Achieving big data privacy in education

被引:43
作者
Reidenberg, Joel R. [1 ]
Schaub, Florian [2 ,3 ]
机构
[1] Fordham Univ, Fordham Law Sch, Ctr Law & Informat Policy, New York, NY 10023 USA
[2] Univ Michigan, Informat, Ann Arbor, MI 48109 USA
[3] Univ Michigan, Comp Sci & Elect Engn, Ann Arbor, MI 48109 USA
关键词
Big data; educational data mining; higher education; learning analytics; privacy;
D O I
10.1177/1477878518805308
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Education, Big Data, and student privacy are a combustible mix. The improvement of education and the protection of student privacy are key societal values. Big Data and Learning Analytics offer the promise of unlocking insights to improving education through large-scale empirical analysis of data generated from student information and student interactions with educational technology tools. This article explores how learning technologies also create ethical tensions between privacy and the use of Big Data for educational improvement. We argue for the need to demonstrate the efficacy of learning systems while respecting privacy and how to build accountability and oversight into learning technologies. We conclude with policy recommendations to achieve these goals.
引用
收藏
页码:263 / 279
页数:17
相关论文
共 46 条
[1]  
American Psychological Association, 2006, STER THREAT WID ACH
[2]   How to De-Identify Your Data [J].
Angiuli, Olivia ;
Blitzstein, Joe ;
Waldo, Jim .
COMMUNICATIONS OF THE ACM, 2015, 58 (12) :48-55
[3]  
[Anonymous], CENTRE
[4]  
Au, 2015, I S J LAW POLICY INF, V11, P325
[5]  
Ben Shahar TH, 2017, THEORY RES EDUC, V15, P306, DOI 10.1177/1477878517737155
[6]  
Ben-Porath S, 2017, THEORY RES EDUC, V15, P243, DOI 10.1177/1477878517737201
[7]  
Bhatia J., 2016, P IEEE 24 INT REQ EN
[8]  
Boninger F., 2016, LEARNING BE WATCHED
[9]  
Bulger M., 2017, LEGACY INBLOOM
[10]  
Bulger M., 2016, PERSONALIZED LEARNIN