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...
Figure 1. The prototyping methodology (Budde et al., 1992;Carr & Verner, 1997) ...................................... 8 Figure 2. The research methodology process followed in this thesis. Methodology trails studies of (Budde et al., 1992) and (Yin, 2003) (Elton, 1996) ........ Table 1. OER popular organization definitions allocated rights (Creative Commons, 2016b) ..................... Table 2. The analyzed xMOOCs and their providers in the evaluation grid study (Brunner, 2014) ............. Table 3. Categories, subcategories and criteria of xMOOCs (Brunner, 2014) 4. The xMOOCs evaluation grid (Brunner, 2014)
The accessibility of the distance learning movement has gained much impetus over the last few years. Some time ago, Open CourseWare was introduced, and this enabled the promotion and growth of open and free online learning as we now know it. Following the debut of Open
CourseWare, the educational technology community witnessed the onset of new courses with massive student numbers which are open for all and available online. These types of courses are called “Massive Open Online Courses,” more commonly known as “MOOCs” (McAuley et al., 2010).
The growth of MOOCs in the modernistic era of online learning is bolstered by the millions of participants from all over the world who choose to enroll in these massive courses. MOOCs bring revolutionary innovation to elementary education as well as to higher education (Khalil & Ebner, 2015a). In fact, the number of available MOOCs has exploded in recent years with over 4,500 courses provided by renowned universities across the USA and Europe.
The first rendition of the MOOC movement was developed by George Siemens and Stephan Downes. These courses are commonly referred to as cMOOCs (Holland & Tirthali, 2014). Their original MOOC was based on the connectivism theory of networking information over social channels. Thereafter, other versions of MOOCs emerged, such as xMOOCs or extended MOOCs, and xMOOCs became more popular. Due to their newfound popularity, xMOOCs were adopted broadly across MOOC providers. One of the most prominent and successful xMOOCs was offered by Stanford University professor Sebastian Thrun in 2011. His group launched an online course called “Introduction to Artificial Intelligence” that attracted over 160,000 students (Yuan & Powell, 2013). The introduction of this first xMOOC proved that providing free learning sessions that are taught by experts from prominent universities in a ubiquitous context drives large numbers of learners from heterogeneous backgrounds to join MOOCs (Alario-Hoyos et al., 2013).
MOOCs have the potential to advance education in many different fields and subjects. A report by Online Course Report (2016) showed that computer science and programming represented the largest percentage of offered MOOCs. Substantial growth of MOOCs has also been seen in Science, Technology, Engineering, and Mathematics (STEM) fields. The anticipated success of MOOCs vary between business purposes such as saving costs and scenarios of improvement involving the pedagogical and educational concepts of online learning (Alario-Hoyos et al., 2013;Khalil & Ebner, 2016e). In addition, their benefits include the improvement of educational outcomes and the extension of accessibility and reach. Another advantage of MOOCs is their long-term ability to contribute to lifelong learning as well as Technology Enhanced Learning (TEL) contexts (Ebner et al., 2014).
Although MOOCs have created a revolution in online education, they continue to suffer from high attrition rates among registered users. Dropout rates can go up to 95% (Daniel, 2012); because of this, MOOC attrition is a considerable area of concern. Reasons behind the profound risk of dropout can be explained by the free nature of MOOCs (Alario-Hoyos et al., 2013), student ability to self-regulate their learning (Zimmerman, 2000), or personal reasons. In addition, MOOCs face the universal educational challenge of keeping students engaged and motivated (XU & YANG, 2016). For instance, it has been found that student motivation decreases significantly following the first weeks of a MOOC (Lackner, Ebner, & Khalil, 2015). This factor has created an abundance of research questions with respect to patterns of engagement and categorization of students in MOOCs (Kizilcec, Piech, & Schneider, 2013;Alario-Hoyos et al., 2016;Ferguson & Clow, 2015;Khalil & Ebner, 2015a). Furthermore, the lack of interaction between learners and instructor(s), and the controversial argument regarding MOOCs’ pedagogical approach act as roadblocks to the advancement of MOOCs. As a consequence of these challenges, research studies in MOOCs from surveys to case studies have heavily increased in the last five years (Liyanagunawardena, Adams, & Williams, 2013;Yousef et al., 2014;Kloos et al., 2014). In fact, the large data sets generated by student interactions in
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