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Analytics for Student Engagement

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Machine Learning Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 158))

Abstract

Volumes of detailed information are now unobtrusively collected as students use learning management systems and digital learning environments in their studies. This significantly elevates opportunities to better understand how students learn. The learning analytics community is exploring these data to describe learning processes [117] and ground recommendations for improved learning environments [8, 102, 139]. One challenge in this work is need for more and more detailed information about each student’s learning processes to mine for developing useful and timely feedback for students and teachers [150]. Student engagement is a strong focus in this work. Research consistently shows positive relationships between engagement and academic success (e.g., [68, 111]). However, to construct learning analytics describing student engagement and recommending more productive forms of engagement, the field needs to better understand what student engagement means, how it can be observed online and quantified, and how it relates to learning processes and achievement. We review literatures describing student engagement and its relations to learning focusing on engagement in online and distance learning in postsecondary education. We catalog conceptualizations, measurement approaches and benefits of student engagement for learning and academic achievement. Through lenses of learning analytics and learning science, we map the evolution of analytics about student engagement and propose future research to bridge the learning analytics—learning science gap. We note challenges of tracking and supporting student engagement and recommend strategies that are predicted to facilitate positive long-term change.

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Acknowledgements

This work was supported by a grants from the Social Sciences and Humanities Research Council of Canada (#435-2016-0379) and Simon Fraser University.

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    J. M. Vytasek, A. Patzak & P. H. Winne

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  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

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    Efthimios Alepis

  3. University of Piraeus, Pireas, Greece

    George A. Tsihrintzis

  4. Faculty of Science, Technology and Mathematics, University of Canberra, Canberra, SA, Australia

    Lakhmi C. Jain

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Vytasek, J.M., Patzak, A., Winne, P.H. (2020). Analytics for Student Engagement. 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_3

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