Abstract
Recent major advances in Information Technologies are leading to an entirely new era in the educational process, which is characterized by the development of more engaging and human-like computer-based learning, personalization and incorporation of artificial intelligence techniques. A new research discipline, termed Learning Analytics, is emerging and examines the collection and intelligent analysis of learner and instructor data with the goal to extract information that can render electronic and/or mobile educational systems more personalized, engaging, dynamically responsive and pedagogically efficient. In this volume, internationally established authors are contributing their research ideas and results towards aspects of Learning Analytics with the purpose to (1) measure Student Engagement, to quantify the Learning Experience and to facilitate Self-Regulation, (2) to predict Student Performance (3) to be incorporated in Tools for Building Learning Materials and Educational Courses, and (4) to be used as Tools to support Learners and Educators in Synchronous and Asynchronous e-Learning.
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Virvou, M., Alepis, E., Tsihrintzis, G.A., Jain, L.C. (2020). Machine Learning Paradigms. 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_1
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