masterthesis-nico-final-digital

master’s thesis
R A I S I N G C O M P L E T I O N R AT E S I N X M O O C S
T H R O U G H S O C I A L E N G A G E M E N T
S T E I G E R N D E R A B S C H L U S S Q U O T E N I N X M O O C S
D U R C H S O Z I A L E I N T E R A K T I O N
nicolas s. fricke
nicolas.fricke@student.hpi.uni-potsdam.de
submitted on: May 30th, 2014
chair
Internet Technologies and Systems
supervisors
Prof. Dr. Christoph Meinel
Dipl-Inf. (FH) Jan Renz
Thomas Staubitz, M. Sc.
Dipl-Inf. Christian Willems

Nicolas S. Fricke: Raising Completion Rates in xMOOCs through Social
Engagement, Master’s Thesis, © May 30th, 2014

A B S T R A C T
Massive Open Online Courses (MOOCs) enable thousands of stu-
dents around the world to participate in university-like courses for
free. Yet, many students never complete the courses they enrolled
for, high dropout rates can be found on all major MOOC platforms.
These online courses are structured like traditional university courses,
but by bringing them online, the personal connection between course
providers and learners as well as between students is getting lost.
This thesis will present findings from motivational and educational
sciences and discuss their relevance for MOOCs. Concepts on how
personalized feedback messages can be provided automatically and
how both, learners and course providers, can benefit from students
actively contributing content are being presented. Their implementa-
tion into the distributed architecture of the openHPI MOOC platform
is being discussed. These concepts help to elevate the intrinsic moti-
vation of students, which is the cornerstone for long lasting interest
and engagement into a course’s subject.
Z U S A M M E N FA S S U N G
Massive Open Online Courses (MOOCs) ermöglichen es vielen tau-
senden Menschen weltweit kostenlos an Kursen auf Universitätsni-
veau teilzunehmen. Jedoch schließen viele Studenten die belegten
Kurse nicht erfolgreich ab, hohe Abbrecherquoten sind auf allen gro-
ßen MOOC Plattformen zu verzeichnen. Diese Online-Kurse sind wie
Universitätskurse strukturiert, durch die Übermittlung durch das In-
ternet gehen jedoch die persönlichen Beziehungen sowohl zwischen
Lehrenden und Lernenden wie auch zwischen den Studierenden ver-
loren. Diese Masterarbeit wird Ergebnisse aus Motivationstheorien
sowie der Pädagogik präsentieren und deren Relevanz für MOOCs
diskutieren. Konzepte, wie personalisierte Feedback-Nachrichten au-
tomatisch bereitgestellt werden und wie sowohl Kursanbieter wie
auch Nutzer vom aktiven Mitwirken der Lernenden profitieren kön-
nen, werden präsentiert. Die Implementierung dieser Konzepte in die
verteilte Architektur der openHPI MOOC Plattform wird diskutiert.
Das Ziel ist die intrinsische Motivation der Studierenden zu erhöhen,
um den Grundstein für langanhaltendes Interesse und Engagement
bezüglich eines Kursthemas zu legen.
iii

A C K N O W L E D G M E N T S
I want to use this opportunity to express my special thanks and ap-
preciation towards my supervisors. Your steady guidance and contin-
uous feedback have been of tremendous value.
Also, I would like to thank all my colleagues at openHPI in both
development and research for their constant help and inspiration.
And, last not least, I probably wouldn’t have made it this far without
the never ending support of my loved ones. Thanks to my friends
and family who were there for me throughout all these months – and
who now and then gave me a reason to leave the ofﬁce.
Especially I want to express my gratitude to Anita Dieckhoff who en-
couraged me every single day to continue. During these sometimes
distressful times you were the greatest support that I can imagine.
Thank you!
iv

C O N T E N T S
1 introduction 1
2 online learning and moocs 4
2.1 Brief History of E-Learning . . . . . . . . . . . . . . . . 4
2.2 Massive Open Online Course Platforms . . . . . . . . . 6
2.3 openHPI as a MOOC Platform . . . . . . . . . . . . . . 7
2.3.1 New Architecture for openHPI . . . . . . . . . . 9
2.3.2 Ongoing Research on openHPI . . . . . . . . . . 10
2.4 Challenges for MOOC Platforms . . . . . . . . . . . . . 11
3 user motivation 14
3.1 Intrinsic and Extrinsic Motivation . . . . . . . . . . . . 14
3.2 Self Determination Theroy . . . . . . . . . . . . . . . . . 16
3.3 Drive Theory . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4 Relatedness, Autonomy, Mastery, and Purpose . . . . . 17
3.5 Gamification . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.6 Classification of Users . . . . . . . . . . . . . . . . . . . 20
3.6.1 Bartle’s Player Types . . . . . . . . . . . . . . . . 20
3.6.2 Marczewski’s User Types . . . . . . . . . . . . . 21
4 motivation and education 25
4.1 The Role of Extrinsic Motivators . . . . . . . . . . . . . 25
4.2 Importance of Feedback . . . . . . . . . . . . . . . . . . 27
4.3 Competition in Classrooms . . . . . . . . . . . . . . . . 30
4.4 Learning Theories . . . . . . . . . . . . . . . . . . . . . . 31
4.4.1 A brief overview . . . . . . . . . . . . . . . . . . 31
4.4.2 Connectivism or the “c” in cMOOC . . . . . . . 32
4.5 Existing Gamification Concept for openHPI . . . . . . . 33
4.5.1 Quiz Points and Gamification Points . . . . . . 34
4.5.2 Badges as Additional Certificates . . . . . . . . . 34
4.5.3 Acknowledgments Make Users Smile . . . . . . 35
4.5.4 A Pinboard Instead of Nested Forums . . . . . . 35
5 concept 38
5.1 Increase Bonding Through Personal Feedback . . . . . 38
5.1.1 Feedback in the Education Sector . . . . . . . . 39
5.1.2 Feedback on Self-Regulation for MOOCs . . . . 42
5.2 Increase Interaction Through Contribution . . . . . . . 47
5.2.1 Contribution, Motivation, and Performance . . 48
5.2.2 How Contribution Can Help Course Providers 49
5.2.3 User Generated Quiz Questions . . . . . . . . . 50
6 implementation 55
6.1 Feedback on Self-Regulation . . . . . . . . . . . . . . . . 55
6.1.1 Evaluating User Performance . . . . . . . . . . . 57
6.1.2 Generating User Feedback . . . . . . . . . . . . . 59
v

contents vi
6.1.3 Presenting Feedback to the Student . . . . . . . 60
6.2 User Contributed Questions . . . . . . . . . . . . . . . . 60
6.2.1 Initial Architecture . . . . . . . . . . . . . . . . . 62
6.2.2 Iteration on Initial Approach . . . . . . . . . . . 63
7 future work 66
8 conclusion 68
i appendix 79
a interviews with teaching assistants 80

A C R O N Y M S
CCK08 Connectivism & Connective Knowledge Course 2008,
the ﬁrst MOOC from 2008 (see chapter 2.2)
HPI Hasso Plattner Institute in Potsdam, Germany
HTTP Hypertext Transfer Protocol
MIT Massachusetts Institute of Technology, USA
MOOC Massive Open Online Course
cMOOC Connectivist Massive Open Online Course, describes a
MOOC based on the connectivist learning theory (see
chapters 2.2 and 4.4.2)
xMOOC Extension Massive Open Online Course, the x is based
on online courses offered at Harvard University, which
are preceded by an x. It describes a MOOC following a
rather typical university lecture style, see chapter 2.2
MUD Multi-User Dungeon (see chapter 3.6.1)
LMS Learning Management System (see chapter 2.1)
OCW OpenCourseWare (see chapter 2.1)
OER Open Educational Resources (see chapter 2.1)
RAMP Relatedness Autonomy Mastery Purpose (see chapter
3.4)
SOA Service Oriented Architecture
SDT Self Determination Theory (see chapter 3.2)
STI Single Table Inheritance, a way of emulating inheritance
in relational databases
UUID Universally Unique Identiﬁer, used as primary keys for
records within openHPI services
WWW World Wide Web
vii

1
I N T R O D U C T I O N
“Education is a human right with immense power to transform.
On its foundation rest the cornerstones of freedom, democracy
and sustainable human development.” — Kofi A. Annan,
1999 [2]1
Since 1948 the “Right to Education” is a human right as a law in Ar-
ticle 26 of the Universal Declaration of Human Rights. As Annan stated,
education is crucial for a developed, free and democratic world.
Making this education available for all mankind is one of the great-
est challenges of the 20th and 21st century. As the world gets more The internet makes
knowledge publicly
available
and more connected, especially since the beginning of the Internet
era, new ways of education have evolved, making knowledge accessi-
ble from everywhere on the globe. Online tutorials, lectures on video
platforms and forums for all kind of different subjects provide a rich
pool of material for self-paced learners.
While these formats are mostly loose collections of more or less
educational content, online schools, such as Khan Academy2, were
created for offering a rather structured syllabus for less autonomous
learners. Online school providers offer learning materials such as
video lectures or reading material which build on one another for
guiding students through the whole comprehensive subject area. This
enables learners to not only find answers to particular questions, but
rather develop whole new skills and embrace new subject areas.
A very recent trend in online education are Massive Open On-
line Courses (MOOCs). In contrast to online schools, these online MOOCs open
university courses
for the public
courses are of a limited duration, usually around six to eight weeks,
with a fixed start and end date. Extension Massive Open Online
Courses (xMOOCs) are a special type of MOOCs, which resemble
traditional university courses and their structure of lectures, assign-
ments and often contain final exams. With fixed deadlines for as-
signments, xMOOCs solve the issue of students being able to pro-
crastinate indefinitely and never completing a course. Also, many
xMOOCs offer participants a certificate which can later be used as
1 From the foreword of “The State of the World’s Children 1999” [2] by Kofi A. Annan,
in his role as seventh Secretary-General of the United Nations
2 Khan Academy website: http://www.khanacademy.org/
1

introduction 2
proof that the user understands the subject. With enrollment numbers
of up to 226,000 students within a single course3 and being available
free of charge, these courses are truly open to the general public.
Participants come from all different backgrounds, university or A great variety of
users interested in
MOOCs
school students, professionals, or job-seekers use the great variety of
existing xMOOCs to independently apprehend new subjects or skills.
Since all enrolled people in these courses are working on more or
less the same content within a certain timeframe, each MOOC forms
a unique community of learners.
But with a great variety of students, the motivations of these par-
ticipants for enrolling in a course are also manifold. Understanding
the learner’s motivation is crucial for being able to successfully refine
education.
challenges for current xmoocs
While the number of students enrolling in these xMOOCs is very Few students
complete a coursehigh, only a small percentage successfully completes the courses [25].
Few, less than 10% of all enrolled students, are actively participating
within a course and more than 87% leave the course prematurely,
most within the first weeks [35].
It has been shown that those students who actively engage in a Engagement
determines successcourse achieve higher final scores and are less likely to discontinue
the course. Yet, the options for engagement on current xMOOC plat-
forms are limited to discussion boards. These boards are only used
by around 10% of all enrolled students [45].
The user types defined by Marczewski [30] help to understand that Forums are not
attractive to all
users
engaging in such discussion forums is not attractive for all types of
learners. Forums fail to attract users who are not as interested in so-
cializing, especially if the motivation for participating in the course is
extrinsic rather than intrinsic, meaning the user’s driving motivation
to participate comes from external factors.
Another problem is the lack of personal feedback within xMOOCs. MOOCs lack
personal feedbackTraditional forms of education, such as public schools, help students
foster a good work morale through providing personalized feedback.
Such gainful feedback is based on the competence and self-regulation
of the particular student. Yet, the all feedback current online educa-
tion offers is only based on the correctness of a completed task [45].
3 See the article “Preliminary results on Duke’s third Coursera effort, ‘Think Again’ ”,
see: http://cit.duke.edu/blog/2013/06/preliminary-results-on-dukes-third-
coursera-effort-think-again/, accessed in March 2014

introduction 3
This master’s thesis presents concepts for actively engaging users
through contribution on MOOC platforms and thereby aims at re-
ducing the number of people losing motivation and dropping out
of courses. Also, the effect of different kinds of feedback on learn-
ers is discussed and an approach on how to integrate personalized
feedback into online courses is presented. Insights into the imple-
mentation of these concepts will be provided.
Chapter 2 will give a brief introduction into MOOCs and present
the openHPI platform which will be used as reference platform in
this work. The already broached problems current xMOOC platform
have to face will be discussed in more detail.
Understanding the driving force of users is vital for building a sys-
tem that meets their needs. Therefore, the motivation of users will be
analyzed in chapter 3. After giving an introduction into different mo-
tivational theories, the concept of gamification will be analyzed. Since
users can have different motives for performing a similar action, clas-
sifications of users will help understanding their individual needs.
How this motivation effects the learning behavior in educational
contexts will be subject of chapter 4. An insight into learning psy-
chology will be provided and the role of motivators as well as feed-
back will be analyzed. Different learning theories will be introduced
and connectivism as the underlying philosophy of Connectivist Mas-
sive Open Online Courses (cMOOCs) will be discussed in more detail.
Furthermore, an existing gamification concept for the openHPI plat-
form will be presented.
Chapter 5 then proposes concepts on how the insights of MOOC
platforms, motivational, and learning theories can be combined for
providing a richer course experience to the enrolled users. A concept
on how personalized feedback can be integrated in a scalable man-
ner will be presented. A second concept will explain how the possi-
bility of users contributing their own quiz questions will achieve to
raise their personal motivation while at the same time help the course
providers.
Implementations of these concepts for the openHPI platform will
be discussed in chapter 6, where an emphasis is put on the Service
Oriented Architecture (SOA) of openHPI. Since these implementa-
tions are not yet deployed in the running openHPI setup, ways to
evaluate their performance will then be discussed in chapter 7, and a
conclusion will be drawn in chapter 8.

2
O N L I N E L E A R N I N G A N D M O O C S
Online education was a subject of interest even before the World Wide
Web (WWW) was started. A brief history of how Massive Open On-
line Courses were born will be given in section 2.1 and section 2.2
will analyze the phenomena “MOOC” in more detail. The MOOC-
Platform of the Hasso Plattner Institute (HPI) in Potsdam, Germany
will be introduced in section 2.3 and section 2.4 will then discuss
existing problems of current MOOCs platforms.
2.1 brief history of e-learning
early beginnings In 1989, the idea of the World Wide Web The WWW
connected the world(WWW) was born as a proposal for a more effective way of shar-
ing academic research results [3]. In the same year, the University
of Phoenix started as one of the ﬁrst universities worldwide with an
online course program1.
learning management systems With the global success of LMSs helped
bringing courses
online
the WWW in the mid-1990s, new forms of knowledge transfer were
created. Learning Management Systems (LMSs) became popular in
19972. They can be used for delivering online courses to students, for
keeping track of their progress as well as performance and for man-
aging enrollments. Typically, LMSs support the structure of conven-
tional university courses consisting of lectures and reading material,
assigned homework, as well as discussions via forums [32]. These
LMSs are typically designed for small numbers of enrolled students
and large numbers of offered courses, which often are not public.
opencourseware With the turn of the millennium, universities OCW made lectures
publicly availablestarted making recordings of their lectures publicly available which
became known as the OpenCourseWare (OCW) movement3. In 2001
1 See article Desktop degrees, University of Phoenix takes education on-line by
Shira Levine: http://connectedplanetonline.com/mag/telecom_desktop_degrees_
university/, accessed in January 2014
2 First LMS by Blackboard Inc, website: http://www.blackboard.com/
3 First OCW project launched in 1999 by the German University of Tübingen
as “timms” – Tübinger Internet Multimedia Server, website: http://timms.uni-
tuebingen.de/archive/sose99.aspx
4

2.1 brief history of e-learning 5
the Massachusetts Institute of Technology (MIT), USA joined the OCW
movement4 and besides publishing videos of lectures, the MIT addi-
tionally started publishing their slides, assignments as well as exams
with solutions online, free of charge and accessible for all. This pro-
cess of making learning resources freely available, called the Open
Educational Resources (OER) movement, soon spread to other edu-
cational institutions. These now freely available resources have since
been used in lectures around the world as well as by many individu-
als for self-studies [24].
In 2004 the HPI joined the OCW movement and launched the tele-
TASK project5 [34]. As of this writing, 394 courses from the HPI
curriculum as well as from different partners with a total of 4951
lectures can be accessed free of charge via the platform.
social networks In the mid 2000s social networks like Facebook6 The “Web 2.0”
changed online
learning
began to gain momentum, which again had an immense impact on
online education. Users got used to online real time communica-
tion, social interaction through online networks became normal. With
the launch of online video platforms like Youtube7 in 2005 anyone
gained the ability to publish videos, which served as a foundation
for projects like Khan Academy8, which was launched in 2006. Khan
Academy was created by Salman Khan to provide “a free world-class
education for anyone anywhere”9.
a new era Almost one decade after the the launch of the OCW A new learning
format: MOOCsmovement, in 2008, a new form of online teaching emerged: The
Connectivism & Connective Knowledge Course 2008 (CCK08) of
George Siemens and Stephen Downes was what Bryan Alexander
and Dave Cormier10 coined as the first Massive Open Online Course
(MOOC). MOOCs are online courses which are published for the
general public, free of charge and admission restrictions.
In 2011 Stanford University professors Sebastian Thrun and Peter
Norvig helped MOOCs to a final breakthrough by offering the course
Introduction to Artificial Intelligence, for which over 140.000 students
signed up [45, 25].
4 MIT OpenCourseWare website: http://ocw.mit.edu/index.htm
5 tele-TASK website: http://www.tele-task.de/
6 Facebook website: https://www.facebook.com, Facebook was founded in 2004
7 Youtube website: https://www.youtube.com
8 Khan Academy website: http://www.khanacademy.org/
9 See the Khan Academy “mission”: https://www.khanacademy.org/about, accessed
in January 2014
10 Dave Cormier, “The CCK08 MOOC: Connectivism course, 1/4 way”:
http://davecormier.com/edblog/2008/10/02/the-cck08-mooc-connectivism-
course-14-way/, accessed in March 2014

2.2 massive open online course platforms 6
2.2 massive open online course platforms
A Massive Open Online Course (MOOC) is a course format aimed at
free and open access via the WWW.
Being based on traditional university courses, MOOCs are facili- MOOCs have up to
several thousand
participants
tated by one or more acknowledged experts on a specific field, con-
sist of a predefined timeline and weekly topics. Yet, MOOCs build
on active community of hundreds to several thousands of enrolled
students per course [33].
Since the breakthrough of the Introduction to Artificial Intelligence Different platforms
and formatscourse by Sebastian Thurn and Peter Norvig, many MOOCs have
launched and different MOOC platforms emerged. These MOOCs
can be classified into two main categories, the so-called cMOOCs and
xMOOCs. After providing a comparison of these two formats, section
2.3 will analyze the openHPI platform as an exemplary xMOOC plat-
form in more detail. Common problems of current MOOC platforms
will be analyzed in section 2.4.
Difference between cMOOCs and xMOOCs
Since the CCK08, the first MOOC in 2008, two main formats for these
online courses have evolved.
cmoocs The idea behind CCK08 by Stephen Downes and George cMOOCs build on
the community for
creating the syllabus
Siemens was to gather a group of interested students around a do-
main of knowledge and follow connectivist (see section 4.4.2) prin-
ciples of learning as a community. Various information sources on
different platforms and in different formats were used for creating
the course contents. While this course had fixed start and end dates
as well as a common learning topic, the curriculum was created by
the learning community while the course was ongoing [45, 54]. This
type of MOOC was later called a connectivist MOOC, or a cMOOC.
xmoocs The initial MOOC concept was then adopted by the major xMOOCs are based
on traditional
university courses
platforms, which entered the market in 2012 (see section 2.3). With
the idea of publishing traditional university courses as MOOCs and
opening them to far more participants, the need for a fixed course
structure became insuperable. For being able to certify the participa-
tion of enrolled students, homework assignments and self tests for
preparation were introduced. Since Harvard and MIT use an “x” to
mark their open courses, this concept was then called xMOOC.
comparison Yuan et al. argue in their white paper “Beyond
MOOCs” that cMOOCs and xMOOCs have a different understand-
ing of the meaning of Massive Open Online Courses [54].

2.3 openhpi as a mooc platform 7
massive: While for xMOOCs this emphasizes scalability of a course,
for cMOOCs the word focuses on having a big learning commu-
nity.
open: Being interpreted by xMOOCs as publicly available, cMOOCs
regard it as having all contents under open licenses.
online: For xMOOCs this focuses individual and self-paced learn-
ing with all content available through the platform, while
cMOOCs think of it as connected and networked learning with
content coming from a multitude of sources.
course: Seen by xMOOCs as the traditional definition of university
courses with a fixed structure, exams and a grade at the end,
for cMOOCs Course means creating and generating own course
content as a community without any assessments.
cMOOCs are therefore better suited for relatively smaller learning cMOOCs are good
for creative subjects,
while xMOOCs
scale better
groups of approximately 1000 active students, and are extremely ef-
fective for acquiring creative matters. Yet, for many classical research
topics like science or mathematics having the matter processed by an
acknowledged expert in the field, with selected material and a prede-
fined course structure is often easier to grasp. While xMOOCs can
be used by a far greater number of students per course run, students
may even receive a certificate at the end of the course. This can, for ex-
ample, be helpful to people who require extended vocational training
for applying for specific jobs [45].
While this thesis focuses on interaction within xMOOCs, chapter
5 will present ideas on how to include parts from the connectevist
counterpart.
2.3 openhpi as a mooc platform
With the enormous success of the the Introduction to Artificial Intelli- 2012 was the year of
the MOOC12– and
its platforms
gence course, several platforms for offering MOOCs have launched.
Sebastian Thurn, who was one of the teachers of the artificial intelli-
gence course, co-founded the for-profit startup Udacity13 in 2012. In
the same year, the startup Coursera14 was founded, and the non-profit
project edX15 was launched by Harvard University and MIT.
While these platforms are all based in the United States, the Ger-
man HPI in Potsdam launched the openHPI platform, which is a non-
profit project for publishing lectures by HPI professors and opening
the HPI courses to the general public.
12 The New York Times – The Year of the MOOC: http://www.nytimes.com/2012/
11/04/education/edlife/massive-open-online-courses-are-multiplying-at-a-
rapid-pace.html, accessed in March 2014
13 Udacity website: https://www.udacity.com/
14 Coursera website: https://www.coursera.org/
15 edX website: https://www.edx.org/

The Web-University team at the chair of Prof. Christoph Meinel The HPI had a good
starting point for
own MOOCs
for Internet-Technologies and Systems at the HPI had already been
researching in the field of online learning for several years. With on-
going development of the tele-Task system16 which allows recording
and publishing of lectures, and with virtual laboratories like the SOA
Security Lab17, the foundation of technologies suitable for a MOOC
platform already existed at the HPI [34].
The new platform was to be used as a basis for further development Requirements for an
own MOOC
platform
as well as research in the field of MOOCs. Since publishing courses
on a hosted platform, such as Coursera, would not satisfy these needs,
the open-source LMS Canvas LMS18 was used and highly adapted for
delivering online courses to a massive community.
On September 3rd, 2012, the first openHPI-lecture In Memory Data
Management, given by Prof. Hasso Plattner, co-founder of SAP and
benefactor and founder of the HPI, started. The lecture was given in
English and more than 16.000 users signed up to participate. More
than 4.000 of these users actively followed from beginning
to end19 [19].
As of this writing, eight courses have been given on openHPI. The
courses were held in either English or German and on average 3.000
students actively followed each course up to the end20.
Courses on the openHPI platform are following a common for- A typical course at
openHPImat [34]. The courses are derived from the HPI IT-Systems Engi-
neering curriculum and are divided into six units, each taking one
week. Each week represents a learning sequence, consisting of video
lectures, reading materials, and quizzes for both, self-assessment and
grading. Weekly homework quizzes offer learners the possibility to
collect points towards the the final score, and are also required for
obtaining a certificate at the end of a course. Other than homework
quizzes, self-assessment quizzes can be taken an indefinite number
of times and are designed to deepen the learner’s understanding.
Each week is accompanied by an actively moderated forum, allowing
learners to exchange ideas on the week’s topics and ask or answer
questions. Also, learners can get together in learning groups. These
groups, called learning rooms, offer a separate, private discussion fo-
rum as well as a wiki for collaborative editing. All courses are open
to the general public and are offered free of costs.
16 teleTASK homepage: http://www.tele-task.de
17 SOA Security Lab: https://www.soa-security-lab.de and more informa-
tion at http://www.hpi.uni-potsdam.de/meinel/security_tech/soa_security_
lab.html, accessed in February 2014
18 Canvas LMS by Instructure Inc. on GitHub: https://github.com/instructure/
canvas-lms
19 Actively following means they submitted all mandatory assignments within the
course. See the problem of dropouts described in chapter 2.4
20 Statistics on openHPI courses: https://blog.openhpi.de/2013/08/statistiken-
zur-abschlussrate/, accessed in February 2014

2.3.1 New Architecture for openHPI
The pleasantly high number of users on the initial openHPI platform Motivation for a
re-developmentsoon brought the application’s performance to a limit. It became
imminent that for being able to satisfy future needs of users as well
as being able to further develop on the platform, the highly adapted
Canvas LMS platform was no longer the best foundation. In 2013 the
development of the new openHPI platform began.
For better scalability, the new platform was to use a Service Ori- The new platform
uses a SOA as
foundation
ented Architecture (SOA) [34]. Therefore, the system was divided
into logical units, like accounts, courses, and the web application. Each
unit is implemented as an independent Ruby on Rails21 service, con-
taining all logic and data for only its own purpose. The units use
RESTful HTTP for communicating with one another.
Web Application
Account Service Course Service Quiz Service Pinboard Service…
Account DB Course DB Quiz DB Pinboard DB
RESTful HTTP
Mobile AppWeb BrowserWeb Browser …
Public WWW
Private openHPI
infrastructure
…
API Application
Figure 1: Abstract model of the Service Oriented Architecture (SOA) of the
new openHPI platform
Only the web and the API applications are accessible from the public WWW.
All logic is supposed to be encapsulated within speciﬁc services, of which
each has its own database. The services use RESTful HTTP for communica-
tion.
As of this writing, the core system is composed of 14 independent An individual
service for each
subdomain of
openHPI
services and the web and API applications. Only the last two are pub-
licly accessible from WWW. These applications handle incoming user
requests and gather the required data from the other services. The
web application is used for rendering the HTML views when user ac-
cess the platform via web browsers. Another way to access openHPI
is via the mobile applications for iOS and Android, which are cur-
rently under development. These mobile applications will use the
API application for communicating with the openHPI platform. These
externally accessible applications store no data whatsoever. All the
21 Rails is a web development framework for the scripting language Ruby, generally
referred to as Ruby on Rails, see: http://rubyonrails.org/

data and business logic is supposed to be encapsulated within the
backend services.
While all services offer the RESTful HTTP as their interface, for Acfs abstracts from
HTTP requestseasier communication the Ruby Gem Acfs22 has been developed to
abstract from raw HTTP requests. Acfs allows each ruby service to
provide a custom client gem which encapsulates HTTP requests to
this service. This client gem can also be equipped with additional
functionality, which allows developers to encapsulate service logic
which then needs to be executed within another service. Other ruby
services which include the client gem can use the classes defined in
the gem along with possible additional functionality for communi-
cation instead of manually sending HTTP requests or parsing JSON
answers.
For asynchronous communication, RabbitMQ23 is used as a mes- Messaging for
asynchronous
communication
saging queue. Any service can publish messages with a custom
payload onto so-called topic exchanges. All services which are reg-
istered to this topic exchange will then asynchronously receive the
message along with its payload. Another custom gem developed by
the openHPI team, Msgr24 again allows for abstraction from the ac-
tual messaging technology used.
The SOA approach of the new openHPI platform allows each ser-
vice to employ technologies, for example specific databases, best
suited for its tasks. Also, scalability of the whole platform improves
since SOA offers the possibility of individually scaling-out each ser-
vice depending on its load.
Moreover , this encapsulated design enables future components The SOA design
makes openHPI
scalable and easily
extensible
being implemented without altering functionality of already existing
components [34]. New components can be developed as individual
services that can then be added to the service environment. The only
existing component which has to be altered for new functionality is
the web service, which handles user requests from and to the new
service as well as the view rendering.
2.3.2 Ongoing Research on openHPI
This easy extensibility allows many research topics in the field of
MOOCs to build onto and therefore extend the core openHPI plat-
form.
In 2013 a master student’s project at the HPI developed a gamifica- Gamification
concept to reduce the
dropout rate
tion concept for reducing the high early dropout rate in courses [52]
(see section 2.4 and chapter 4.5). By including carefully selected game
elements into the platform, the students aimed at drawing the learn-
22 Acfs on Github: https://github.com/jgraichen/acfs
23 RabbitMQ homepage: https://www.rabbitmq.com/
24 Msgr on Github: https://github.com/jgraichen/msgr

2.4 challenges for mooc platforms 11
ers attention into the discussion boards, which then leads to higher
engagement within the courses.
The implementation of a learning analytics service was discussed Learning analytics
as opportunity for
customization
in a master’s thesis by Thomas Klingbeil [26]. This analytics service
logs information of a user interaction and user learning progress on
the platform. It allows arbitrary queries for documenting and ana-
lyzing this learning process of users and can be used for both, un-
derstanding how learners approach the course material as well as
customizing the platform based on a user’s personal preferences.
Another research topic is the integration of social functionalities A social graph for
learning together
with friends
into the learning environment. Sebastian Woinar integrated a social
graph into the openHPI platform, allowing users to add one another
as friends [53]. Friends can then for example see the other’s activi-
ties on the platform. This activity stream is based on group dynam-
ics and therefore motivates individual learners in progressing in the
course [53].
Further research is being done in for example improving the abili-
ties of learning groups, automating the assessment of programming
exercises, or improving the offline-capabilities of openHPI through
modern browser technologies.
2.4 challenges for mooc platforms
Since the first xMOOCs have been launched, some characteristic prob- Only few students
complete a courselems in these courses became obvious. While more than 160,000 stu-
dents were enrolled in the first xMOOC on artificial intelligence, only
20,000 (12.5%) completed the course25. These high dropout rates can
be found within most xMOOCs, the average completion rate is less
than 13% [45, 25]. While the completion rate for openHPI courses with
around 18% is well above average, the remaining high rate of stu-
dent drop outs is worth further investigation. Meinel et al. analyzed
the dropout development during courses on the openHPI platform
and found that the rate of students who discontinue the course is
16.5% after the first week and falls to steady 8% within the following
weeks [35].
missing social interaction In further research on the activ- Success is linked to
engagementity of openHPI participants, Grünewald et al. found that the engage-
ment of a participant, measured by their activity within the forums,
directly correlates with their result in homework assignments [19].
This phenomena is well known in traditional learning environments,
positive interaction with other learners as well as with the teachers
25 See Peter Norvig’s TED talk reflecting on creating and running the online AI
course: http://fm.schmoller.net/2012/07/peter-norvigs-ted-talk-about-the-
ai-course.html, accessed in March 2014

is crucial for a motivated learning situation and active engagement
leads to deeper understanding [13, 5].
In xMOOCs, such social interaction often is limited to the forums, Engagement is
mostly restricted to
forums only few
users actively use
which are either public or sometimes within private learning groups.
Yet, 85% of all participants of the analyzed course did not even once
post in these forums [19], in other xMOOCs this number is even as
high as 90% [45]. Such behavior of passively consuming without
contributing is called “lurking”26. Lurking is by far not limited to
xMOOCs, it was already observed on the first existing online mailing
lists. Fei-ching Chen from the National Central University in Taiwan
argues that the great majority within online communities, over 90%,
consists of such lurkers. Yet, while not engaging actively, Chen found
that lurkers often steadily follow discussions [9]. Cross-referencing
the number of lurkers within a xMOOC with the dropout rate, it is
obvious that users who have less interaction within the course are
much more likely to quit the course completely, especially within the
first two weeks. Among the lurkers, those who at least passively read
in the forums are again performing better than those who do not even
visit the forums once [22].
Nonnece and Preece studied why lurkers lurk within email discus- Reasons many users
don’t engage in
forums – or “why
lurkers lurk”
sion groups. They found that lurking can have four main reasons:
1) users want to stay anonymous for security or privacy reasons,
2) experience a lack of time, 3) are overwhelmed by the amount of
messages or annoyed by poor quality, or 4) users feel they are not
competent enough and thus shy away from publicly posting their
questions or answering other’s questions [38].
By alleviating these reasons for lurking, the interaction on a plat-
form can be amplified. In the case of xMOOCs this may reduce the
dropout rate and ultimately increase the user’s performance within
the course.
lack of personal mentoring Another issue of these massive
courses is the inability to offer personalized mentoring.
In 1984 Benjamin Bloom analyzed three groups of students and xMOOCs offer no
personalized
feedback
their performance and found that students with a personal tutorial
mentor perform better than the second mastery-learning class room
sized group with corrective feedback, which was again better than the
third, traditional teacher-centered class [4]. Bloom called this the 2
Sigma problem. Schulmeister argues that xMOOCs resemble this third
category of Bloom’s 2 Sigma problem, being solely teacher-centered
and offering no personalized feedback whatsoever [45].
Jere Brophy sees “praising students effectively” as one key incen-
tive a teacher can offer to motivate students and improve their atti-
26 Lurking: “One who lurks; a visitor to a newsgroup, chat room, blog, or so-
cial networking site who only reads other people’s posts, but never posts his or
her own comments, thus remaining anonymous.”, definition from netlingo: http:
//www.netlingo.com/word/lurker.php, accessed in March 2014

tude towards learning [5]. He states, this praising should be a spon- Effective feedback is
one key to
motivation
taneous reaction to an accomplishment and should always be personal as
well as delivered privately. By focusing on the “effort and care the
student put into the work” rather than “portraying the achievment
as evidence of the student’s intelligence or aptitude”, Brophy argues,
such feedback will not lead to vulnerabilities to failure in the future
(see section 4.2).
While learning rooms offer a less public place for exchange, stu-
dents still have to actively engage to receive feedback. Moreover, such
feedback can only be given on subjects the student talks about by him
or herself, since other users within the learning room have no other
connection to the student. Personalized feedback on the performance
of a student, for example after submitting a homework assignment,
does not exist in current xMOOC platforms.

3
U S E R M O T I VAT I O N
For being able to overcome the problems current MOOC platforms
face, it is crucial to understand what it is that motivates the partici-
pants to attend a course. Motivation comes from the latin word “mo-
vere” which means to move or to stir, motivation therefore means being
moved to do something.
Motivation of users has been an active field of research for more
than hundred years. Section 3.1 will introduce the difference between
intrinsic and extrinsic motivation, the most common differentiation
psychologists take when talking about motivation. The following sec-
tions will give an overview over existing motivational theories and
focus on theories applied in gamification. Gamification itself will be
subject in section 3.5 and section 3.6 will introduce two concepts of
classifying users by their leading motivation for engaging in a task.
3.1 intrinsic and extrinsic motivation
In the 1970s the concept of intrinsic versus extrinsic motivation has Where does the
motivation come
from?
been developed [27, 8], which differentiates between an individual’s
self-motivation and motivation which is triggered by the environment
of an individual.
intrinsic motivation Describing a self-determined motivation, Intrinsic motivation
comes from within
the person
which, rather than relying on a reward or other external pressure, in-
trinsic motivation is based on an individual’s own interest in the task
itself [43]. The following factors can enhance intrinsic motivation of
students:
• Autonomy, students having control over their own educational
results
• Self-efficacy beliefs, having the required skills for being effective
in reaching desired goals
• Interest in mastery, not only being after achieving good grades,
but having an interest in the topic itself
Intrinsic motivation can lead to an increased willingness in spending
free time for a specific topic or task. Students who are intrinsically
14

3.1 intrinsic and extrinsic motivation 15
motivated have shown to voluntarily work to improve their personal
skills and increase their capabilities [51].
extrinsic motivation Contrary to intrinsic motivation, extrin- Extrinsic motivation
comes from external
factors
sic motivation is derived from external factors. It lets individuals
strive for the goal of achieving a certain outcome, rather than for the
task itself. This sort of motivation is not rooted in personal interests
for the task. While extrinsically motivated tasks might be intrinsically
motivated as well, the motivation does not rely on an individual’s in-
trinsic motivation. Factors which can lead to extrinsic motivation are:
• Rewards, such as grades or monetary incentives
• Punishment if the task is not, or not well, done
• Competition in which individuals have the incentive of being bet-
ter than their opponents
the overjustification effect The phenomena of decreasing Extrinsic motivators
can undermine
intrinsic motivation
intrinsic motivation if extrinsic awards are being offered is described
by the overjustification effect. Stanford Professor Mark Lepper for
psychology and his research group analyzed this effect in a field ex-
periment, described in their paper Undermining Children’s Intrinsic In-
terest with Extrinsic Reward [27].
Lepper analyzed a group of preschool children who all showed
an intrinsic motivation in drawing. This group was subdivided into
three groups of the same size. In the first phase, each group was
instructed to draw a painting. The first group was told they would be
rewarded with a certificate when handing in the painting. The second
group was not offered this certificate, yet each student also received
a similar, unexpected, certificate at the end of this phase. The third
group was neither offered a certificate nor did they receive one. After
some time, this painting session was repeated. The psychologists
found that the intrinsic motivation of the students of the first group,
which was told that they would receive a certificate, was significantly
lower than the intrinsic motivation of both other groups.
Brophy explains this effect with the feeling of being bribed [5]. When
students become aware that a reward is offered for engaging in a cer-
tain behavior, they get the idea that this behavior must be so unpleas-
ant no one would voluntarily choose to engage in it. As long as the
reward is offered, students might engage in this performance, but the
appeal of freely engaging is diminished. The preschool children of
the first group then associated painting with receiving a certificate
rather than with the fun of the actual process of drawing. The in-
trinsic motivation fades and if at some point the reward is no longer
offered, the students will no longer show the behavior or participate
in the task.

3.2 self determination theroy 16
choice of extrinsic motivation While the overjustification Extrinsic motivators
need to be well
designed for being
successful
effect might suggest not to give any external rewards to intrinsically
motivated individuals, a well chosen extrinsic motivation can also
help establishing a routine in a task [31]. Rewards are found to be
most valuable for establishing such a routine when individuals have
a choice in the selection of rewards. Self determination of the individ-
ual is considered an important keystone for choosing rewards. Also,
an individual can gain intrinsic motivation for a task which was be-
fore unknown – if the task is introduced via such well chosen extrinsic
motivators [55]. This is a strategy good sales people often employ: a
customer leaves the store with a feeling of having bought the item he
or she always desired – without having known of its existence before
entering the store. While this might sound like a negative example,
it shows how extrinsic rewards may help fostering intrinsic motiva-
tion. How this choice of extrinsic motivators can effect the impetus
of learners will be subject in section 4.1.
3.2 self determination theroy
Psychology Professor Edward Deci and Richard Ryan from the Uni- Where does intrinsic
motivation come
from?
versity of Rochester described the human need for growth and ful-
fillment in the Self Determination Theory (SDT), a macro theory of
human motivation [12, 13].
SDT focuses on sources for intrinsic motivation. Deci and Ryan
assume people are actively directed toward personal growth and ful-
fillment. They identified three basic human needs that, if met, will
lead to intrinsic motivation:
autonomy The universal urge to be in power of one’s own life. An
example of autonomy is, if students are able to decide on what
they want to work for themselves.
competence The will to learn skills and gain mastery of tasks. This
can, for example, be seen when children want to learn an instru-
ment. They continue doing so for the sole purpose of improving
their own skills.
relatedness Every human being has an innate desire to interact
and connect with, or to relate to others. This desire is what
inspirits social networks as well as help forums such as Stack
Overflow1.
The authors explain further that the assumed orientation towards
personal growth requires continuous subsistence. Here, interaction
and relations to other individuals, their feedback and caring can fos-
ter – but also obstruct – this personal growth (see chapter 4.2).
1 Stack Overflow website: https://stackoverflow.com

3.3 drive theory 17
Deci warns against awarding people with extrinsic rewards for in-
trinsically motivated actions. This may impair their autonomy [11].
If extrinsic rewards are given, the behavior becomes more and more
controlled by such external factors until the intrinsic motivation fi-
nally abates.
For circumventing this problem, Deci suggests to instead offer peo- Unexpected feedback
as a good motivatorple positive, unexpected feedback as encouragement for a good per-
formance on a task. Since such feedback is not expected, people will
not wait for it and if the causing action was intrinsically motivated,
this motivation will not be affected by the feedback. To the contrary,
such feedback can help people to feel even more competent, which is
one of the above described keys to self-determination, and therefore
foster their intrinsic motivation.
3.3 drive theory
In 2009, economist and book author Daniel Pink introduced the Drive “Purpose” as
another intrinsic
motivator
Theory [42]. Pink analyzed why extrinsic incentives seem to fail for
complex tasks. Similar to SDT, he tried to find factors which lead
to an increase in intrinsic motivation and stipulated three, slightly
different key aspects:
autonomy Leads to increased engagement, similar to SDT.
mastery Focusing on the individual’s will to improve personal skills,
mastery is similar to SDT’s competence.
purpose People want to identify with the task they have. This, for
example, is what big companies call a vision. It unites all em-
ployees in striving to achieve this vision, this purpose.
3.4 relatedness, autonomy, mastery, and purpose
Andrzej Marczewski combined the findings of Deci and Ryan’s SDT Combining the SDT
and the Drive
Theory
(see section 3.2) and Pink’s Drive Theory (see section 3.3) and de-
veloped the Relatedness Autonomy Mastery Purpose (RAMP) the-
ory [29]. Being a web developer and game reviewer for more than
a decade, Marczewski started researching in the field of gamification
in 2011. Today, he is considered a thought leader in the field of gami-
fication2. RAMP has not yet been published in a scientific paper, but
it is the basis for Marczewski’s user types. As these will be discussed
in section 3.6.2 it will be regarded as a working theory here.
Combining the ideas of SDT and the Drive Theory, Marczewski stip-
ulates the following four motivational drivers which he argues should
2 see: http://sf14.gsummit.com/vote-for-the-most-influential-people-in-
gamification/ and remarks in http://www.engagingleader.com/four-game-
drives/

3.5 gamification 18
be included in all good gamified systems: relatedness, autonomy, mas-
tery , and purpose. While autonomy and mastery are part of SDT as Intrinsic motivation
as the key to good
gamification
well as the Drive Theory, Marczewski saw an importance in including
both, relatedness and purpose. He argues that relatedness, which is de-
scribed within the SDT, will keep people engaged even when “badges
have got boring, when the points are meaningless”. It creates a sense of
community and provides users with valued feedback. Purpose, while
being closely connected to relatedness, is regarded a key component
for continuous engagement. Marczewski argues that relatedness keeps
a community together, but it is purpose that makes individuals willing
contribute. He names Wikipedia3 as a paragon of such human seek
for purpose. On Wikipedia, millions of people contribute their knowl-
edge, free of charge. Their sole motivation is to increase the common
understanding of topics.
One or more of these four intrinsic motivators are included within
every good gamified system, argues Marczewski. But he further ex-
plains that additional extrinsic motivators should be included as well
as a way to “reinforce and support motivation”.
3.5 gamification
Gamification describes the use of game elements in non-game con- New research on a
long used concepttexts for motivating and encouraging users on several different levels.
This concept is not new at all. Duke University sociologist Donald
F. Roy published the article “Banana Time – Job Satisfaction and In-
formal Interaction” in 1959, where he describes how factory workers
dealt with the “beast of monotony” by implementing “fun and fool-
ing” into their daily routines. In 1979, Frank Lorenzo’s Texas Interna-
tional Airlines launched the world’s first frequent flier program. This
program was later often regarded as the first strategic implementa-
tion of gamification, though the term gamification did not yet exist4.
The term was first coined in 2002 by Nick Pelling [28] and it took an-
other eight years, until 2010, that “gamification” gained widespread
usage5. In the last few years, researchers have started analyzing the
concept of gamification, and how it can be applied most successfully.
a formal definition “‘Gamification’ refers to the use of design ele-
ments characteristic for games in non-game contexts”, Deterding et al. in
2011 [15].
Figure 2 shows a classification by Deterding et al. from the same
paper [15] which arranges “gameful design”, which is used as a syn-
3 see: https://www.wikipedia.org/
4 see: http://markenregisseur.at/wp-content/uploads/2012/08/gamification-
factsheet_2012.pdf, accessed in March 2014
5 see: http://tech.fortune.cnn.com/2010/09/03/the-game-based-economy/, ac-
cessed in March 2014

3.5 gamification 19
Toys
Gaming
Playing
PartsWhole
Gameful Design
(Gamification)
Playful Design
(serious) Games
Figure 2: “Gamification” beween game and play, and whole and parts of the
system; derived from [15].
onym to gamification, on the dimensions gaming versus playing and
usage on the whole system versus on single parts.
This definition explicitly limits the term gamification to “non-game Gamification is
limited to non-game
contexts
contexts”. Therefore games themselves are not considered gamified,
since the whole setting is a game. This also applies to serious games,
which are full-fledged games but have another purpose other than
fun alone, like learning games. This means the term gamification
can only be applied to systems which, in general, are not games –
gamification is only applied to parts of the system.
Also, Deterding et al. differentiate between gaming and playing.
This differentiation, paidia to ludus (lat. for playing and gaming), was
already introduced in 1961 by Roger Caillois [7] in his work Man,
Play, and Games. Playing is considered a more loosely, unstructured,
not bound to rules form of enjoyable activities. Gaming is, in contrast,
bound to rules and has defined goals.
Gamification as depicted in figure 2, is, by this definition, applied
only to parts of the system and consists of fixed rules with defined goals.
These rules are what Deterding et al. describe as “design elements
characteristic for games”.
Building on the ideas of intrinsic and extrinsic motivation (see sec- Adding playful,
extrinsic motivators
to non-game
environments
tion 3.1), gamification seeks to add extrinsic motivators to existing
non-game environments. Thereby, it is assumed that well engineered
extrinsic motivators can foster intrinsic motivation in users. Yet, when
developing a gamification concept, it is crucial to avoid suggesting
that the task at hand is in fact gamified, since otherwise users might
not fully engage. But by choosing the employed extrinsic motivators
carefully, such an overjustification effect can be avoided [31]. Further-
more, gamification can help users foster routines and user behavior
can be guided through deliberate use of gamification elements.

3.6 classification of users 20
3.6 classification of users
As the variety of motivation theories already indicate, different users Users have diverse
interestsmay have very different motivations for engaging in the same task.
To properly understand the motivation of users and for designing
the system to their needs a classification of different user classes is
required.
3.6.1 Bartle’s Player Types
Computer Game Design Professor Richard Bartle from the Univer- Multiple
motivations for
playing the same
game
sity of Essex analyzed player behavior in 1996 within a Multi-User
Dungeon (MUD), a usually text-based real-time multiplayer virtual
world [1]. A lively discussion of fifteen advanced MUD players about
why people engage in these games was the starting point for Bartle’s
research.
Acting
Interacting
WorldPlayers
Explorers
♠︎
Socializers
♥︎
♣︎

Killers
♦

Achievers

Figure 3: Bartle’s four player types categorized by their source of interest in
the game
The icons were added by Bartle to represent the interest of the respecting
group: achievers like collecting diamonds, explorers dig up every corner with
their spades, socializers are dominated by a big heart, and killers enjoy hitting
other players with their clubs; derived from [1].
For better understanding player behavior, he came up with a cat- Four categories
based on a player’s
main interests
egorization of four different player types. These types, depicted in
figure 3, are based on a player’s main interest in either acting or in-
teracting and being mostly interested in the world or interest for other
players. As Woinar described in more detail [53], Bartle found that
these player types can be classified based on their main motivation to
play [1]:
achievers are interested in progressing within the game and in achiev-
ing the final goal. They are acting on the game world and are all
about progression within the game.

explorers are interested in discovering everything that can be dis-
covered. Explorers are looking for surprises while interacting
with the world, for explorers it’s all about discovery.
socializers are interested in what other players have to say, what
they think and how they feel. They are not primarily interested
in the game, they are all about interacting with other players and
getting to know them.
killers are interested in superiority over other players. Being all
about acting on other players, killers want to show off with their
superiority.
While a player tends to belong to one primary, fixed category, the One primary
category and traits
of all others
other categories also influence the player’s behavior. The player drifts
between these secondary categories based on mood and preferred
goal within the game.
On average, the vast majority of people, approximately 75%, are so-
cializers as a primary category. Achievers and explorers account for 10%
of people’s primary categories while killers only represent 5% [55].
While Bartle’s paper focuses on players of MUDs in particular, his
findings were used in multiple fields of game design and often are
referred to when user behavior in other domains is analyzed. Also,
many gamification theories still go back to this classification of play-
ers [50].
3.6.2 Marczewski’s User Types
Marczewski developed the user types for transferring Bartle’s player A categorization for
the non-game worldtypes (see section 3.6.1) to applications in non-game contexts [28, 30].
Bartle himself compared using his player types on anything other
than voluntary MUD players who play solely for fun, to applying
human psychoanalysis on animals6. While this might work to some
extent, there is no actual proof that it does.
To a certain extent, Bartle argues, his player types will help to under-
stand user behavior even outside of actual games. Yet, there will be
situations his theory does not explain.
Marczewski saw this as an opportunity to evolve a new categoriza- Integrating RAMP
into Bartle’s player
types
tion of users7. Based on Bartle’s player types, but combining them
with the RAMP motivation theory (see section 3.4), the user types seek
to explain user behavior outside of games [30, 28].
Defining his user types, Marczewski differentiates between users Differentiates
between intrinsically
and extrinsically
motivated users
6 See Richard Bartle’s talk from the Casual Connect Europe, February 2012: https:
//www.youtube.com/watch?v=ZIzLbE-93nc
7 In December 2013, Marczewski started publishing a new revision on his user types,
the User Types 2.0. He introduced another user type, the Disruptor. Since, as of this
writing, these User Types 2.0 are not quite complete yet and their description is still
rather vague, this thesis will use the initial user types as working theory.

Networkers
Self Seekers
Exploiters
Philanthropists
Socializers
Consumers
INTERACTING
EXTRINSIC
USER
ACTING
INTRINSIC
Free Spirits
Achievers
SYSTEM
Figure 4: Marczewski’s user types and their relations
Categorized by the three dimensions intrinsic or extrinsic motivation, focus
on acting or interacting, and interest in other users or the system; derived
from Marczewski’s User Types in Gamiﬁcation8.
who primarily want to use the system – who are intrinsically moti-
vated – and those who are motivated by extrinsic factors, who want
to play.
Figure 4 shows the interconnection of the different user types by
the three dimensions intrinsic or extrinsic motivation, focus on acting
or interacting, and interest in other users or the system.
intrinsically motivated users These users are interested in Intrinsically
motivated users are
in for the content
what the system has to offer, they have an intrinsic motivation to
reach the system’s goals. This means, if the system is a learning
platform, they are there for actually learning about the subject. These
intrinsically motivated users somewhat resemble Bartle’s player types,
yet Marczewski combined them with the RAMP theory. He came up
with the following four intrinsically motivated user types:
philanthropists Seek for purpose. Philanthropists want to give
back to others and feel being part of something bigger. They
are known for being the ones always helping out on forums,
contributing to wikis and happily sharing their knowledge with
others.
8 Synopsis of Marczewski’s User Types in Gamiﬁcation: http://marczewski.me.uk/wp-
content/uploads/2013/06/user-type-download.pdf, accessed in December 2013

achievers Mastery is what leads Achievers. They want to achieve ev-
erything there is to achieve within the system, learn everything
there is to learn and be the best at it. Achievers will compete
against other users, but merely as a way to get better. They are
known for striving towards knowledge by any means.
socializers Similar to the socializers Bartle defined, they endeavor
relatedness. Interacting with others and being connected is what
leads socializers. Socializers will use all parts of the system which
will help them getting in touch with others. Especially the in-
ternal social networks of the system, if existing, will be heavily
used by socializers.
free spirits Seeking self expression and autonomy, free spirits like
to have agency. They do not want to feel restricted in any way,
the more possibilities a Free Spirit has, the more his or her cre-
ativity can unfold. Free spirits like to explore the system and find
out everything there is to know. They are known for having the
fanciest avatars and also have the most personal content.
extrinsically motivated users This group was initially only The extrinsically
motivated counter-
parts are in for the
“game”
called players, since these extrinsically motivated users use the service
to play. They are interested in rewards and like the “game” of it all.
Players are most likely to use “loop holes”9 to gain advantage. Within
this player group, Marczewski found that for each intrinsically moti-
vated user group there is a player-pendant. But since these groups
are not primarily intrinsically motivated they each behave differently
than their pendants when using the system. This way there are four
player subgroups:
self seekers Resemble the philanthropists, yet self seekers engage for
benefits they could gain. When for example answering ques-
tions of other users, this can create the problem of quantity over
quality, since self seekers try to optimize the effort they spend per
achievement.
consumers While resembling the achievers, consumers are outstand-
ing when it comes to optimization. Since consumers are more
interested in the awards they get for using the system then the
content of the system, they will try to do just as much as re-
quired for gaining the reward. consumers will for example hap-
pily use loyalty schemes of any kind.
networkers Similar to socializers, networkers will seek to connect
to others, but rather for the reason of increasing the number
of followers on their profile. While socializers want to interact
with their connections, networkers are happy with just having
9 “Loop holes” are bugs in a system, which can lead to unintended behavior.

the connection. networkers send friend requests to people they
never spoke to or wrote with before.
exploiters Like free Spirits, exploiters want to know everything there
is to know about the system. But exploiters are interested in us-
ing this knowledge to gain rewards by any means. They will
link their proﬁle to all other services if this is rewarded with
points. Also, exploiters will use “loop holes” if they can gain an
advantage through these.
Having these two categories of users with their subcategories in Take all user types
into considerationmind, a good gamiﬁcation concept can employ well chosen extrin-
sic rewards to foster intrinsic motivation in players while avoiding to
scare off the already intrinsically motivated users. This concept is
described in section 3.1.

4
M O T I VAT I O N A N D E D U C AT I O N
“Tell me and I forget, Teach me and I may remember, Involve me
and I learn.” — Xunzi1
Through applying ideas of gamification and user motivation above
involvement of users can be intensified. The goal is to make users
more than just mere consumers. Why such involvement is important
for learners will be discussed in section 4.1, section 4.2 discusses the
role of feedback in learning settings, competition in classrooms is dis-
cussed in section 4.3. Section 4.4 will then present different learning
theories and an existing concept for gamifying openHPI is laid out in
section 4.5.
insight into learning psychology Learning situations have Intrinsic motivation
facilitates learningshown to be most efficient when users have an intrinsic motivation
(see chapter 3.1) for apprehending the taught matter [5]. This obser-
vation can be explained by SDT (see chapter 3.2), which focuses on
the three human needs autonomy, competence, and relatedness. Espe-
cially competence stresses the desire to internalize knowledge as key
for intrinsic motivation.
4.1 the role of extrinsic motivators
Jere Brophy therefore argues in his book “Motivating students to Extrinsic motivators
can harm learninglearn” [5] that for learning situations self determination in particular
is critical. He explains why extrinsic motivators – often originally em-
ployed for motivating the students – can in fact decrease the student’s
motivation to learn autonomously. Brophy names three characteris-
tics of extrinsic motivators which are especially harmful to intrinsic
motivation of learners. These characteristics are described as:
high salience meaning the offered rewards are very attractive or
draw attention to themselves.
1 Xunxi, born as Xun Kuang in 312 BC was a Chinese Confucian philosopher; this
quote is derived from chapter 11 from the eighth book of his works, Ruxiao, and in
this translation became popular in the 1980s; the quote is often falsely attributed
to Benjamin Franklin, see: http://www.barrypopik.com/index.php/new_york_city/
entry/tell_me_and_i_forget_teach_me_and_i_may_remember_involve_me_and_i_
will_lear/
25

4.1 the role of extrinsic motivators 26
non-contingency exists if rather than rewarding the achievement
of specific goals, mere participation in the activity is rewarded.
unnatural / unusual rewards are not the natural outcome of a
student’s behaviors but rather are artificially used as a means
to control the student’s learning behavior.
Rewards which implicate such characteristics often lead to learners Optimize learning
for acquiring
rewards
loosing interest in the actual matter and starting to learn merely for
acquiring rewards. Learners then try to optimize their “efficiency” by
deciding to rather half-heartedly participating in multiple different
tasks than to fully committing to one single task.
This leads to learners being less retentive, and even worse, less
motivated in gathering knowledge.
Rewards in learning environments can be either verbal or tangible
and can be given on three different levels, 1) engagement-dependent
rewards are granted for the mere engagement in a task, 2) completion-
dependent rewards which are granted after completing an activity, and
3) performance-dependent rewards which are not given for any comple-
tion, but only if the student fulfills some criterion for performance.
Two seemingly contradictory research positions on the effect of Are there “good”
extrinsic
motivators?
such rewards on students have been postulated in the late 1990s by
Eisenberger et al. [18, 17] on one side and Deci et al. [14, 10] on the
other. While Deci and his colleagues emphasized that the Overjustifi-
cation Effect (described in section 3.1) causes a decrease of a student’s
intrinsic motivation when being extrinsically rewarded, Eisenberger
et al. argue that especially verbal or performance-dependent rewards can
result in an increased intrinsic motivation of students. Eisenberger
and his research team stated that these either verbal or performance-
dependent rewards increase the students’ feeling of competence and
therefore, as explained by the SDT (see chapter 3.2), increase their
intrinsic motivation.
In 2002, Houlfort et al. analyzed this differences in outcomes [23]
and found that the two research teams had a different way of mea-
suring the student’s feeling of autonomy, one of the key measures for
analyzing intrinsic motivation. While Eisenberger’s team measured
autonomy as if a student feels free to choose something else than what
asked to do, Deci et al. took the student’s feeling of being pressured as
indicator of a lack of autonomy. Houlfort summarizes that expected,
performance-dependent rewards can very well increase a student’s feel-
ing of competence, but at the same time let the student feel this pres-
sure. Such rewards do not influence a student’s freedom of choice, but
still affect the perceived feeling of autonomy since the student feels
pressured to perform accordingly.
Referencing Sansone & Harackiewicz’ book on Intrinsic and Extrin-
sic Motivation [44] and referring to the different results when analyz-
ing effects of extrinsic rewards, Brophy argues that such rewards need

4.2 importance of feedback 27
to be carefully designed to be expedient [5]. warning against overus- Well selected
extrinsic motivators
can increase
intrinsic motivation
ing or misusing aforesaid rewards, Brophy argues in favor of using
well selected extrinsic motivators in learning contexts. If extrinsic
rewards are designed properly, by carefully avoiding the previously
described demotivational aspects, an increase in intrinsic motivation
can be achieved.
Rather than just maintaining existing types and levels of intrinsic
motivation, teachers in traditional learning environments also are
obliged to establish and enforce regulations and make students en-
gage in tasks they might otherwise not engage in out of their free
will. Here, properly designed extrinsic rewards can help motivating
the students.
But educators also face the external pressure of having to grade Grades are
accompanied by
unfavorable side
effects
their student’s performance [37]. Grades represent a sort of tangible
rewards, since they are persistent and comparable. And while grades
are performance-dependent in respect to which grade one will get, the
fact that a grade is given is merely engagement-dependent. Moreover,
since grades are comparable they introduce competition into classes,
producing winners and losers [5].
Control mechanisms, such as supervision, monitoring, or perfor-
mance evaluation, are employed for assessing the student’s accom-
plishments. These control mechanisms and the negative competition
often replace the student’s joy of free learning with experiences of
pressure, anxiety, boredom, or alienation and ultimately will under-
mine the student’s feeling of autonomy and weaken the intrinsic mo-
tivation [37].
4.2 importance of feedback
As described in section 4.1, verbal rewards have shown to be a power- Feedback can have a
positive impact on
learners
ful extrinsic motivator which can have a positive impact on a learner’s
intrinsic motivation. A common form of such a verbal reward is feed-
back.
Feedback, as conceptualized by Hattie and Timperley in The Power
of Feedback [20], is an “information provided by an agent (e.g., teacher, peer
[...]) regarding aspects of one’s performance or understanding”. It is seen
as a consequence of performance.
The authors continue that while teachers for example can provide Different types of
feedback have
different effects
corrective information, peers can provide an alternative strategy. By
synthesizing several meta-analyses, Hattie and Timperley could show
an overall positive effect of feedback on students’ accomplishments
within classes. While typical schooling has an effect size of 0.4, feed-
back including reinforcement, motivational inﬂuences or cues reach
almost double these effect sizes, being greater than 0.7.
Hattie and Timperley distinguish between four categories of feed-
back:

task oriented This category of feedback is the most common one
and regarded as very powerful. Distinguishing correct from
incorrect answers, aimed at building more surface knowledge
and acquiring information, this type of feedback is also called
corrective feedback. Corrective feedback can be diluted by mixing
it with person oriented feedback. Moreover, it bears the risk of
not providing the opportunity for generalization, therefore stu-
dents may not be able to employ the learning in other contexts.
If task oriented feedback is too specific and always provided in
an immediate manner, trial-and-error strategies gain the upper
hand over cognitive efforts.
task processing oriented Setting tasks into relation and extend-
ing them, feedback about the processing of a task can facilitate
deeper learning. Such feedback is aimed at the construction of
meaning and providing context surrounding the task. While
this category of feedback is in general also not customized to
the recipient, it is usually particularly helpful for error detection
strategies. If students encounter an impediment while pursuing
a goal, a reassessment of the situation is triggered.
self-regulation oriented Such feedback addresses the way a
student monitors, directs and regulates actions toward the own
learning goal. Self-regulation is seen as an interplay between
commitment, control and confidence and implies autonomy, self-
control, direction and discipline. While being engaged in aca-
demic tasks, effective learners may create internal, self-regulated
feedback. Yet, less effective learners depend on external in-
put for delivering such feedback and rarely seek for it. Self-
regulation oriented feedback helps setting performance into re-
lation to one’s own goals and expectations. Such feedback may
refer to the learner’s effort, which is especially remunerating in
earlier stages of learning when an expansion of effort translates
into higher success levels.
person oriented This last category is feedback about the self as a
person. Person oriented feedback carries little to no task-related
information, often is diluted and is considered being too influ-
enced by student’s self-concept of being efficient. Praise often is
considered one form of such self-oriented feedback. While stu-
dents commonly claim to like being praised, Hattie and Timper-
ley refer to several studies indicating a negative effect on the stu-
dent’s learning behavior. Especially adult learners have shown
to react negatively to solely person oriented feedback [20]. If
praised in case of success and treated neutrally in case of fail-
ure, learners see this as in indication of the teacher perceiv-
ing their personal competence as low. Otherwise, if success is
treated neutrally and failure is criticized, learners perceive that

the teacher estimates their competence as high, yet the invested
effort as too low. In both ways, it leads to negative overall ef-
fects.
Jere Brophy summarizes that feedback should be provided shortly Task, task processing
or self-regulation
oriented feedback is
valuable
after a response, it should explain reasons for errors and how to ei-
ther avoid or correct them (task orientation). Feedback should help
a learner to build the ability of self-monitoring and evaluation (self-
regulation) and it should direct the learner’s attention to develop-
ment of knowledge, skills or competences (task processing) [5].
One form of feedback is praise. While Hattie and Timplerley con-
sider praise as solely person oriented feedback and therefore only
carrying negative effects [20], others regard praise as addressing a
basic human desire for seeking the approval of others [21]. They ar-
gue that, if applied cautiously, praise can be a good motivator which
helps fostering intrinsic motivation (see section 4.1).
Brophy composed the following guidelines for praise, classifying How to praise
effectively?certain facets into either leading to effective or ineffective praise [5]:
effective praise Praise is found to be most effective, when it is
delivered contingently, yet not being expected by the learner.
Effective praise should show spontaneity and should be var-
ied. Setting the praise in context to the student’s own prior
accomplishments or the performance on prior challenges leads
to more effective praise. Praise should be given in recognition
of noteworthy efforts or success at tasks which were difﬁcult for
the individual student.
ineffective praise On the other hand, when praise is delivered
unsystematically, is restricted to global positive reactions or re-
wards mere participation, the negative effects Hattie and Tim-
perley criticized overweight. Praise not providing any addi-
tional information or comparing the student’s own status to oth-
ers has shown to have even more severe negative effects. Such
praise orients students towards comparing themselves to oth-
ers or to thinking about competing, which shifts the focus away
from the task. Not taking the student’s effort into account or
not setting the accomplishment into context has further nega-
tive consequences. Also praise should never attribute to ability
or luck alone.
If these guidelines are followed when praising students, Brophy
argues, praise can be a very effective way of providing motivating
feedback [5].

4.3 competition in classrooms 30
4.3 competition in classrooms
Another very powerful and yet highly debated motivator in class- Succeed or avoid
failure?room situations is competition. Motivation to succeed and motivation to
avoid failure are two key components of achievement motivation [5].
motivation to succeed is determined by the personal value or
need of the task’s outcome, the estimated probability of succeed-
ing, and the personal appreciation of possible rewards attained
by a successful outcome.
motivation to avoid failure in turn is determined by the per-
sonal need to avoid failing the task, and the severity of fearing
the negative outcomes this failure could bring, such as private
disappointment or public embarrassment.
How an individual behaves when choosing a task at hand is often
determined by the relative strengths of these two motivations. If the
motivation to succeed is predominant, individuals engage in the task
willingly. If the motivation to avoid failure is stronger, individuals
seek to either avoid the task or, if this is not possible, to minimize the
likelihood of failure.
Grades , as explained in section 4.1, now introduce a situation of Grades introduce
competitioncompetition into classroom situations. While grades are generally
seen as having a negative effect on the students’ learning behav-
ior [37, 20], Brophy argues that especially risking students having
to face public failure is what makes this system of competition so
negative.
Brophy described the following aspects as why competition is widely What makes
competition
demotivating?
regarded as demotivational in learning situations [5]:
public failure This risk of public failure is seen as one key demo-
tivational aspect of competition. Moreover, competition tends to
draw the students attention away from the subject’s matter to-
wards to the social comparison. This effect is especially present,
when the competition is personal.
compelled to compete If participation is mandatory, or results
even count for grading, competition becomes more coercive
than motivational. This becomes even more severe when high
stakes are attached to the competition’s outcomes.
chance of winning Competition also can only be powerful if the
motivation to avoid failure does not outweigh the motivation to
succeed. This can only be ensured, if everyone has a good or at
least an equal chance of winning.
losing streak Another root problem with competition is that it
always creates winners as well as losers. If someone loses con-
sistently conﬁdence, self-esteem, and enjoyment of learning can

4.4 learning theories 31
suffer. In team competitions members of losing teams may
scapegoat certain members of the team.
Yet, competition is a very powerful extrinsic motivator and has By minimizing risks,
competition can
become an expedient
motivator
been found especially useful for routine practicing tasks.
Minimizing risks and ensuring equal chances of winning is crucial
for using competition as a beneficial component of learning. Balanced
teams by ability or handicapping system in case of individual compe-
tition can help ensuring these equal chances for winning. Also, who
wins should be primarily decided by effort and maybe by luck rather
than by ability. The competition moreover should never become per-
sonal. The focus of the competition has to lie on the task, not on who
wins and who loses. By congratulating the winner but not criticizing
the looser, such a positive atmosphere can be reinforced [5].
Therefore, elements that introduce competition into learning envi-
ronments need to be very carefully designed.
4.4 learning theories
Theories of learning have been an active field of research since the
early 20th century and a variety of different theories have since evolved.
The following section will give a brief overview, after which connec-
tivism as the theory cMOOCs are based on will be discussed in more
detail.
4.4.1 A brief overview
In the 1930s, Researcher B.F. Skinner began to analyze rats while Behaviorism and the
instructionist model
of learning
carefully applying positive and negative reinforcement in a repetitive
fashion. He found that this way the animals learned to perform even
complex tasks [48]. In this research the cognitive theory of “behavior-
ism” is rooted, which seeks to explain human and animal behavior
with scientific methods only, rejecting introspective methods. Apply-
ing behaviorism to human education, drill and practice mechanisms
evolved. Students learn the matter by repetition and are fully con-
trolled by the external reinforcements. Underlying is the “instruction-
ist” model of learning which assumes that learning is about knowing
facts [6].
“Constructivism” is another learning theory, including introspective Constructivism
seeks to engage
learners
methods and stating that people learn better when actively engaged
with the subject matter. Jean Piaget, the founder of constructivism,
believed that humans learn from their interaction between own expe-
riences and ideas [41]. Constructivist learning is the idea that by e.g.
solving puzzles and trying different approaches, students experience
the subject’s matter and learn en passant [6].

4.4 learning theories 32
Building on this idea, Seymour Papert, a former student of Piaget, Constructionism
introduces creation
into learning
developed the “constructionist” learning theory. This iteration on Pi-
aget’s theory holds that people learn most effectively when they are
actively creating tangible objects within the real world [40]. Construc-
tion kits are one approach of including these ideas into education [6].
More recent approaches to learning theories seek to integrate the Connectivism and
the social dimension
of learning
social context of learning. By taking the motivation and support of
others into account, these theories are particularly powerful when
combined with constructionism [6].
4.4.2 Connectivism or the “c” in cMOOC
Introduced in 2005 by George Siemens and Stephen Downes, “con- Importance of social
and cultural
contexts
nectivism” is a learning theory which puts emphasis on the impor-
tance of social and cultural contexts and at the same time sees the
potential in individuals creating content. Connectivism seeks to com-
bine principles of chaotic, networked learning, complexity and self-
organization [46, 45].
a learning theory Connectivism assumes that knowledge is Knowledge resides
in networksdistributed across networks of connections. Learning therefore is the
process of constructing and traversing these networks [16].
Siemens argues connections that enable individuals to learn are
more valuable than the actual knowledge the individual currently
has itself [46].
Following Siemens [47], connectivist learning 1) is based on knowl- How does
connectivist
learning work?
edge resting in a diversity of opinions, and 2) it is a process of connect-
ing specialized nodes or information resources within the network.
Knowledge 3) may reside in non-human appliances. Learning itself
is 4) more critical than knowing and 5) continual learning is maintain-
ing and nurturing these connections. Moreover, 6) the ability to see
connections, recognize and and make sense of patterns is a core skill
for all individuals in today’s world. 7) All connectivist learning ac-
tivities seek to keep the knowledge up-to-date, to have accurate and
current information. And 8) decision-making itself is learning.
Connectivist learning is a self-organized and social process. Not
only do students choose what and how to learn, they also curate, rate
and produce content themselves [45]. While students become equal
partners in learning who practice and reﬂect, teachers become “facil-
itators”, modeling and demonstrating the subject [16].
connectivism and moocs As described in chapter 2.2, MOOCs Connectivism as the
initial theory behind
MOOCs
evolved from this idea of connectivist learning.
In 2008, Siemens and Downes launched the ﬁrst MOOC “Connec-
tivism and Connective Knowledge” (CCK08). While the course cov-
ered connectivism as content, its format also attempted to implement

4.5 existing gamification concept for openhpi 33
these connectivist ideas. The content was available through feeds,
learners could for example use discussions in the Moodle platform or
write personal blog posts. They met in chats or in virtual worlds. The
actual MOOC platform in these cMOOCs is more an entrance point
for learning, a collection of – mostly user generated – resources.
Downes states autonomy, diversity, openness, and interactivity are
four unalienable principles for self-determined learning which are
key to a thriving MOOC [45].
Yet , as already described in chapter 2.2, for coping with higher xMOOCs and
connectivist ideasstudent numbers and for being able to bring traditional classes to the
online learning community, xMOOCs leave out many of the connec-
tivist ideas. xMOOCs are generally intended to be conveyed on one,
central platform. Discussions take place in the platform’s forums and
learners mainly follow the course plan laid out by the teaching team.
But these approaches do not have to be mutually exclusive. By inte-
grating ideas of cMOOCs into xMOOCs, benefits of both approaches
might be combined which can lead to an even richer learning experi-
ence2.
4.5 existing gamification concept for openhpi
Within a master student’s project at HPI in 2013 (see chapter 2.3.2), Reducing the
dropout rate through
gamification
an initial gamification concept for xMOOC platforms had been devel-
oped, exemplarily implemented for the openHPI platform [52]. The
student’s goal was to reduce the problem of high dropout rates within
current xMOOCs (see chapter 2.4) by combining the two emerging
trends xMOOCs and gamification (see chapter 3.5).
gamification elements suited for xmoocs By carefully an- Which game
elements are suited
for xMOOCs?
alyzing game elements and their effect on learners, the students devel-
oped a list of elements, which are particularly suited for use in educa-
tional settings. Rewards in forms of points, badges and acknowledg-
ments were introduced as a way of motivating learners. A point sys-
tem was developed which rewards active participation within courses.
The forums were redesigned as non-hierarchical pinboards which can
be integrated throughout the whole platform. Moreover, the visual-
ization of the learner’s progress was spotted as an important factor
for keeping students motivated. Therefore, a new visualization con-
cept was developed, which represents the course as a two dimen-
sional map. This map can then be discovered by the students.
2 From the blog article “cMOOCs and xMOOCs – key differences” by Janny Mackness
who participated in the CCK08 course as well as in xMOOCs provided on Coursera
and compares the experiences. See: http://jennymackness.wordpress.com/2013/
10/22/cMOOC-and-xMOOC-key-differences/, accessed in April 2014

4.5.1 Quiz Points and Gamification Points
In current xMOOCs, students can collect points for submitted home- Points already exist
in current xMOOCswork assignments or within the final exam. These points are then
summed up and used to calculate the student’s performance within
the course and to issue a course certificate.
Points are a common tool in gamification to increase the motiva-
tion of primarily extrinsically motivated users (see Marczewski’s user
types, section 3.6.2). Being a countable measure, points can help users
to compare the “value” of different tasks and therefore can be a mea-
sure to steer user behavior. Yet, this in turn reduces the autonomy of
users which ultimately can lead to a decrease of overall motivation
(see chapter 3.2). Therefore, a pointing system needs to be carefully
designed to be effective.
The master students’ project found that the existing grading points Existing points
resemble grades,
gamification points
should motivate
users
value only how students perform within the test situation. But these
points lack to value how a student performs, behaves and participates
within the whole rest of the course. A student who is very active in
the forum and helps others out a lot, yet performs not as good at the
assignments does not get any quantifiable reward for it. By imple-
menting a gamification point system, which is decoupled from the
grading points, the master’s project aimed at rewarding these active
students. Points are being granted for two major types of activities,
personal progression within the course and helping other learners
out.
This way, the points represent a student’s experience and reputa- Gamification points
resemble experience
and reputation
tion within a single course. When the points are being aggregated, a
measure for the experience on the whole platform can be created. An
initial point system has been laid out by the master student’s project
team and has been implemented into the new openHPI platform.
4.5.2 Badges as Additional Certificates
Another popular tool in gamification for rewarding users are badges. Badges can be
collectedThey are also especially effective on the extrinsically motivated user
types. Badges are persistent, meaning a user cannot lose the badge,
and are unique for a certain activity. Therefore, they are seen as
awards users can collect and use to openly demonstrate their mas-
tery of the particular activity. If users are aware of existing badges
before they perform a task, just like points these badges can serve as
guideposts.
Badges have a remarkably high value value for users. Since in Will only be given
for outstanding
accomplishments
MOOCs the foremost unique accomplishment is the successful com-
pletion of the course, the project team decided to only issue badges
for such successful completion. Additionally, badges can be issued
is when teaching assistants want to award outstanding students, for

example if they were a major help within the discussions. This way,
badges on openHPI keep a very high value.
4.5.3 Acknowledgments Make Users Smile
As another way of rewarding users for minor accomplishments, ac- Acknowledgments
reward minor
accomplishments
knowledgments have been introduced to openHPI. Acknowledgments
are implemented as short textual messages displayed in a modal di-
alog. Like badges, these messages are unique for a certain activity.
Yet, such acknowledgments are not persistent, meaning they are only
visual for a short period of time and can only be seen by the user
receiving the acknowledgment.
Such messages are supposed to be delivered spontaneously and do
not necessarily need to be tied to rules a user understands intuitively.
Acknowledgments are supposed to focus on how the student did some-
thing, what the student did or what patterns a student followed. This way,
these messages are presenting the student with either task, task pro-
cessing or self-regulation oriented feedback, which has been found to
be a successful measure for motivating learners (see section 4.2).
A scenario where such motivational messages could be effectively Regain motivation
through
acknowledgments
employed is for example when a student slowly loses interest in a
course. The team imagined that the system might recognize that
the student’s performance in assignments decreases, which could be
an indicator for an impending dropout. An acknowledging message
might then convince the student to not give up and to return and live
up to earlier successes.
This scenario is being discussed and extended in section 5.1.
4.5.4 A Pinboard Instead of Nested Forums
In chapter 2.4, forums were found to be the key social element within Making forums
more flexibleMOOCs. Yet, the masters’ project found that applied in MOOCs,
these traditional, hierarchical forums lack some key features such as
the ability to easily find the correct answer for a question or being
able to ask questions related to multiple topics [53].
Therefore, pinboards have been introduced to openHPI. Pinboards
have a flat structure, grouping and filtering is done via tags the user
assigns while creating the post. Moreover, a pinboard post can either
be a discussion or a question.
Discussions resemble the traditional forum threads. Students can Discussions to
exchange ideascomment onto the initial post and all comments are displayed in the
chronological order. If a user finds a discussion helpful, the initial
post can be upvoted. Such discussions can for example be used for
exchanging ideas about ongoing course subjects.

A

B

D

E1

E2

G1

G2

C

F1

F2

H1

H2

H3

I

Figure 5: A question on the openHPI pinboard
(A) The question title and tags added by the user (B) The question asked
by the user (C) Votes for this question (D) Comments on the initial question
(D1,D2) Answers given by users, ordered by their votes (F1,F2) Votes for
the questions, (F1) has already been accepted by the questioner (G1,G2)
Comments on the answers (H1,H2,H3) Information about who posted the
question / answer and when (I) Possibility to subscribe to the question
Questions on the other hand follow another principle. While stu- Questions to
effectively find
correct answers
dents can still comment onto the question, for example for asking the
questioner to further explain the initial question, answers are treated
separately. Figure 5 shows a question on the new pinboard. Both,
the initial question as well as the answers can be upvoted. Questions
cannot be downvoted, which is supposed to emphasize the idea that
no question is neither wrong nor stupid. Yet, since answers can be
incorrect, they can be downvoted. Given answers will be displayed
underneath the question and will be ranked by their votes. This way,
the most helpful answer will be displayed as first post right below the
question. Moreover, next to each question and answer, the avatar and
name of the author is shown. This is intended to increase the social
component of these pinboards.
Since the structure of pinboards is determined only by tags added Pinboards can be
integrated
throughout the
whole platform
to the questions or discussion posts, they are intended to being used
throughout the whole system. From many places within the system
the user should be able to access the pinboard and post a question, or
start a discussion. While tags can manually be added, some tags will
be provided by the system. These system tags connect the question or
discussion to the course, the current week and can also be as specific
as referencing a single lecture video or quiz question. This way, users

can effectively filter the pinboards. More fine grained tags, such as a
tag for a single lecture video, include their higher level tags, such as
the tag for the current course week. Users can search the forums for
questions related to a specific course item, or just for posts within a
whole week. The latter query then includes questions and discussions
for the more specific course items as well as generic questions relating
to the whole week.

5
C O N C E P T
As stated in chapter 2.4, high dropout rates are common in current Where do the high
dropout rates come
from?
MOOCs. Users sign up for a course but then loose interest in continu-
ing on the course’s journey. Obviously, a number of users who signed
up never really intended to actively follow the course. Since MOOCs
have very low barriers for entry, being free of costs and openly avail-
able on the internet, some users might just have joined out of curios-
ity and soon after signing up forgot about it. Yet, as stated in the
problem description, a high number of users quits the course after
actively following it for the first weeks. These users obviously lost
the motivation to continue.
Chapter 3 provided an insight into what motivates users and a clas-
sification for users which allows to better analyze their needs. The
difference between extrinsic and intrinsic motivation as well as mo-
tivators and the concept of gamification were discussed. Chapter 4
then analyzed how motivational concepts translate to the educational
domain. The role of feedback as a way of fostering intrinsic moti-
vation was analyzed and different learning theories were presented.
Moreover, an initial concept for gamifying openHPI was outlined.
By combining results from these research findings, a stronger cohe- Combine research
findings to increase
joy of learning
sion between learners and xMOOC-Platforms may be fostered. Well
chosen motivation strategies can increase the joy of learning right
from the beginning of a xMOOC, which ultimately can have positive
effects on the completion rates within these courses (see chapter 2.4).
The following chapter will present different concepts on how to
increase user participation and engagement and therefore raise the
learner’s overall motivation. As described in section 2.4, the success
in MOOCs is closely connected to the learner’s engagement on the
platform. But in traditional MOOC platforms, such engagement is
restricted to the forums, in which only 10% of all participants actively
engage.
5.1 increase bonding through personal feedback
Massive Open Online Courses have often been criticized that for the Feedback in
xMOOCs?sheer mass of students, individual mentoring would be impossible
(see chapter 2.4). Yet, findings from SDT have shown, that intrinsic
38

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