"Modeling Communities in Information Systems: Informal Learning Communities in Social Media" Von der Fakultät für Mathematik, Informatik und Naturwissenschaften der RWTH Aachen University zur Erlangung des akademischen Grades einer Doktorin der Naturwissenschaften genehmigte Dissertation vorgelegt von Zinayida Kensche, geb. Petrushyna, MSc. aus Odessa, Ukraine Berichter: Universitätsprofessor Professor Dr. Matthias Jarke Universitätsprofessor Professor Dr. Marcus Specht Privatdozent Dr. Ralf Klamma Tag der mündlichen Prüfung: 17.11.2015 Diese Dissertation ist auf den Internetseiten der Universitätsbibliothek online verfügbar. i c Copyright by 2016 All Rights Reserved ii Abstract Information modeling is required for creating a successful information system while modeling of communities is pivotal for maintaining community information systems (CIS). Online social media, a special case of CIS, have been intensively used but not usually adopted for learning community needs. Thus community stakeholders meet problems by supporting learning communities in social media. Under the prism of Community of Practice theory, such communities have three dimensions that are responsible for community sustainability: mutual engagement, joint enterprises and shared repertoire. Existing modeling solutions use either perspectives of learning theories, or anal- ysis of learner or community data captured in social media but rarely combine both approaches. Therefore, current solutions produce community models that supply only a part of community stakeholders with information that can hardly describe community success and failure. We also claim that community models must be created based on community data analysis integrated with our learning community dimensions. More- over, the models need to be adapted according to environmental changes. This work provides a solution to continuous modeling of informal learning com- munities in social media. In particular, it makes the following contributions: 1. A metamodel of learning communities and its specific cases in social media. 2. A pro- cess of continuous community model creation that consists of four phases that model, refine, monitor and analyze learning communities. The phases and their realizations can be used to model any learning community with the purpose to support commu- nity evolution and to improve social media facilities to satisfy community needs. 3. Methods for community data analysis and storage have been exploited for retrieving learning community states to manage competences in a collaborative space and spec- ifying culturally sensitive requirements of communities towards social media. 4. Our formal representation of a learning community has been used to model early require- ments of learning communities and their evolution and to validate the effectiveness of possible community changes through multi-agent simulation. iii iv Zusammenfassung Fur¨ die Entwicklung erfolgreicher Informationssysteme ist Informationsmodellierung notwendig wahrend¨ die Modellierung von Gemeinschaften fur¨ die Pflege und Weiter- entwicklung von Community-Informationssystemen (CIS) eine entscheidende Stutze¨ bilden kann. Soziale Online-Medien, ein Spezialfall von CIS, werden haufig¨ zur Un- terstutzung¨ von Lerngemeinschaften verwendet obwohl sie ohne Anpassung nicht zu diesem Zweck geeignet sind. Durch das Prisma der Theorie uber¨ praxisbezogene Gemeinschaften betrachtet haben solche Gemeinschaften drei Dimensionen die ihre Zukunftsfahigkeit¨ bestimmen: gegenseitige Verbindlichkeit, ein gemeinsames Unter- fangen, sowie den Zugriff auf das gleiche Repertoire an Ressourcen. Existierende Modellierungslosungen¨ greifen entweder auf die Perspektive der Lern- theorien zuruck¨ oder basieren auf einer Analyse von Lern- oder Gemeinschaftsdaten in Sozialen Medien aber kombinieren nur selten beide Ansatze.¨ Daher fuhren¨ bestehende Losungen¨ zu Gemeinschaftsmodellen die nur einen Ausschnitt der Interessengruppen mit notwendigen Informationen versorgen und kaum hinreichend sind um Erfolg und Scheitern von Gemeinschaften zu erklaren.¨ Stattdessen mussen¨ Gemeinschaftsmodelle auf Basis einer Integration von Datenanalysen sowie der Dimensionen von Lernge- meinschaften erzeugt sowie an sich andernde¨ Umgebungen angepasst werden. Diese Arbeit stellt eine Losung¨ fur¨ die kontinuierliche Modellierung von informel- len Lerngemeinschaften in sozialen Medien dar. Insbesondere leistet sie die folgen- den Beitrage:¨ 1. Ein Metamodell fur¨ Lerngemeinschaften und Spezialfalle¨ dieses Modells in sozialen Medien. 2. Einen Prozess zur Erzeugung und kontinuierlichen Weiterentwicklung von Gemeinschaftsmodellen der aus vier Phasen besteht: Model- lierung, Verfeinerung, Beobachtung und Analyse. Die Phasen und ihre Umsetzung konnen¨ zur Modellierung beliebiger Lerngemeinschaft zur Unterstutzung¨ ihrer Evo- lution und zur Weiterentwicklung der Funktionalitat¨ sozialer Medien genutzt werden um so Anforderungen der Gemeinschaften zu erfullen.¨ 3. Es wurde ein Verfahren zur Analyse und Speicherung von Gemeinschaftsdaten genutzt das es ermoglicht¨ Eigen- schaften von Lerngemeinschaften zu erkennen und so die Entwicklung von Kompe- tenzen in kollaborativen Umgebungen sowie die Spezifikation kulturell sensitiver An- forderungen an soziale Medien ermoglicht.¨ 4. Die formale Darstellung von Lernge- meinschaften wurde zur Modellierung fruher¨ Anforderungen von Gemeinschaften und ihre weitere Entwicklung genutzt. Die Effektivitat¨ moglicher¨ Anderungen¨ an Gemein- schaften wurde mit Hilfe von Multiagentensimulationen verifiziert. v vi Acknowledgments This work finalizes an important part of my life and I could not close it without naming the people that I want to thank for their advice and support. First of all, I want to thank my principal advisor Prof. Matthias Jarke for precious discussions on the contents of this work and for giving me the opportunity to freely research on a challenging topic which I enjoyed a lot. I would like to specially thank PD Dr. Ralf Klamma who served as coadvisor and gave major advice and recommen- dations that helped me to finish the dissertation work. Moreover, I thank Prof. Marcus Specht for his willingness to become my second advisor for this thesis. During my work I cooperated with my colleagues and friends at the chair, par- ticularly, Anna Hannemann, Yiwei Cao, Manh Cuong Pham, Dominik Renzel, Istvan Koren, Milos Kravcik, Dejan Kovachev, Mohsen Shahriari, Dominik Schmitz, Petru Nicolaescu, Michael Derntl, Katya Neulinger, Sandra Geisler, Stefan Schiffer, Daniele Glockner, Gabriele Hoeppermanns, Tatiana Liberzon, Reinhard Linde, Chao Li. I want to thank them for inspiring, interesting, and often funny academic and non-academic discussions as well as for our fine team work in teaching activities. Moreover, I thank my former students including Julian Krenge, Florian Oberloer, Alexander Ruppert, Er- gang Song, Alex Tritthart and many others for their support in realizing parts of this work. This research would not have been possible without the funding by the TEMPUS project on Cairo University E-learning Center, the EU FP7 project on Responsive Open Learning Environments, the Lifelong Learning project on Teacher’s Lifeling Learning Network and further support from BIT Research School and RWTH Aachen Univer- sity. I thank my colleagues in these projects for years of collaborative research, partic- ularly, Prof. Katherine Maillet, Prof. Magda Fayek, Prof. Martin Wolpers, Alexander Nussbaumer, Felix Modritscher,¨ Riina Vuokari, Prof. Peter Sloep, Mark Kroll¨ and Prof. Markus Strohmaier. Furthermore, I give my thanks to organizers and students I collaborated with at Joint Technology Enhanced Learning summer schools includ- ing Prof. Ambjorn¨ Naeve, Nicolas Weber, Sandy El Helou, Joris Klerkx, Anna Lea Dyckhoff, Hannes Ebner, Fridolin Wild and others. Last but not least, I thank my daughter, Helena, for giving me time for writing, my husband, David, for taking the load off and making relevant comments for the work, my parents and family for support from the beginning till the end of this thesis. vii viii Contents Abstract iii Zusammenfassung v Acknowledgments vii 1 Introduction 1 1.1 Research Questions and Methods . .2 1.2 Approaches and Contributions . .3 1.3 Outline of the Dissertation . .6 2 Background and State of the Art 9 2.1 Context of Learning Theories . .9 2.1.1 Networked Learning . 10 2.1.2 Self-Regulated Learning . 11 2.1.3 A New Era for Learning Communities . 11 2.2 Modeling Learning Communities . 13 2.2.1 Terminology . 13 2.2.2 Monitoring and Storage of Data . 14 2.2.2.1 Monitoring Learning Community Data . 14 2.2.2.2 Data Management Solutions for Learning Commu- nity Data . 15 2.2.3 Mirroring Tools for Learning . 17 2.2.4 Guiding Tools for Learning . 17 2.2.4.1 Discourse Analysis . 18 2.2.4.2 Activities . 18 2.2.4.3 Learning Groups . 19 2.2.4.4 Collaborations . 20 2.2.4.5 Social Media and Massive Open Online Courses . 20 2.2.5 Modeling of Collaborative Learning . 21 2.3 Information Systems Background . 22 2.3.1 Information Modeling Essentials . 22 2.3.2 Modeling Approaches . 23 ix 2.3.2.1 i* Modeling of Social Media . 24 2.4 Summary . 26 3 Supporting Learning Community Needs 29 3.1 Modeling Learning Communities . 32 3.1.1 A General Community Model . 32 3.1.2 Specific Community Models . 34 3.2 Refinement . 37 3.3 Monitoring Social Media Learning Communities . 38 3.3.1 Data . 38 3.3.1.1 Forums . 39 3.3.1.2 Collaborative Space eTwinning . 40 3.3.1.3 Wikipedia . 41 3.3.2 Mediabase Model . 41 3.3.3 Multidimensional Data Model