Web Analytics as Extension for a Learning Analytics Dashboard of a Massive Open Online Platform
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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.
KeywordsMOOC Learning Analytics Learning dashboard Online learning Visualization
- Arapakis, I., Lalmas, M., & Valkanas, G. (2014). Understanding within-content engagement through pattern analysis of mouse gestures. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. CIKM’14 (pp. 1439–1448).Google Scholar
- Blikstein, P. (2013). Multimodal learning analytics. In Proceedings of the 3rd International Conference on Learning Analytics and Knowledge. LAK´13 (pp. 102–106).Google Scholar
- Dawson, S., Gašević, D., Siemens, G., & Joksimovic, S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. In Proceedings of the 4th International Conference on Learning Analytics and Knowledge (pp. 231–240).Google Scholar
- Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a DELICATE issue a checklist for trusted learning analytics. In Proceedings of the 6th International Conference on Learning Analytics & Knowledge (pp. 89–98).Google Scholar
- Duval, E. (2011). Attention please! Learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 9–17).Google Scholar
- Elias, T. (2011). Learning analytics: The definitions, the processes, and the potential.Google Scholar
- Huang, J., White, R., & Buscher, G. (2012). User see, user point: Gaze and cursor alignment in web search. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI’12 (pp. 1341–1350).Google Scholar
- Jivet, I. (2016). The learning tracker a learner dashboard that encourages self-regulation in MOOC learners.Google Scholar
- Khalil, M., & Ebner, M. (2015). Learning analytics: Principles and constraints. In Proceedings of ED-Media 2015 conference.Google Scholar
- Khalil, M., & Ebner, M. (2016a). When learning analytics meets MOOCs-a review on iMooX case studies. In International Conference on Innovations for Community Services (pp. 3–19).Google Scholar
- Khalil, M., Taraghi, B., & Ebner, M. (2016). Engaging learning analytics in MOOCS: The good, the bad, and the ugly.Google Scholar
- Kocher, P., Genkin, D., Gruss, D., Haas, W., Hamburg, M., Lipp, M., Mangard, S., Prescher, T., Schwarz, M., & Yarom, Y. (2018). Spectre attacks: Exploiting speculative execution. In 40th IEEE Symposium on Security and Privacy S&P’19.Google Scholar
- Kopp, M., & Ebner, M. (2015). iMooX – Publikationen rund um das Pionierprojekt. Weinitzen: Verlag Mayer.Google Scholar
- Leitner, P., & Ebner, M. (2017). Development of a dashboard for learning analytics in higher education. In International Conference on Learning and Collaboration Technologies (pp. 293–301).Google Scholar
- Leitner, P., Khalil, M., & Ebner, M. (2017). Learning analytics in higher education – A literature review. In Learning analytics: Fundaments, applications, and trends.Google Scholar
- Maier, K., Leitner, P., & Ebner, M. (2019). Learning analytics cockpit for MOOC platforms. In Emerging trends in learning analytics.Google Scholar
- McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010) Massive Open online courses digital ways of knowing and learning, The MOOC model For Digital Practice. Retrieved from: http://davecormier.com/edblog/wp-content/uploads/MOOC_Final.pdf. Last accessed Oct 2019.
- Rohloff, T., Oldag, S., Renz, J., & Meinel, C. (2019). Utilizing web analytics in the context of learning analytics for large-scale online learning. In 2019 IEEE Global Engineering Education Conference EDUCON (pp. 296–305).Google Scholar
- Romanowski, B., & Konak, A. (2016). Using Google analytics to improve the course website of a database course. https://www.hofstra.edu/pdf/academics/colleges/seas/asee-fall-2016/asee-midatlantic-f2016-konak.pdf. Last accessed 4 Oct 2019.
- Spikol, D., Prieto, L. P., Rodríguez-Triana, M. J., Worsley, M., Ochoa, X., & Cukurova, M. (2017). Current and future multimodal learning analytics data challenges. In Proceedings of the 7th International Learning Analytics & Knowledge Conference (pp. 518–519).Google Scholar