Exploring engagement profiling in MOOCs through Learning Analytics: The Open edX Case
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- Master theses 
The enormous amount of data being generated daily, requires effective and efficient ways of processing and analysing in order to extract useful information and form meaningful conclusions. Learning Analytics is a set of methodologies and practices that uncover such information from educational data. The research in this thesis explores the addition of a Learning Analytics feature to the context of a Learning Analytics tool that aids instructors using the online Massive Open Online Course (MOOC) platform, Open edX. This is done through the development and evaluation of a working artefact that supports profiling of students according to their activity throughout the course, alongside the visualizations, which represent said activity. As a result, the thoroughly demonstrated process of the artefact creation and feedback collection from the instructors shows the potential of Learning Analytics methods when applied to Open edX tracking data. Several practical features for creating different engagement groups, together with the visualizations, are conceptualized, implemented and evaluated, and are positively assessed by the target group of instructors. In addition, the challenges that were encountered in the period of the development, are presented, together with the suggestions to overcome them. Finally, a few extra features are outlined for future work, which could expand the existing functionality even more and bring additional knowledge to this research area.