Abstract
Massive open online courses (MOOCs) provide anyone with Internet access the chance to study at university level for free. In such learning environments and due to their ubiquitous nature, learners produce vast amounts of data representing their learning process. Learning Analytics (LA) can help identifying, quantifying, and understanding these data traces. Within the implemented web-based tool, called LA Cockpit, basic metrics to capture the learners’ activity for the Austrian MOOC platform iMooX were defined. Data is aggregated in an approach of behavioral and web analysis as well as paired with state-of-the-art visualization techniques to build a LA dashboard. It should act as suitable tool to bridge the distant nature of learning in MOOCs. Together with the extendible design of the LA Cockpit, it shall act as a future proof framework to be reused and improved over time. Aimed toward administrators and educators, the dashboard contains interactive widgets letting the user explore their datasets themselves rather than presenting categories. This supports the data literacy and improves the understanding of the underlying key figures, thereby helping them generate actionable insights from the data. The web analytical feature of the LA Cockpit captures mouse activity in individual course-wide heatmaps to identify regions of learner’s interest and help separating structure and content. Activity over time is aggregated in a calendar view, making timely reoccurring patterns otherwise not deductible, now visible. Through the additional feedback from the LA Cockpit on the learners’ behavior within the courses, it will become easier to improve the teaching and learning process by tailoring the provided content to the needs of the online learning community.
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Leitner, P., Maier, K., Ebner, M. (2020). Web Analytics as Extension for a Learning Analytics Dashboard of a Massive Open Online Platform. In: Ifenthaler, D., Gibson, D. (eds) Adoption of Data Analytics in Higher Education Learning and Teaching. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-47392-1_19
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