  Educational technology has obtained great importance over the last fifteen
years. At present, the umbrella of educational technology incorporates
multitudes of engaging online environments and fields. Learning analytics and
Massive Open Online Courses (MOOCs) are two of the most relevant emerging
topics in this domain. Since they are open to everyone at no cost, MOOCs excel
in attracting numerous participants that can reach hundreds and hundreds of
thousands. Experts from different disciplines have shown significant interest
in MOOCs as the phenomenon has rapidly grown. In fact, MOOCs have been proven
to scale education in disparate areas. Their benefits are crystallized in the
improvement of educational outcomes, reduction of costs and accessibility
expansion. Due to their unusual massiveness, the large datasets of MOOC
platforms require advanced tools and methodologies for further examination. The
key importance of learning analytics is reflected here. MOOCs offer diverse
challenges and practices for learning analytics to tackle. In view of that,
this thesis combines both fields in order to investigate further steps in the
learning analytics capabilities in MOOCs. The primary research of this
dissertation focuses on the integration of learning analytics in MOOCs, and
thereafter looks into examining students' behavior on one side and bridging
MOOC issues on the other side. The research was done on the Austrian iMooX
xMOOC platform. We followed the prototyping and case studies research
methodology to carry out the research questions of this dissertation. The main
contributions incorporate designing a general learning analytics framework,
learning analytics prototype, records of students' behavior in nearly every
MOOC's variables (discussion forums, interactions in videos, self-assessment
quizzes, login frequency), a cluster of student engagement...
