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Educational technology has obtained great importance over the last fifteenyears. At present, the umbrella of educational technology incorporatesmultitudes of engaging online environments and fields. Learning analytics andMassive Open Online Courses (MOOCs) are two of the most relevant emergingtopics in this domain. Since they are open to everyone at no cost, MOOCs excelin attracting numerous participants that can reach hundreds and hundreds ofthousands. Experts from different disciplines have shown significant interestin MOOCs as the phenomenon has rapidly grown. In fact, MOOCs have been provento scale education in disparate areas. Their benefits are crystallized in theimprovement of educational outcomes, reduction of costs and accessibilityexpansion. Due to their unusual massiveness, the large datasets of MOOCplatforms require advanced tools and methodologies for further examination. Thekey importance of learning analytics is reflected here. MOOCs offer diversechallenges and practices for learning analytics to tackle. In view of that,this thesis combines both fields in order to investigate further steps in thelearning analytics capabilities in MOOCs. The primary research of thisdissertation focuses on the integration of learning analytics in MOOCs, andthereafter looks into examining students' behavior on one side and bridgingMOOC issues on the other side. The research was done on the Austrian iMooXxMOOC platform. We followed the prototyping and case studies researchmethodology to carry out the research questions of this dissertation. The maincontributions incorporate designing a general learning analytics framework,learning analytics prototype, records of students' behavior in nearly everyMOOC's variables (discussion forums, interactions in videos, self-assessmentquizzes, login frequency), a cluster of student engagement...

Learning Analytics in Massive Open Online Courses