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 language | English |
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Title of host publication | Handbook of Computational Social Science for Policy |
Editors | Eleonora Bertoni, Matteo Fontana, Lorenzo Gabrielli, Serena Signorelli, Michele Vespe |
Publisher | Springer International Publishing |
Pages | 305-326 |
Number of pages | 22 |
ISBN (Electronic) | 9783031166242 |
ISBN (Print) | 9783031166235 |
DOIs | |
Publication status | Published - 1 Jan 2023 |