Driving Student Motivation in MOOCs through a Conceptual Activity-Motivation Framework

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DOI: 10.3217/zfhe-12-01/06
Abstract
Massive Open Online Courses (MOOCs) require students' commitment and engagement to earn the completion, certified or passing status. This study presents a conceptual Learning Analytics Activity-Motivation framework that looks into increasing students' activity in MOOCs. The proposed framework followed an empirical data analysis from MOOC variables using different case studies. The results of this analysis show that students who are more active within the offered environment are more likely to complete MOOCs. The framework strongly relies on a direct gamified feedback that seeks driving students' inner motivation of competency.
Scientific Contribution
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Mohammad KHALIL & Martin EBNER
1
(Graz)
Driving Student Motivation in MOOCs through
a Conceptual Activity-Motivation Framework
Abstract
Massive Open Online Courses (MOOCs) require students’ commitment and
engagement to earn the completion, certified or passing status. This study presents
a conceptual Learning Analytics Activity-Motivation framework that looks into
increasing students’ activity in MOOCs. The proposed framework followed an
empirical data analysis from MOOC variables using different case studies. The
results of this analysis show that students who are more active within the offered
environment are more likely to complete MOOCs. The framework strongly relies on
a direct gamified feedback that seeks driving students’ inner motivation of
competency.
Keywords
Learning Analytics, Massive Open Online Courses (MOOCs), Motivation, Activity,
Framework
1
email: martin.ebner@tugraz.at
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1 Introduction
Distance learning in the form of Massive Open Online Courses (MOOCs) has ex-
perienced a quantum leap in the development of open educational resources and
educational technology. The main advantage of such courses is that hundreds or
even thousands of students can enrol in one course, which normally impossible in a
regular classroom setting. The story of MOOCs started with Siemens and Downes’
first course in 2008 and reached an early peak with Sebastian Thrun’s “Introduc-
tion to Artificial Intelligence” course, which attracted more than 160,000 students
from all over the world (YUAN & POWELL, 2013). Since then, MOOCs have
received significant attention from the media and educationalists for their potential
to extend the reach of education via technology.
MOOCs allow anyone to learn and interact through available learning technology-
enhanced learning materials and tools such as video lectures, recommended arti-
cles, content downloads, discussion forums, assessments, etc. The two popular
MOOC models, cMOOCs (connectivism MOOC) and xMOOCs (extended
MOOCs), deliver courses at a remarkably large scale in terms of enrollments, di-
versity of topics and geographical reach. In addition, the characteristics of the open
environment of MOOCs bring a distinct range of motivations and beliefs among
students (LITTLEJOHN et al., 2016). Assuming the direct interaction between
teachers and students does not reach the level of traditional face-to-face classroom
lectures, students are forced to organize their own learning. Furthermore, MOOCs
differ from the traditional settings in which student engagement varies in objec-
tives. As a result, the need for students to self-regulate their learning, maintain their
personal motivation (ZIMMERMAN, 2000), and actively interact with online
learning objects (KHALIL, KASTL & EBNER, 2016) becomes crucial.
To increase great learning activities, newly adopted technologies in MOOCs allow
researchers to track students’ behavior (i.e. what they do and how they learn) by
examining stored information about student engagement with different digital
learning activities (e.g. videos, discussion forums, and quizzes). The collection of
such a high volume of student data transforms the conventional data analytics into
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so-called “Big Data” analytics. Examining large data sets of student interactions
and activities with online learning platforms provides a valuable opportunity to
discover patterns and understand student behavior. To that end, there are two ap-
proaches for examining data in educational settings: Educational Data Mining
(EDM) and Learning Analytics (PAPAMITSIOU & ECONOMIDES, 2014). Rely-
ing on data analytics, both approaches share common goals of improving education
and optimizing learning environments.
Despite the fact that MOOCs have great benefits, there are corresponding challeng-
es that affect their growth, especially in higher education courses. For instance,
keeping students engaged and motivated (KHALIL, TARAGHI & EBNER, 2016;
XU & YANG, 2016), the high attrition rate, the boring pedagogical design
(STACEY, 2014). Being on the same track, Learning Analytics shows great poten-
tial when meets MOOCs (KNOX, 2014). The key benefits of Learning Analytics in
connection with MOOCs are embodied in predicting, visualizing, recommending,
personalizing, saving costs, and improving students’ engagement (SIEMENS &
BAKER, 2012; KHALIL, TARAGHI & EBNER, 2016).
In this research study, we aim at preserving students’ engagement (and participa-
tion) within the MOOC sphere. Based on the empirical data from the Austrian
MOOC platform, iMooX (http://www.imoox.at), this study proposes a framework
to improve students’ activity by examining various MOOC indicators. The article
is outlined along the following hypothesis and research question:
Hypothesis: There is a relation between MOOC learning activities a stu-
dent performs and his/her retention till the end of the MOOC.
RQ: How could we motivate MOOC students to stay active during the
MOOC?
The paper begins with a literature review on Learning Analytics and Learning Ana-
lytics of MOOCs as well as the related topics of students’ motivation and activity.
After that, we describe the used methodology to validate the hypothesis and answer
the research question with a description of the investigated dataset and its analysis.
Further, the proposed Activity-Motivation module that describes our scheme of
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motivating students is discussed. At the end of the article, the key findings are
summarized.
2 Related Work
In order to provide the context of this study, this section gives a brief literature
review of Learning Analytics and Learning Analytics of MOOCs, and reads previ-
ous studies of motivation and activity in online learning environments.
2.1 Learning Analytics
The prominent field of Learning Analytics has been widely discussed since the first
international conference on Learning Analytics and Knowledge in 2011 (LAK’11).
While there were a plethora of definitions describing its objectives, the Learning
Analytics community has agreed to finally define it as “…the measurement, collec-
tion, analysis and reporting of data about learners and their contexts, for purposes
of understanding and optimizing learning and the environments in which it occurs
(SIEMENS ET AL., 2011). Learning Analytics uses the generated data from stu-
dents in order to discover patterns and develop insights into their behaviors. The
borderless Internet and the increasing demand to reduce attrition as well as enhance
learning environments and learning are considered to be the major driving factors
behind the emergence expansion of this field (SIEMENS et al., 2011; PA-
PAMITSIOU & ECONOMIDES, 2014; KHALIL & EBNER, 2016a).
Acquiring and analyzing students’ data are no more than two stages that open an
iteration loop in the holistic Learning Analytics lifecycle. According to Doug Clow
(2013) paper The Learning Analytics Cycle: Closing the loop effectively”, Learn-
ing Analytics loop should be closed in a way to invest the analysis phase outcome
with proper action(s). Provided that, Learning Analytics encompasses four main
phases (KHALIL & EBNER, 2015): a) learners generate data, b) data gets pro-
cessed, c) results get interpreted, and finally d) actions are optimized.
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2.2 Learning Analytics of MOOCs
Software and online platforms are usually supported by event recording systems,
called log files. MOOC platforms are online approaches that are set up using Inter-
net programming languages such as HTML or JavaScript. MOOCs’ log files record
every event happening on the platform and on that basis, Learning Analytics appli-
cations of MOOCs are developed to mine and interpret such data in order to inter-
vene or predict actions (KHALIL & EBNER, 2016a).
Despite their great advantages in online learning, MOOCs face challenges of drop-
out, disengagement, and lack of motivation (KHALIL & EBNER, 2016b). For such
purposes, researchers were looking into finding adequate algorithms, tools, or ele-
ments to surpass MOOC issues. There are various research studies and topics re-
garding Learning Analytics and MOOCs. For example, Kizilcec and his colleagues
focused on engagement among students in MOOCs (KIZILCEC, PIECH &
SCHNEIDER, 2013). They developed a classification model to identify a small
number of longitudinal engagement trajectories. Another study about engagement
is based on the examination of assignments and video lecture views (ANDERSON
et al., 2014). The authors clustered MOOC students into five subpopulations:
Viewers, Solvers, All-Rounders, Collectors, and Bystanders.
Other research studies utilized Learning Analytics to detect and intervene before a
student drops out of the course. For instance, a recent study by XING et al. (2016)
developed a mechanism to detect at-risk students through their activity in discus-
sion forums. The researchers used the decision tree as an Educational Data Mining
technique for building the at-risk detection algorithm. Researchers from HTW Ber-
lin were able to predict students’ success based on MOOC discussion forum by
building student profiles (KLÜSENER & FORTENBACHER, 2015).
2.3 Student Activity and Motivation
Recent work on MOOCs revealed a high attrition scale in activities in the first two
weeks (BALAKRISHNAN & COETZEE, 2013). The authors reported a 50%
dropout at the end of the second week. Some researchers suggested cutting course
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duration in half (LACKNER, EBNER & KHALIL, 2015). Others pushed the con-
cept of grabbing students’ attention by looking into boosting the extrinsic factors
such as offering badges, certificates and honor awards (WÜSTER & EBNER,
2016). Researchers from Northeastern University of China have noticed that the
activities performed by students in the MOOC platform reflect their motivation
(XU & YANG, 2016). The authors concluded a strong relation between someone’s
behavior and his/her evaluation of excitation. From there, they tried to find a rela-
tion between grade prediction and certification ratio along with their activities in
the MOOC through a developed classification model. While such prediction might
be hard to examine because of the complex nature of the predictive models (KLÜ-
SENER & FORTENBACHER, 2015), others used online surveys and semi-
structured interviews to identify learners’ motivation (LITTLEJOHN et al., 2016).
Further research about understanding the motivation of online learners in MOOCs
can be found in the article by Stanford University researchers who listed 13 factors
that could captivate learners’ motivation (KIZILCEC & SCHNEIDER, 2015). De-
spite their benefits, online surveys lack the proper target group and might provide
inaccurate results.
This study was further influenced by a couple of Learning Analytics applications,
which were considered in our proposed framework. One of these tools was Course
Signals (ARNOLD & PISTILLI, 2012). It is an application that provides feedback
according to the traffic light system. Whenever a green light is shown, it means that
the student is on track, whereas the orange and the red lights imply at-risk situa-
tions and intervention(s) by either a teacher or an institution would be required.
The literature review described previously is strongly related to our research. By
examining engagement and activity either in the discussion forums or video events,
this research study leverages the data from MOOC variables to preserve students’
activities and motivate them to stay engaged. The existing literature, however, pro-
vides very little research in regards to direct Learning Analytics feedback for stu-
dents on MOOC platforms. Despite the fact that showing statistics or gamification
elements to students is usually obtainable in most online environments as a motiva-
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tion factor, to the best of our knowledge, we could rarely find a module that looks
at preserving students’ activity in MOOCs and providing them a direct feedback.
3 Methodology
Our methodology focused on obtaining data from the following MOOC variables:
watching video lectures, login frequency, posts in forum, reading of forum posts,
and quizzes in order to identify a competent activity level. Henceforth, an analysis
that includes finding patterns in visualizations and an examination using explorato-
ry analysis on empirical data was conducted. Data collection was performed using
the iMooX Learning Analytics Prototype (iLAP). iLAP is a Learning Analytics
application developed to track students on the iMooX MOOC-platform to improve
online learning and to provide a rich repository of data for research purposes
(KHALIL & EBNER, 2016a). When students log into the MOOC platform, the
database starts to be filled with low-level data related to students’ performance and
behavior. Every action performed is recorded, saved and filtered for a large scale
processing phase. We followed the content analysis methodology (NEUENDORF,
2002), in which these variables were measured and referenced to answer the re-
search questions. The study also employed WANG and HANNAFIN’s (2005) de-
sign-based research methodology that depends on identifying goals, collecting data
during the whole design process, and refining according to the required goals.
3.1 Dataset Description
iMooX MOOC-platform offers various courses which target people from German
speaking countries from secondary school level to Higher Education and beyond.
As a case study, we have chosen a MOOC called “Gratis Online Lernen” which
translates to “Free Online Learning”, abbreviated in this article as GOL-2014 and
GOL-2015 (EBNER, SCHÖN & KÄFMÜLLER, 2015). The course has been of-
fered in a continued series in the years: 2014 and 2015, and educates people about
using the Internet for learning. The MOOCs duration was set to be 8 weeks with a
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workload of 2 hours/week (in total, 16 hours). Students had to score 50% in every
weekly quiz in order to pass. The MOOC platform offers self-assessment quizzes
in which every test can be repeated up to five times with a systematic approach to
consider the highest grade out of the attempts.
The main content of the courses were video lectures with an average duration of 5
minutes per video. Students were rewarded with certificates after they successfully
passed all the quizzes.
4 Dataset Analysis
In this section, we try to validate the questioned hypothesis by examining whether
the certified students show more activity using MOOC variables (forums and vide-
os). For this purpose, we chose to analyse the following three MOOC variables:
posts in forum, views in forum and video lectures for MOOCs GOL-2014 and
GOL-2015. We split the students into two categories: certified and non-certified.
The first group includes those who completed a MOOC and therefore received a
certificate at the end of the course, while the second group includes the students
who dropped out of the MOOC at any time during the course. The certified stu-
dents in GOL-2014 and GOL-2015 were (N= 193, N= 117) respectively, while the
non-certified students in GOL-2014 and GOL-2015 were (N= 810, N= 359). The
analysis results in the following subsections proved that learning activities have
quite an impact on students to persist in a massive open online course.
4.1 Forum Readings Analysis
During the 8 weeks of forum discussions, there were 22,565 views of forum
threads in GOL-2014 and 8,214 views of forum threads in GOL-2015. Figure 1a
and figure 1b show the average number of thread views for both MOOCs. The
difference between the reading activity of the two groups is quite obvious. Figure
1a depicts a maximum number of reads in week 1 for both groups, which rapidly
drops until week 4. This follows the condition that attrition rate becomes more
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stable after the first four weeks of a MOOC (LACKNER, EBNER & KHALIL,
2015). However, in figure 1b, we realized that certified students’ forum views es-
calated in week 5 and then dropped to around 4 views per user till the end of the
MOOC. A study by Wong and his colleagues recorded similar student behavior
(WONG et al., 2015). The authors unveiled that active users showed higher activity
after the first weeks of the MOOC.
Figure 1a: The average number of discussion forum views in GOL-2014 MOOC
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Figure 1b: The average number of discussion forum views in GOL-2015 MOOC
4.2 Forum Posts Analysis
In total, in GOL-2014 there were 828 and in GOL-2015 there were 408 posts writ-
ten in the respective forum. These posts took the forms of comments, threads, and
replies. Figure 2a and Figure 2b illustrate the average number of written posts in
both MOOCs forums.
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Figure 2a: The average number of discussion forum posts in GOL-2014 MOOC
Figure 2b: The average number of discussion forum posts in GOL-2015 MOOC
In fact, it is apparent that certified students are more active in posting and com-
menting in MOOC forums. In Figure 2a, the average number of contributions is
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very low after the fourth week. There are various reasons for this, such as the steep
drop out rate after the first weeks (see Figure 3), or the low motivation to contrib-
ute and comment (MANNING & SANDERS, 2013).
Figure 3: Remarkable activities attrition after the first weeks of the GOL-2014
and GOL-2015 MOOCs
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4.3 Video Lectures Analysis
The third MOOC variable we analyzed was video lectures. Video content was
hosted on YouTube; however, the iLAP can only mine events of participants using
play and pause/stop that happen on the iMooX platform. We summed up the total
number of video interactions and showed the average number of events (play,
pause, and full-watch) per week. There were 17,384 video events in GOL-2014 and
8,102 video events in GOL-2015. Figure 4a and Figure 4b show a graph line of
learner interactions in GOL-2014 and GOL-2015.
Figure 4a: The average number of video events in GOL-2014 MOOC
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Figure 4b: The average number of video events in GOL-2015 MOOC
The figures displayed above show that the average number of video events of certi-
fied students is undoubtedly higher than non-certified students. Non-certified stu-
dents show weak video lectures activity.
4.4 Data Analysis summary
The seven figures in the previous subsections showed that the active users demon-
strated higher activity during the MOOC weeks. With regards to students’ forum
activities, we found there was an obvious gap between certified and non-certified
students that made us consider the first hypothesis. Motivated students are more
likely to engage in discussion forums (LACKNER, EBNER & KHALIL, 2015).
Gilly Salmon identified four learner strategies in online discussions: (1) “Some do
not try to read all messages.” (2) “Some remove themselves from conferences of
little or no interest to them, and save or download others.” (3) “Others try to read
everything and spend considerable time happily online, responding where appro-
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priate.” (4) “Yet others try to read everything but rarely respond.” (SALMON,
2007). The data presented in sections 4.1 and 4.2 corresponds to Salmon’s learner
types 1, 2 and 4. Non-active students do not ask questions or comment in the fo-
rums. Presumably, certified students are more likely to post questions to ask a
teacher or colleague for help which means they are more active in forums.
The video analysis also showed the difference between certified and non-certified
students. As MOOCs rely on videos, students need to watch them in order to pass
quizzes. Thus, active students who want to pass quizzes need to watch videos, ex-
cept for some cases where students try to game the MOOC system (KHALIL &
EBNER, 2016c).
5 Proposed Activity-Motivation Framework
According to the previous analysis results and the impact of activities on students’
motivation to complete a MOOC, we propose an Activity-Motivation framework to
motivate learners to do more activities. We designed this framework in corre-
spondence to the iMooX MOOC-platform’s potential of offering variables such as
quiz attempts, logins, forum posts and views, and the tested empirical data in sec-
tion 4. The Activity-Motivation framework intends to assist in increasing students’
motivation and engagement.
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Figure 5: The Activity-Motivation framework
The proposed model is shown above in Figure 5 and consists of four main dimen-
sions. Each of the dimensions contributes with a portion to a gamification element.
Our choice of such items was a battery as we believed it expresses a “filling up”
animation. We thought that what happens to a battery is similar to what a student
does with the MOOC activities. We aim to keep the students charged with activity,
motivation, and incentive. Gamification elements have a positive impact on student
motivation and learning (GONZALEZ, TOLEDO & MUNOZ, 2016).
The four dimensions of the proposed framework are: login, video, quiz, and forum.
It is worth noting that these dimensions can be extended and are not exhaustive to
the ones listed. For instance, extra dimensions involve readable content such as a
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downloadable article or assignments, can be included when required. The gamifica-
tion element was divided into four segments based on the number of selected
MOOC variables. The proposed Activity-Motivation model can be implemented as
a plugin or as an independent tab on the MOOC page and would be updated on a
weekly-basis. In the following paragraph, we will briefly elaborate on the model
and describe its mechanism. As seen below, each element counts for a 25%
charged portion in the battery:
Login: When a student logs into the MOOC, he/she will reflect relatively
on the gamification element (battery). The first segment of the battery will
be 25% charged. Several logins will not increase the charged portion.
Video: The second dimension is the video lecture. When a student interacts
with the MOOC video lectures and completes a number of predefined
events the battery is charged a bit further.
Quiz: The battery will be filled with one extra portion when a student does
a quiz. As previously described, iMooX MOOC-Platform allows each stu-
dent to try the every weekly quiz up to five times. However, just one trial
would be enough to indicate that the student is active. Identical to the pre-
vious dimensions, several attempts will not increase the battery’s charged
portion.
Forum: The analysis in sections 4.1 and 4.2, showed the relation of discus-
sion forums and student activity. Being engaged in the forums either by
writing or reading threads will increase the battery charging portion.
6 Discussion and Conclusion
Massive Open Online Courses (MOOCs) are a new trend in the domain of Tech-
nology-Enhanced Learning. Higher Education institutions have come under pres-
sure to adopt an accessible and open educational environment. MOOCs provide
such an opportunity, yet, there are issues regarding drop-out rate, engagement with
MOOC elements, the interaction between students and instructors as well as moti-
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vation. On the other hand, Learning Analytics offers techniques and tools to predict
and intervene to enhance both the learning and the educational environment.
In this research study, we utilized analysis techniques on students’ data in order to
investigate the hypothesis of the relation between students’ activities and retention
in MOOCs. We found that certified students, who got certificates at the end of the
course, participated in more activities than the non-certified students. Certified
students engaged more in discussion forums; they viewed more forum posts and
wrote more frequently than non-certified students. Additionally, they often inter-
acted more with video lectures. In fact, the seven figures in section 4 show that the
active users demonstrated higher activity during the MOOC weeks. As a result of
that, we became quite certain of the hypothesis that the more activities are done,
the more likely the students are to complete the MOOC.
Based on the validation of this hypothesis, we proposed an Activity-Motivation
model with the aid of Learning Analytics techniques and a gamification element.
The framework was built on the previous analysis results in this study using the
inheritance of MOOC indicators. The proposed framework can be extended with
extra MOOC indicators and is easy to implement and adopt in similar MOOC plat-
forms. While we agree that the didactical approaches and the intrinsic factors of
MOOCs can affect students’ motivation, we also strongly believe in the need to
develop such a model to stir students’ motivation of competency.
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Authors
Mohammad KHALIL  Graz University of Technology, Educa-
tional Technology  Münzgrabenstraße 35a, A-8010 Graz
http://mohdkhalil.wordpress.com
mohammad.khalil@tugraz.at
Adjunct Prof. PhD. Martin EBNER  Graz University of Technol-
ogy, Educational Technology  Münzgrabenstraße 35a,
A-8010 Graz
http://martinebner.at, http://elearningblog.tugraz.at,
http://elearning.tugraz.at
martin.ebner@tugraz.at
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    MOOCs as granular systems: design patterns to foster participant activity MOOCs often suffer from high drop-out and low completion rates. At the beginning of the course, the audience is indeed " massive " ; thousands of people wait for the course to begin, but in the end only a low number of participants stay active and complete the course. This paper answers the research question " Is there a specific point during an xMOOC where learners decide to drop out of the course or to become lurkers? " by identifying MOOCs as a challenging learning setting with a " drop-out problem " and a decrease in participant activity after the fourth to fifth course week. These are the first results of a Learning Analytics view on participant activity within three Austrian MOOCs. This " drop-out point " led the paper to introduce a design pattern or strategy to overcome the " drop-out point " : " Think granular! " can be seen as an instructional design claim for MOOCs in order to keep participant activity and motivation high, and that results in three design patterns: four-week MOOCs, granular certificates and suspense peak narratives. 1. MOOCs: a challenging learning setting with a drop-out problem? The MOOC phenomenon was born in Canada in 2008 and has since then become a worldwide movement (Hay-Jew 2015, 614; Hollands & Tirthali 2014, 25f.; Jasnani 2013). MOOCs can be seen as an expression for a modern orientation towards learning as learning can no longer be seen as a formal act that depends only on universities, schools and other institutions within a formal education system. Learning has to be seen as a lifelong process that has become flexible and seamless, as Wong (2012) and Hay-Jew (2015) resume. It encompasses formal and informal learning and physical and digital (learning) worlds (Wong & Looi 2011; Wong 2012). MOOCs – in our short research study, mainly xMOOCs – are open (Rodriguez 2013) and conducted online, with only an internet connection and registration on an xMOOC platform. The American providers Coursera (www.coursera. org), edX (www.edx.org), the German platforms iversity (www. iversity.org) and MOOIN (www.mooin.oncampus.de) or the Austrian iMooX (www.imoox.at), for example, are necessary for attending courses from different fields. Therefore, the audience is very heterogeneous and cannot be predicted in advance, as it can be for traditional learning settings. It can nevertheless be stated that " the majority of MOOC participants are already well-educated with at least a B.A. degree " (Hollands & Tirthali 2014, 42). They have a certain experience within the learning or the educational context (Gaebel 2014, 25). There are almost no limitations regarding location, age, sex and education, to name a few variables. Thus, MOOC design has to respect this unpredictable heterogeneity, which results in a balancing act between multicity and unity regarding, for example, resources and prior knowledge or further information. As a consequence, MOOCs need to have a special instructional design (Jasnani 2013; Kopp & Lackner 2014) that focuses on different framework conditions. Jasnani (2013, 7) thus mentions a " lack of professional instructional design for MOOCs " which can be cited as one of the reasons for the low completion rates MOOCs suffer from. If we assume " an average 50,000 enrollments in MOOCs, with the typical completion rate of below 10%, approximately 7.5%, that amounts to 3,700 completions per 50,000 enrollments " (Ibid., 6) or even less: " Completion rates for courses offered by our interviewees ranged from around 3% to 15% of all enrollees. " (Hollands & Tirthali 2014, 42) Several investigations (Khalil & Ebner 2014) have already been conducted to identify reasons for