Abstract
Learning analytics brings considerable challenges in the field of e-learning. Researchers increasingly use the technological advancements emerging from learning analytics in order to support the digital education. The way learning analytics is used, can vary. It can be used to provide learners with information to reflect on their achievements and patterns of behavior in relation to others, or to identify students requiring extra support and attention, or to help teachers plan supporting interventions for functional groups such as course teams. In view of the above, this paper employs learning analytics and presents the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Furthermore, it presents a multi module model consisting of the identification of target material, curriculum improvement, cognitive states and behavior prediction and personalization in order to support learners and further enhance their learning experience. The evaluation results are very promising and show that learning analytics can bring new insights that can benefit learners, educators and administrators.
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Notes
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Convention Document. The Asilomar Convention for Learning Research in Higher Education. http://asilomar-highered.info/asilomar-convention-20140612.pdf (2014).
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Troussas, C., Krouska, A., Virvou, M. (2020). Using a Multi Module Model for Learning Analytics to Predict Learners’ Cognitive States and Provide Tailored Learning Pathways and Assessment. In: Virvou, M., Alepis, E., Tsihrintzis, G., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-13743-4_2
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