Learning Analytics in Education for the Twenty-First Century

Kristof De Witte*, Marc Andre Chenier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

Abstract

The online traces that students leave on electronic learning platforms; the improved integration of educational, administrative and online data sources; and the increasing accessibility of hands-on software allow the domain of learning analytics to flourish. Learning analytics, as in interdisciplinary domain borrowing from statistics, computer sciences and education, exploits the increased accessibility of technology to foster an optimal learning environment that is both transparent and cost-effective. This chapter illustrates the potential of learning analytics to stimulate learning outcomes and to contribute to educational quality management. Moreover, it discusses the increasing emergence of large and accessible data sets in education and compares the cost-effectiveness of learning analytics to that of costly and unreliable retrospective studies and surveys. The chapter showcases the potential of methods that permit savvy users to make insightful predictions about student types, performance and the potential of reforms. The chapter concludes with recommendations, challenges to the implementation and growth of learning analytics.
Original languageEnglish
Title of host publicationHandbook of Computational Social Science for Policy
EditorsEleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespe
PublisherSpringer International Publishing
Pages305-326
Number of pages22
ISBN (Electronic)9783031166242
ISBN (Print)9783031166235
DOIs
Publication statusPublished - 1 Jan 2023

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