Conference PaperPDF Available

Abstract

Massive Open Online Courses (MOOCs) have been a hype in technology enhanced learning systems the last couple of years. The promises behind MOOCs stand on delivering free and open education to the public, as well as training a large criterion of students. However, MOOCs clashes severely with students dropout which by then forced educationalists to deeply think of MOOCs effectivity from all angles. As a result, the authors of this paper propose a pedagogical idea that strongly depends on injecting the online learning (MOOC) with face-to-face sessions to refresh the students minds as well as integrating them in the real learning process. The authors after that analyze the results of their experiment using Learning Analytics. The outcomes have shown a new record of certification ratio (35.4%), an improvement of student interaction in the MOOC platform, and a manifest in social interaction in the MOOC discussion forum.
How Inverse Blended Learning can Turn Up Learning
with MOOCs?
Martin Ebner1, Mohammad Khalil2*, Sandra Schön3, Christian Gütl1, Birgit Asche-
mann4, Wilfried Frei4, and David Röthler5
1 Graz University of Technology, Graz, Austria
2 Delft University of Technology, Delft, the Netherlands
3 Salzburg Research, Salzburg, Austria
4 CONEDU, Graz, Austria
5 Werdedigital.at, Austria
Martin.ebner@tugraz.at
Abstract. Massive Open Online Courses (MOOCs) have been a hype in tech-
nology enhanced learning systems the last couple of years. The promises behind
MOOCs stand on delivering free and open education to the public, as well as
training a large criterion of students. However, MOOCs clashes severely with
students dropout which by then forced educationalists to deeply think of
MOOCs effectivity from all angles. As a result, the authors of this paper pro-
pose a pedagogical idea that strongly depends on injecting the online learning
(MOOC) with face-to-face sessions to refresh the students minds as well as in-
tegrating them in the real learning process. The authors after that analyze the re-
sults of their experiment using Learning Analytics. The outcomes have shown a
new record of certification ratio (35.4%), an improvement of student interaction
in the MOOC platform, and a manifest in social interaction in the MOOC dis-
cussion forum.
Keywords: MOOC, Learning Analytics, iMooX, Inverse Blended Learning
1 Introduction
Massive Open Online Courses, shortly MOOCs, are still on the move and on top in
any discussions in the research area of Technology Enhanced Learning. Nevertheless,
we can look back now for more than 8 years now, when George Siemens and Stephen
Downes started their first trials on open global online courses [1], [2]. Just a couple of
months later very famous universities like Stanford, Harvard or MIT attracted thou-
sands of learners all over the world with their MOOCs too [3] and 2012 we celebrated
the “Year of the MOOC” [4]. But there was a change within this time frame concern-
ing the didactical approach in behind. Siemens and Downes started those initiatives to
open education and connect people (learners) through discussion about learning re-
* Author’s research is supported by the Leiden-Delft-Erasmus Centre for Education and Learn-
ing
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Antigua Guatemala, Guatemala, November 16-17, 2017.
21
sources. They strongly based their concept on their learning theory connectivism [5].
Consequently, those kinds of MOOCs are called cMOOCs. In contrast, the today’s
biggest MOOC-platforms, like Udacity, Coursera, edX follow the success story of
Sebastian Thrun and deliver video-based online courses with a very strict course
framework, shortly named as xMOOCs [6]. Similarly, the Austrian MOOC-platform
(started in 2013), called iMooX, intended to bring open online courses to the German-
speaking public [7]. iMooX strongly follows the concept of xMOOCs, where video-
based and self-regulated learning are being the main theories in behind [7]. Addition-
ally the content itself is provided as Open Education Resource [8] and a Learning-
Analytics-Framework supported the analyses of learners’ data [9]. Since 2013 to now,
more than 40 MOOCs have been offered to the public with more than 18,000 learners
[10]. Two of the most successful MOOCs are following a new didactical approach
called Inverse-Blended-Learning [11].
In this publication, we address the research question what can we learn from ana-
lyzing learners’ data of a MOOC that is supported by the didactical approach of In-
verse Blended Learning?
This paper is organized as follows: Section 2 shows the research design of the In-
verse Blended Learning. Section 3 describes the structure of the studied MOOC,
while section 4 reveals how did we collect, analyze and interpret students data using
the iMooX Learning Analytics software. Finally, section 5 discusses and concludes
the findings.
2 Research Design
The research design of this article follows the case study design concept. We imple-
mented a MOOC, which is based on the concept of Inverse Blended Learning and ran
it for 6 weeks. Overall, we can summarize four different phases (lasting more than
one year) and are pointed out as the following:
Concept phase (I): The concept of the MOOC as well as marketing strate-
gies and first trials have been done.
Preparing phase (II): The content of the MOOC was prepared and first in-
structions to face-to-face trainers were given.
Execution phase (III): The MOOC becomes online and is being launched.
Completion phase (IV): Final work was done (e. g. emails to learners who
didn’t get it in time) as well as final analyses of the evaluation
To measure the results respectively and answer the scientific questions concerning the
concept of Inverse Blended Learning, different kind of data collection was carried
out. There was an initial questionnaire at the beginning of the course, and a final eval-
uation form to all successful learners, interviews with trainers and learners as well as
tracking learners’ data on the iMooX server.
Tracking students on the Austrian MOOC platform has been done to help decision
makers like researchers and instructors to make proper interpretation of students bulk
data. The Learning Analytics tool was developed based on four stages [9]. In brief,
the first stage includes the data generation, and this is assuredly done by the students
Proceedings of the International Conference MOOC-MAKER 2017.
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22
who produce huge clickstreams on the platform. The second stage is the data collec-
tion. A dedicated web server was enabled to track students and collect their left traces
such as their login information, quiz inputs, downloads,…etc. Following that, the
third stage involves processing the data and tidying them up from any noisy and un-
wanted stacks. Finally, the fourth stage is the interpretation and the optimization
phase. Usually, the last stage could be translated into interventions, decisions,
amendments in the instructional design or a set of recommendation for future offered
courses.
3 Studied MOOC (EBmooc)
The offered course was called “Erwachsenenbildungs-MOOC” (short EBmooc);
translation: adult education professionals MOOC) that addressed adult learning pro-
fessionals as a main target group in the German speaking area. The content of the
course was about digital tools, which can assist adults through their daily business or
their daily life. The MOOC started on the 6th of March 2017 and lasted for six weeks.
In details, the MOOC was offered with videos and some additional learning resources
on the following topics:
Week 1: Learning with MOOCs
Week 2: Digital Tools for daily work
Week 3: Social media in adult education
Week 4: Blended Learning and Technology Enhanced Learning
Week 5: Open Educational Resources
Week 6: Online consulting
In addition, four webinars were given to deepen the learning contents and answering
questions.
As mentioned before, the course followed the Inverse Blended Learning concept. This
concept reverses the original concept of Blended Learning, where face-to-face educa-
tion is interrupted by online elements up to 30-50% of the course duration. Typically,
a course starts with face-to-face meetings, followed by online elements, face-to-face
elements etc. Inverse Blended Learning turns this idea completely, by bringing a
complete online course back to face-to-face situation. Therefore online elements are
interrupted by real life situations or local regularly training supporting the online
course (see Fig. 1). Similar to Christensen et al. [12] Inverse Blended Learning can
be seen as one explicit scenario within their Blended Learning Matrix.
Proceedings of the International Conference MOOC-MAKER 2017.
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23
Fig. 1 Inverse Blended Learning
The summary of our experiment is as the following: more than 40 trainers support-
ed the MOOC with local trainings. In other words, learners had the chance to meet
other learners in those offered groups for discussions or information exchange. On
average, local groups were offered on a weekly basis and most of them were located
in typical adult education training centers spread over the German speaking area. At
some places, learners had to pay a small fee and there was one completely online
group, too. The number of participants varied from 2-12 learners. The corresponding
trainers got an additional train-the-trainer package for about 2 months before the
launch of the MOOC. An introduction to the concept of Inverse-Blended-Learning
was given about the content of the provided MOOC. The trainers got the possibility to
access the MOOC one month earlier so that they could start their own preparations.
4 Analysis
In this section, we provide details and analysis of the studied MOOC. In order to do
so, we have enabled the iMooX Learning Analytics software to track students over
the MOOC platform. By following the four stages in section 2 of this study, we pro-
vide outcomes. Starting up from the launch of the EBmooc till the last offered day,
the Learning Analytics tool has provided us with over 200,000 data records of student
logs. We have cleaned the records from any unnecessary data bulks, since the data in
the log files was unstructured, duplicated and not regularly formatted. We processed
the information after that using the R software (https://www.r-project.org), version
3.40.
The Learning Analytics tool translates low-level collected data into useful infor-
mation. The tool follows vital stages to prepare the data for analysis. 1. The first stage
incorporates when the data are generated by the students. The records are then saved
on a web-server for further analysis in a later step. 2. The second stage involves filter-
ing the stored scripts in the web server and thereafter distilling main activities such as
the number of tried quizzes, the number of discussion forum reads and posts as well
as the total number of logins. 3. The third stage incorporates storing the filtered data
in another protected server for the analysis stage.
After having the data prepared, it was one of the first steps to look at the general
description of the studied MOOC participants (see Table 1). In the initial analysis of
counting the enrolment types, we defined four types of students:
1. Registrants: The total number of registration in the MOOC.
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2. Active: and those are the students who were more involved in the MOOC, we
defined this cohort members by those who log in, did some activities like at-
tending a quiz, making a couple of forum reads or writing a post/comment.
3. Passive: and those are the student who just signed up in the MOOC and did
very minimal activities like a single login.
4. Certified: the students who successfully completed all the requirements and
passed the quizzes of the MOOC and applied for a certificate at the end.
Table 1. EBmooc-participants summary
Type
Total (Percentage)
Registrants
3,064
Active
2,247 (73.33%)
Passive
817 (26.66%)
Certified (type I/type II)
1,083 (35.35% / 48.20%)
The summary in Table 1 showed that there were 3,064 registrants in the MOOC. The
number of inactive students was 817 students. However, the active students were
2,247. The students who were handed certificates were 1,083 students, which mean a
ratio of 35.35% of the total registrants. Whilst if we took into consideration our spe-
cial typecast, the certification ratio is 48.20% with reference to the total number of
active students in the EBmooc.
Next, we tried to sum up the total number of the available activities and interaction
that have been mined by the Learning Analytics software in the MOOC. Table 2 de-
picts four types of MOOC interactions. The values in the table below are shown based
on 95% of the data. Given that, we tried to avoid interruptions and negative impact by
the outliers.
The first activity that we tracked is the logins, which indicate the log in frequency
that each user has done during the active days of the MOOC. In our case study, the
total number of logins is 18,812 with an average of 7.17 per student and the standard
deviation is (σ=6.95).
Table 2. EBmooc activity and interaction summary
MOOC Activity
Total
SD
logins
18,812
6.95
forums reads
66,661
79.06
forums posts
885
9.97
quiz attempts
35,825
12.74
The second activity that was mined is the discussion forum events. What has been
interesting is the extreme number of forum scans (reads) that exceeded any prior pro-
vided MOOC in iMooX. Arithmetically, there have been 66,661 discussion forum
reads done by the students. The mean value for this activity is 29.21 and a noticeable
high standard deviation of (σ=79.06). On the other side, we looked at the discussion
forum comments and posts that were done by the students. Table 2 shows that there
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has been 885 forum posts. These posts can take the format of comments, replies, and
new threads. The average number of forums posts is 4.58 while the standard deviation
is (σ=9.97).
Last but not least, we investigated the performed quiz attempts by all the regis-
trants in the MOOC. With over 35,000 quiz attempts, the students showed a vast level
of fluctuation in dealing with the quizzes. With an average value of 17.32 quiz trials
per student and a standard deviation of (σ=12.74), we move to our next stage of anal-
ysis.
Provided that our reference for success in this MOOC is the students who are certi-
fied, it was wise to examine the certified students interaction sequences. As a result,
we filtered our data upon this request and present the following table (see Table 3).
Table 3. EBmooc activity and interaction summary for certified students
Activity
Min.
1st Qu.
Median
Mean
3rd
Qu.
Max.
SD
Login
2.00
8.00
11.00
12.31
15.00
77.00
6.602
F. reads
1.00
14.00
23.00
43.49
42.00
1,643
95.102
F. posts
1.00
1.00
2.00
5.12
4.00
90.00
11.592
Quizzes
5.00
18.00
26.00
25.96
34.00
47.00
10.098
Certified students activity and interaction summary are provided in Table 3 showing
the minimum value, first quartile, median, mean, third quartile, the maximum value
and the standard deviation. The first quartile can be defined as the middle number
between the minimum value and the median in the data set. The third quartile is the
middle point between the median and the maximum value.
In the first place, the login facts display that the average number of logins of the
certified students during the six weeks was of 12.31, a median of 11 and σ=6.602. We
also noticed some quiz miners in the MOOC similar to several publications provided
before [7,9]. This can be tracked by those who have less than the minimum expected
logins (6 times, 1 per week). Next, the forum reads demonstrated a quite high number
of activities the certified students performed during this course. The table shows that
the average number of reads was 43.49 per student. On the other hand, there were
very active social participants such as those who read over 1,000 reads. The standard
deviation of 95.102 is quite high because of the high value of some students who read
intensively. On the other way around, the discussion forum posts show a minimum
thread input of 1 post, a maximum of 90, a mean of 5.12 and a standard deviation of
(σ=11.592).
The quizzes part displays that the certified students did an average of 25.96 quiz at-
tempts during the six weeks long of the MOOC. The maximum was 47, the median
was close to the mean, while the standard deviation was (σ=10.09).
Provided that social interactivity has been fundamental in this MOOC (see section
2), we have used the log data to analyze the forum reads and posts on each week for
all the enrolled students (see Fig. 2 and Fig. 3).
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Fig. 2 The number of forum reads during the six weeks of the offered MOOC
Fig. 3 The number of posts during the six weeks of the offered MOOC. The orange square
identifies the mentioned period.
The social interactivity in the EBmooc holds great potential and was seen as a key
difference in comparison to previous offered MOOCs on the iMooX platform. Given
that most social interactivity like reading in the forums usually is dropped up to the
half in the mid-range of offered MOOCs, the EBmooc reading was unalike. As seen
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in Fig.2, There have been over 8000 reads in the forum in the first week, followed by
increased reads of around 8,800 reads in the second week. The third week holds a
stable frequency of reads similar to the first week. Nevertheless, the fifth week rec-
orded the lowest number of reads dropping from 8,000 to 4,300 reads. While the gen-
eral direction is declining, the last week of the MOOC recorded the largest number of
reads. The sixth week has over 9000 numbers of reads.
Fig.3 depicts the number of posts in the EBmooc digital forum. We noticed that
there were two peak points in the figure. These peaks are in the first and in the second
week, respectively. The first peak point was on the 15th.March.2017 with 100 posts,
and the second peak point was on the 20th.March.2017 with 70 posts.
5 Discussion
This publication has introduced the Inverse Blended Learning concept in short. Un-
like normal xMOOCs, the new concept of this research has been carried out on one
MOOC offered on the Austrian MOOC Platform (iMooX) with great potential be-
hind. Inverse Blended Learning focuses on integrating online learners into live face-
to-face sessions like local round tables, central meetings, webinars, and a high level of
preparation such as advertisement, training, and distributing printed materials. The
secret outcome word is (“uniqueness”). This experiment was unique with several
results such as the high level of online interaction, high certification rate, and a re-
markable ratio of active students. By using Learning Analytics and tracking the suc-
cessful (certified) students, we were able to identify an action sequence. Remarkably,
certified students logged in, in average, 12 times, read 43 times in the forums and
posted 5 times. This indicates that online learners impelled by the Inverse Blended
Learning concept has driven students to stay active during the weekly online sessions.
The impact of the Inverse Blended Learning was relevant based on our observations
in Fig.2 and Fig.3:
- Fig. 2 and Fig. 3 pointed clearly out that the concept of Inverse Blended
Learning induced a very high commitment by the participants. Based on the
regularly offline meetings, learners are motivated to do their tasks and they al-
so liked to discuss their experiences with peers and trainers.
- Fig. 3 displays the high number of posts even in the second week. This was the
week of the “digital tools for adult education”. The participants’ learners were
able to try them (digital tools, …etc.) on themselves. Many learners expressed
their willingness of using different applications in the posts.
- The concept of Inverse Blended Learning was following the idea to enhance
social engagement duo to learning is highly social process. It seems that the
combination of off- and online was a perfect mix.
- On the other side, even the trainers reported back that learners were motivated
and engaged in their trainings, deep discussions on different experiences often
took longer than the planned weekly hours.
Proceedings of the International Conference MOOC-MAKER 2017.
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- A very interesting fact is also that the offline trainings differed arbitrarily. One
training even happened online in a webinar room.
6 Conclusion
The concept of Inverse Blended Learning worked with high success. Not only the
certification ratio was significant higher, many learners expressed their satisfaction
and showed great enthusiasm to attend another MOOC in the near future. The learn-
ing experiences have led to even receiving many emails from those who did not finish
the MOOC. They expressed that they would love to do the course in the next run
more intensively. Nevertheless we have to point out that, of course, the preparation
effort is very time consuming, but it seems to be worth it!
7 Limitation
The Inverse Blended Learning can be limited with a large number of students in-
volved. Given that even splitting the participants into multiple cohorts may provide a
temporal solution for organizing this teaching methodology, the cost of workshops
and training is considered as one barrier. Furthermore organizational efforts increas-
ing arbitrarily.
Acknowledgement
We express our gratitude to the Austrian Federal Ministry of Education for the
support and funding of the MOOC project.
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Usually, it is the university teachers who develop educational resources for their students. The development procedure presented in this article differs significantly from common processes: Students aged about 15 years (9th grade) were actively involved in the production of videos for a language learning MOOC, which is primarily aimed at university students. The article pursues the question of how and with what effects students were involved in the video production for a language MOOC. We systematically describe the background and processes of the development of the MOOC “Tenses Explained” and the final result. The paper gives insights into the processes and activities of more than 600 participants so far through data from the MOOC platform MooX.at. In addition, the aim is to share the experience in the form of insights in processes as well lessons learned.
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Didaktische Fragestellungen waren bei der Umstellung der Lehre in der COVID-19-Krise auch an der TU Graz zunächst zweitrangig, als es im März 2020 darum ging, vom Präsenzunterricht in den Online-Modus zu wechseln. Im Beitrag wird die Arbeit des Teams „Lehr- und Lerntechnologien“ der TU Graz vor und während der ersten Wochen der COVID-19-Krise vorgestellt. Zu den Angeboten zur Förderung innovativer Lern- und Lehrsettings gehört auch der „ReDesign-Canvas“. Im Beitrag wird beschrieben, wie er beim didaktischen Re-Design in einem konkreten Fallbeispiel eingesetzt wurde. Der Beitrag schließt mit einem Ausblick über die geänderten Voraussetzungen und Strategien für die technologiegestützte Lehre in den kommenden Monaten.
... An inverse blended learning course requires learners to complete online elements before meeting face-to-face ( Figure 1). 4,5 Published reports of inverse blended learning, 4,5 the reverse of blended learning, are rare as it is a relatively new approach. These reports use a teacher-centric didactic approach in Massive Open Online Courses (MOOCs), which are typically targeted toward adult learners and were not implemented in a science course. ...
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Mit der Abkürzung MOOC für „Massive Open Online Courses“ werden Online-Kurse bezeichnet, die mehr als 150 TeilnehmerInnen erreichen. Diese Lern- und Darbietungsform von Inhalten für sehr viele Personen, auch aus unterschiedlichen Einrichtungen und persönlichen Situationen, ist auch für die Erwachsenen- und Weiterbildung interessant. Auf der österreichischen MOOC-Plattform iMooX.at werden seit 2014 MOOCs mit offen lizenzierten Bildungsmaterialien von Hochschullehrenden angeboten, die ohne weitere Zugangsvoraussetzungen (wie z. B. Hochschulreife) genutzt werden können. Einige der Online-Kurse sind explizit der Erwachsenen- und Weiterbildung zuzuordnen und werden von EB-Einrichtungen (mit-)veranstaltet. Die Verantwortlichen für diese MOOCs mit insgesamt etwa 21.000 registrierten TeilnehmerInnen und mehreren Auszeichnungen tragen in dieser Veröffentlichung ihre Erfahrungen zusammen: Wo liegen Potenziale von MOOCs? Wo gibt es Schwierigkeiten und Herausforderungen? Welche Empfehlungen gibt es für NachahmerInnen? Dieser Beitrag ist somit ein kondensierter Blick auf den aktuellen MOOC-Einsatz in der Erwachsenenbildung und unter der Voraussetzung der Nutzung offen lizenzierter Bildungsmaterialien.
Conference Paper
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Initially, in March 2020, when COVID-19 forced conventional face-to-face to pure online teaching, didactic matters were only of secondary importance. At Graz University of Technology (TU Graz, Austria) both the support team for Educational Technology as well as the instructors were mostly concerned with solving technical challenges. Nevertheless, a special tool, the ReDesign Canvas, was available to support lecturers in their endeavor to also address the didactic aspects of their teaching systematically. The article presents the application of the canvas in a redesign of an exemplary lecture.
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Einreichung. Erschienen und zugänglich (CC BY-SA) als: Schön, Sandra & Ebner, Martin (2020). MOOCs planen und gestalten für Einsteiger*innen - mit Monstern und Canvas. Beitrag im Hochschulforum Digitalisierung vom 4. Dezember 2020, URL: https://hochschulforumdigitalisierung.de/de/blog/moocs-planen-und-gestalten-fuer-einsteigerinnen
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Martin Ebner & Sandra Schön (2020). Teacher Training at Universities with “Inverse Blended MOOC”. Presentation at the Online Thematic Seminar, European Schoolnet Academy. 1rst of December 2020, online
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In this chapter, we discuss why open educational resources (OER) and MOOCs are a necessary and powerful combination, especially in German-speaking Europe. We begin with an introduction to open online courses and an overview of copyright law in Germany and Austria. We then describe the evolution of OER MOOCs in Austria and Germany, especially the development of two MOOC platforms. Finally, we present examples of the impact of OER on MOOCs to conclude that an approach combining OER and MOOCs can be very valuable to foster new and innovative didactical approaches as well as future education.
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Massive open online courses (MOOCs) are the road that led to a revolution and a new era of learning environments. Educational institutions have come under pressure to adopt new models that assure openness in their education distribution. Nonetheless, there is still altercation about the pedagogical approach and the absolute information delivery to the students. On the other side with the use of Learning Analytics, powerful tools become available which mainly aim to enhance learning and improve learners’ performance. In this chapter, the development phases of a Learning Analytics prototype and the experiment of integrating it into a MOOC platform, called iMooX will be presented. This chapter explores how MOOC stakeholders may benefit from Learning Analytics as well as it reports an exploratory analysis of some of the offered courses and demonstrates use cases as a typical evaluation of this prototype in order to discover hidden patterns, overture future proper decisions, and to optimize learning with applicable and convenient interventions.
Article
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MOOCs (Massive Open Online Courses) are the educational buzzword of 2012. Media frenzy surrounds them and commercial interests have moved in. Sober analysis is overwhelmed by apocalyptic predictions that ignore the history of earlier educational technology fads. The paper describes the short history of MOOCs and sets them in the wider context of the evolution of educational technology and open/distance learning. While the hype about MOOCs presaging a revolution in higher education has focussed on their scale, the real revolution is that universities with scarcity at the heart of their business models are embracing openness. We explore the paradoxes that permeate the MOOCs movement and explode some myths enlisted in its support. The competition inherent in the gadarene rush to offer MOOCs will create a sea change by obliging participating institutions to revisit their missions and focus on teaching quality and students as never before. It could also create a welcome deflationary trend in the costs of higher education. Explanatory Note During my time as a Fellow at the Korea National Open University (KNOU) in September 2012 media and web coverage of Massive Open Online Courses (MOOCs) was intense. Since one of the requirements of the fellowship was a research paper, exploring the phenomenon of MOOCs seemed an appropriate topic. This essay had to be submitted to KNOU on 25 September 2012 but the MOOCs story is still evolving rapidly. I shall continue to follow it. 'What is new is not true, and what is true is not new'. Hans Eysenck on Freudianism This paper is published by JIME following its first release as a paper produced as part of a fellowship at the Korea National Open University (KNOU). Both the original and this republication are available non-exclusively under Creative Commons Attribution (CC-BY). Apart from this note and minor editorial adjustments the paper is unchanged. Normal 0 false false false EN-GB X-NONE X-NONE
Conference Paper
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Unter der Abkürzung MOOC werden Online-Kurse verstanden, die sich poten- tiell an viele hundert TeilnehmerInnen richten. Auch der Kurs „Gratis Online Lernen“ ist auf den ersten Blick ein reines Online-Angebot. Da es sich bei der Zielgruppe um EinsteigerInnen beim Lernen handelt, wurde jedoch besonderer Wert darauf gelegt, den Kurs mit der Lebenswelt der TeilnehmerInnen zu ver- knüpfen. Die entsprechenden Aktivitäten des so bezeichneten „Inverse-Blended- Learning-Konzeptes“ führten zu einer beträchtlich hohen Abschlussquote: Von den bis Dezember 2014 849 registrierten TeilnehmerInnen wurden 383 mindes- tens einmal im Kurs aktiv, und von diesen wiederum haben 115 (30%) den Kurs erfolgreich abgeschlossen.
Article
Massive open online courses are the educational happening of the moment. Everyone wants in. No one is quite sure what they’re getting into.
Online, Bigger Classes May Be Better Classes. The Chronicle of Higher Education
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