visual conversion of informa-
tion by using special engineering
techniques in original forms of
Mind Map
Concept Map
Cognitive Map
Radial Tree
Semantic Map
Visual Metaphor
Tree Structure
Argument Map
Social Map
Social Network
Relational bonds and behaviors are
measured, defined, analyzed and ta-
ken precautions[38].
and equivalence
Signed graphs
and structural ba-
lance analysis
network analysis
It is used to increase the quality and
quantity of activity and training out-
puts by increasing user effective-
Visual Status
Social enga-
Freedom of cho-
Freedom to fail
Rapid feedback
Although MOOCs are among the education trends in the world today, they comprise
many challenges within. The determination of the needs of learning and the suitability
of education is very complex on the system. In general, in the statistics of MOOCs, it
occurs a low rate of fulfilment on education, despite remarkable high rate of the regis-
tered participant. The concept of learning analytics supports to solve and analyzes such
cases that are mentioned above [40].
According to the study by Khail, Taraghi and Ebner (2016), the benefits of learning
analytics in MOOCs are limitless. These are indicated as Prediction, Recommendation,
Visualization, Entertainment, Benchmarking, Personalization, Enhance Engagement,
Communication Information and Cost Saving [34].
Learning analytics aims to develop students' knowledge and skills with data obtained
from online learning platforms and MOOCs. When this method uses, students' learning
quality is maximized. Examining, using and analyzing the obtained data provide several
benefits for education.
Learning Analytics and MOOCs together enables students to interpret their own results
and review their performance, to instructors to affect the learning of students or student
groups in MOOCs. Moreover, they also provide an advantage to management about
learning outcomes and using personal programs [41].
With the analysis of the data, instructors can determine renewals in the curriculum and
students’ weaknesses in learning and understanding. Instructors can make educational
strategic changes over the curriculum. These renewals provide an advantage for im-
proving curriculum quality and learning potential of students [42].With the implemen-
tation of learning analytics, instructors can easily identify individual errors [43].
In this study, MOOCs and Learning Analytics concepts, methods, benefits and chal-
lenges of the implementation of Learning Analytics in MOOCs have been put forth. It
is seen that the application of Learning Analytics in online learning platforms and es-
pecially MOOC platforms are interdisciplinary studies. There is a need to operate a
comprehensive process from all sorts of inputs and outputs on MOOCs, from students
and participants to educators. There are many techniques such as Visualization, Statis-
tical and Mathematical etc. and pedagogical methods such as Gamification in order to
make learning analysis true. These methods which can be applied in MOOCs are men-
tioned. As part of the literature review, references to these methods and fields are given
in Table 3.
Table 3. 6 Areas Reference Review
Place Table 3 here
Implementations of these methods gain favors on converting large amounts of data in
MOOCs into information and quality. It provides useful information about the needs of
students, classroom interactions of teachers, educational activity and curriculum ar-
rangements. In the future, as the usage and the development of technologies advance,
new advantages and benefits will arise
4.3 Typical Examples
In the following paragraphs we would like to list a typical example for each area ac-
cording to table 2.
Data Mining: In the study conducted by Mukala et al., Learning behaviours of students
have been analyzed by examining the correlation between the students’ video course
follow-ups and success. Using the data mining technique, it is seen that the student's
video surveillance have a direct impact on performance. In addition, it has been ob-
served that behaviour studies can be developed and necessary improvements and
measures can be taken for the future with the interesting parts of the course examined
and the data of the most skipped or repeated sections [44].
Statistical and Mathematical: In the study carried out by Taraghi et al., Data set analysis
was performed by considering the answering time of the students and it has been deter-
mined the easiest and most difficult questions in different types. The questions were
classified as difficult, medium, easy with the K-means algorithm method. Thus, it was
concluded that the question selection algorithm will be developed with the Markov sta-
tistical analysis method which will be applied for each question and this will affect the
performance of the students [21].
Text mining- Semantics-Linguistics Analysis: In the study of Tucker and Pursel on
MOOCs, they investigated the effects of the texts produced by the students on the stu-
dent performances and outputs by using the Text mining - Semantics-Linguistics Anal-
ysis technique. According to the results of the study, it was found out that the students'
feelings had an impact on exam performance and homework. During the research, text
data was obtained via MOOCs and the data were arranged on SQL. This technique was
applied by using emotional analysis algorithms on the textual data and results were
obtained [45].
Visualization: Zhang and Yuan have worked on video recordings in MOOCs to explore
the relationship among courses. Within the scope of the study, it was seen that all par-
ticipants registered at least two courses while there was not any participant in only one
course. A visualized network diagram was applied to the relationship between courses
and learners. With the performed analysis, it was concluded that it is possible to enroll
in lectures at the same time. In this way, an environment will be created to present the
other open courses for registered participants [46].
Social Network Analysis: Kellogg, Booth and Oliver have applied the Social Network
Analysis technique in their studies in order to present a peer-assisted learning approach
in MOOCs. This method has been preferred to measure and visualize interaction pat-
terns within the scope of the study. To implement this statement, they have used the
free NodeXL template, which is an add-in to Excel. Thus, they explained the im-
portance and use of peer interaction in training prepared for educators [47].
Gamification: In their study, Vaibhav and Gupta have done Gamified and Non Gami-
fied groups on MOOCs and analyzed the differences. It was seen that the completion
rate of courses was more successful in the platforms using gamification technique. The
number of students who refused to take the final exam decreased by 14% in gamified
platforms. It is explained based on the survey results that students have fun and devel-
opments in their education by Gamification application [48].
As it is seen in these studies; learning analytics in MOOCs provides many benefits; On
the other hand, it includes ethical, legal, technological and security challenges. On this
topic some precautions should be taken on pedagogical transparency, data control and
Generally, the advantages of learning analytics in MOOCs are indicated as follows
Determining the target course
Improvement of the curriculum
Student learning outcomes, behaviors and processes
Individualized learning
Teacher performance improvement
Post-educational employment.
Although learning analytics in MOOCs provides many benefits over learning, there are
some difficulties in the implementation process. The data which is obtained on the plat-
forms is not knowledge. This data should be transformed into information and
knowledge by processing, then interpreted correctly. Learning is a complicated process.
Learning also contains cultural behaviors. While learning analytics offers technical
analysis, it requires trainers to use different culturally and behaviorally educational
models in interpretation .To perform and interpret can take time on Learning Analysis.
Considering the individuality of learning, these issues can be seen as challenges.
According to Ferguson, some of these challenges [49]:
The necessity of establishing strong connections with learning science
Difficulty in understanding and optimizing learning environments and MOOCs.
Focus on students’ point of view
Not to develop and implement a clear set of ethical rules
There is no doubt that the challenges with envisaged by our age technologies and rap-
idly evolving platforms will be solved in the future. Learning analysis and MOOCs,
with contributions to education and learning, is a scientific field and it is continuously
Learning Analytics and MOOC with all their content are pioneers in education. They
will continue to be the focal point on implementing large data analysis in education and
online learning platforms.
5 Discussions
That Literature Review provides a comprehensive overview of the Learning Analytics
methods, benefits, and challenges of MOOCs. The examination of these features
showed positive contributions. However, with the literature review, it has also been
revealed from publications that the use of Learning Analytics in education has increased
over time.
This study revealed that Learning Analytics is an interdisciplinary field as suggested
by Clow (2013). Therefore, learning analytics selects and uses the most appropriate
methods and analysis techniques to achieve the goal of making education more efficient
at MOOCs. The use of an inclusive method will lead to improved learning experience
for students.
6 Conclusions
Learning Analytics is a new area and ever developing concept. In additional to this,
MOOCs with the huge data sources have an important role in applying learning analy-
sis. The state-of-the-art methods of learning analytics in MOOC are described in this
study. Data mining, statistics and mathematics, Text Mining, Semantics-Linguistics
Analysis, visualization, Social network analysis and Gamification areas are imple-
mented Learning Analytics in MOOCs allied with benefits and challenges. In the future,
it is expected that the implementation of learning analytics and large data will be effec-
tively used more and more in MOOCs to understand how learning is happening.
1. Saqr, M.: A Literature Review of Empirical Research On Learning Analytics in Medical
Education. International Journal of Health Sciences 12(5),1-6 (2018).
2. Bahçeci, F.: Öğrenme Yönetim Sistemlerinde Kullanılan Öğrenme Analitikleri Araçlarının
İncelenmesi. Turkish Journal of Educational Studies 2(1), 41-58 (2015).
3. Chang V., Gütl C., Ebner M.: Trends and Opportunities in Online Learning, MOOCs, and
Cloud-Based Tools. Springer International Handbooks of Education, Springer International
Publishing.1-19 (2018).
4. Siemens, G.: Learning Analytics the Emergence of a Discipline. American Behavioral Sci-
entist 57(10), 1380-1400 (2013).
5. Elias, T. :Learning Analytics: Definitions, Processes and Potential, https://landing.athabas-, last accessed 2019/04/23.
6. Dawson, S., Joksimovic, S., Poquet, O., Siemens, G.: Increasing the Impact of Learning
Analytics. In Proceedings of the International Conference on Learning Analytics and
Knowledge, ,last accessed
7. Khalil, M.: Learning Analytics in Massive Open Online Courses. Doctoral Thesis, Graz
University of Technology, Graz (2018).
8. Baturay, M.H.: An overview of the world of MOOCs. Procedia - Social and Behavioral
Sciences 174, 427-433 (2015).
9. Bozkurt, A.: Kitlesel Açık Çevrimiçi Dersler (Massive Open Online Courses - MOOCs) ve
Sayısal Bilgi Çağında Yaşamboyu Öğrenme Fırsatı. Açıköğretim Uygulamaları ve
Araştırmaları Dergisi 1(1), 56-81 (2015).
10. Mali,A.K. :Massive Open Online Courses,.EduInspire: An International E-Journal 3(1) 1-9
11. Dhar, V.: Data Science and Prediction. Communication of the ACM 56(12), 64-73(2013).
12. Piety, P.J., Behrens, J., Rea, R.: Educational Data Science and The Need for Interpretive
Skills. American Educational Research Associations 27(2013).
13. O’Reily, U.M., Veeromochoneni, K.: Technology for Mining the Big Data of MOOCs. Re-
search&Practice in Assessment 9, 29-37 (2014).
14. Khail, M., Ebner, M.: What is Learning Analytics about? A Survey of Different Methods
Used in 2013-2015. Smart Learning Conference Dubai, 294-304 (2016).
15. Tucker, C., Pursel, B.K.,Divinsky, A.: Mining Student-Generated Textual Data In Moocs
And Quantifying Their Effects On Student Performance And Learning Outcomes. Comput-
ers in Education JournaL 5(4), 84 (2014).
16. Ionita, I.: Data Mining Techniques for E-Learning. Journal of Applied Computer Science &
Mathematics 10(22), 26-31 (2016).
17. Abd Elaal. S.A.E.: E-Learning Using Data Mining. Chinese-Egyptian Research Journal
18. Lile, A.: Analyzing E-Learning Systems Using Educational Data Mining Techniques. Med-
iterranean Journal of Social Sciences 2(3),403-419 (2011).
19. Al-Shabandar,R.,Hussain, A.J., Liatsis,P., Keight, R.: Analyzing Learners Behavior in
MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach.
IEEE Access PP,99 (2018)
20. Chatti, M.A., Dyckhoff, A.L., Schroeder, U., Thüs, H.: A Reference Model for Learning
Analytics. International Journal of Technology Enhanced Learning (IJTEL) 4(5), 318-331
21. Taraghi, B., Saranti, A., Ebner, M., Schön, M.: Markov Chain and Classification of Diffi-
culty Levels Enhances the Learning Path in One Digit Multiplication. In International Con-
ference on Learning and Collaboration Technologies Springer, 322-333 (2014).
22. Wen, M., Yang, D., Rose,C.P.: Sentiment Analysis in MOOC Discussion Forums:What
Does It Tell Us?.Conference: Educational Data Mining (2014)
23. Dang, S.,Ahmad, P.H.: Text Mining : Techniques and its Application. IJETI International
Journal of Engineering & Technology Innovations 1(4), 22-25 (2014).
24. Gama, S., Gonçalves, D.: Visualizing Educational Datamining Patterns. Eurographics Con-
ference on Visualization (EuroVis), United Kingdom (2014).
25. Williamson,B. :Digital Education Governance: Data Visualization, Predictive Analytics,
And ‘Real-Time’ Policy Instruments. Journal of Education Policy 31(2), 123- 141 (2016).
26. Latha, S., Raj, M.R.C., Sukumaran, R.: Use of Visualization Techniques for Active Learning
Engagement in Environmental Science Engineering Courses. International Scholarly and
Scientific Research & Innovation 10(4), 1340-1344 (2016).
27. Filva, D.A., Penalvo, F.J.G., Forment,M.A.: Social Network Analysis Approaches for Social
Learning Support.Proceedings of the Second International Conference on Technological
Ecosystems for Enhancing Multiculturality, Spain, 269-274(2014).
28. Perovic,D.: Data Mining Influence On E-Learning. The Sixth International Conference on
e-Learning,Serbia,Belgrade (2015).
29. Butts,C.T.: Social Network Analysis: A Methodological Introduction. Asian Journal of So-
cial Psychology 11, 13-41 (2008).
30. Khalil, M., Wong, J., de Koning, B. B., Ebner, M., Paas, F.: Gamification in MOOCs: A
Review of the State of the Art, IEEE Global Engineering Education Conference, 1635-1644
31. Glover, I.: Play As You Learn: Gamification as a Technique for Motivating Learners, Shef-
field Hallam University Research Archive, ,last ac-
cessed 2019/04/22.
32. Brull, S., Finlayson, S.: Importance of Gamification in Increasing Learning,.The Journal of
Continuing Education in Nursing 47(8),372-375 (2016).
33. Dicheva, D., Dichev, C., Agre, G.,Angelova, G.: Gamification In Education: A Systematic
Mapping Study. Educational Technology & Society 18(3),75-88 (2015).
34. Khail, M., Traghi, B., ,Ebner, M. :Engaging Learning Analytics In MOOCS: The Good, The
Bad, And The Ugly. International Conference On Education and New Developments (2016).
35. Chang, G., Healey, M., McHugh, J.A.M., Wang, T.L. :Mining the World Wide Web An
Information Search Approach,Kluwer Academic Publisher, Boston (2001).
36. Sundari, J.G.,Sundae, D.: A Study of Various Text Mining Techniques. International Journal
of Advanced Networking & Applications (IJANA) 8(5), 82-85 (2017).
37. Pardos, Z.A.,Horodyskyj ,L.: Analysis of Student Behaviour in Habitable Worlds Using
Continuous Representation Visualization,.The Journal of Learning Analytics 6(1), 1-15
38. Müller, T.:Social Network Analysis: A Practical Method To Improve Knowledge Sharing,
Hands-On Knowledge Co-Creation And Sharing; Practival Methods And Techniques, , last accessed 2019/04/25.
39. Morschheuser, B., Hassan, L.,Werder, K., Hamari, J.: How To Design Gamification? A
Method for Engineering Gamified Software, Information and Software Technology 95, 219-
237 (2018).
40. Chea,C.: Benefits And Challenges Of Massive Open Online Courses. ASEAN Journal of
Open Distance Learning 8(1),16-23 (2016).
41. Manderveld,J.: Grand Challenges For Learning Analytics And Open & Online Education.
Open and Online Education, 70-74 (2015).
42. Avella, J.T., Kebrithchi, M., Nunn,S.G., Knai, T.: Learning Analytics Methods, Benefits,
and Challenges in Higher Education: A Systematic Literature Review. Online Learning
20(2) (2016).
43. Ebner,M., Pronegg,M.: Use of Learning Analytics Applications in Mathematics with Ele-
mentary Learners. International Journal of Academic Research in Education 1(2), 26-39
44. Mukala, P., Buijs, J. C. A. M.,Van Der Aalst, W. M. P.: Exploring Students’ Learning Be-
haviour In Moocs Using Process Mining Techniques, Department of Mathematics and Com-
puter Science, University of Technology, Eindhoven, The Netherlands, 179-196 (2015).
45. Tucker, C., Pursel, B.K .: Mining Student-Generated Textual Data In Moocs And Quantify-
ing Their Effects On Student Performance And Learning Outcomes,.The ASEE Computers
in Education (CoED) Journal 4(5), 84 (2014).
46. Zhang, T., Yuan, B.: Visualizing MOOC User Behaviors: A Case Study on XuetangX. In
International Conference on Intelligent Data Engineering and Automated Learning,
Springer, Cham, 89-98 (2016)
47. Kellogg, S., Booth, S., Oliver, K.: A Social Network Perspective On Peer Supported Learn-
ing in MOOCs for Educators. The International Review of Research in Open and Distributed
Learning 15(5) (2014).
48. Vaibhav, A., Gupta, P.: Gamification of Moocs for Increasing User Engagement. In 2014
IEEE International Conference on MOOC, Innovation and Technology in Education
(MITE), 290-295(2014).
49. Ferguson, R.: Learning Analytics: Drivers, Developments and Challenges. International
Journal of Technology Enhanced Learning, 4(5/6), 304317 (2012).
... EDM is considered as a methodology for mining regularities from big educational data that are gathered in educational environments [2]. LA is aimed at tools development for analyzing and optimization learning [3,4]. ...
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