TY - JOUR A1 - Caspari-Sadeghi, Sima T1 - Learning assessment in the age of big data: Learning analytics in higher education T2 - Cogent Education N2 - Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that educa-tion lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper con-cludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education. KW - Big data KW - learning analytics KW - technology-enhanced assessment Y1 - 2022 UR - https://opus4.kobv.de/opus4-uni-passau/frontdoor/index/index/docId/1223 UR - https://nbn-resolving.org/urn:nbn:de:bvb:739-opus4-12236 VL - 2023 IS - Volume 110, issue 1 PB - Taylor & Francis ER -