气候变化领域集成服务门户(CCPortal): Task-specific information outperforms surveillance-style big data in predictive analytics

CCPortal

DOI	10.1073/pnas.2020258118
	Task-specific information outperforms surveillance-style big data in predictive analytics
	Bjerre-Nielsen A.; Kassarnig V.; Lassen D.D.; Lehmann S.
发表日期	2021
ISSN	00278424
卷号	118 期号:14
英文摘要	Increasingly, human behavior can be monitored through the collection of data from digital devices revealing information on behaviors and locations. In the context of higher education, a growing number of schools and universities collect data on their students with the purpose of assessing or predicting behaviors and academic performance, and the COVID-19-induced move to online education dramatically increases what can be accumulated in this way, raising concerns about students' privacy. We focus on academic performance and ask whether predictive performance for a given dataset can be achieved with less privacy-invasive, but more task-specific, data. We draw on a unique dataset on a large student population containing both highly detailed measures of behavior and personality and high-quality third-party reported individual-level administrative data. We find that models estimated using the big behavioral data are indeed able to accurately predict academic performance out of sample. However, models using only low-dimensional and arguably less privacyinvasive administrative data perform considerably better and, importantly, do not improve when we add the high-resolution, privacy-invasive behavioral data. We argue that combining big behavioral data with "ground truth" administrative registry data can ideally allow the identification of privacy-preserving taskspecific features that can be employed instead of current indiscriminate troves of behavioral data, with better privacy and better prediction resulting. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Academic performance; Big data; Prediction; Privacy
语种	英语
scopus关键词	academic achievement; article; big data; human; human experiment; personality; prediction; privacy; education; learning; machine learning; student; Big Data; COVID-19; Education, Distance; Humans; Learning; Machine Learning; SARS-CoV-2; Students
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179975
作者单位	Department of Economics, University of Copenhagen, Copenhagen, 1353, Denmark; Center for Social Data Science, University of Copenhagen, Copenhagen, 1353, Denmark; Institute of Software Technology, Graz University of Technology, Graz, 8010, Austria; Center for Economic Behavior and Inequality, University of Copenhagen, Copenhagen, 1353, Denmark; DTU Compute, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
推荐引用方式 GB/T 7714	Bjerre-Nielsen A.,Kassarnig V.,Lassen D.D.,et al. Task-specific information outperforms surveillance-style big data in predictive analytics[J],2021,118(14).
APA	Bjerre-Nielsen A.,Kassarnig V.,Lassen D.D.,&Lehmann S..(2021).Task-specific information outperforms surveillance-style big data in predictive analytics.Proceedings of the National Academy of Sciences of the United States of America,118(14).
MLA	Bjerre-Nielsen A.,et al."Task-specific information outperforms surveillance-style big data in predictive analytics".Proceedings of the National Academy of Sciences of the United States of America 118.14(2021).