Abstract
Todays, learning analytics is a vital component in the concept of intelligent education. Analytical tools give many benefits for educators and enable a smart educational process. Our research hypothesis is that learning analytics will provide essential information for achieving adaptable learning paths, better learning performance, and as a result – a more efficient educational process. The paper aims to present a survey of the used learning analytics tools in contemporary education and their applicability for personalization and learning in groups. It also shows how clustering methods can be applied for grouping students with similar learning performance or interests to support learning in groups. Another issue that is considered is how learning analytics can work together with knowledge-based intelligent approaches to achieve personalized and adaptive tutoring.
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Acknowledgments
This research is supported by the Bulgarian FNI fund through the project “Modeling and Research of Intelligent Educational Systems and Sensor Networks (ISOSeM)”, contract КП-06-H47/4 from 26.11.2020.
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Ivanova, M., Terzieva, V., Ivanova, T., Todorova, K. (2022). Learning Analytics - Survey and Practical Considerations for Intelligent Education. In: Auer, M.E., Tsiatsos, T. (eds) New Realities, Mobile Systems and Applications. IMCL 2021. Lecture Notes in Networks and Systems, vol 411. Springer, Cham. https://doi.org/10.1007/978-3-030-96296-8_22
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