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Big data in education: Balancing the benefits of educational research and student privacy

Big data in education: Balancing the benefits of educational research and student privacy
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  B IG   D  ATA    IN   E DUCATION Balancing the Benets of Educational Research and Student Privacy   A Workshop Summary   1 Ths s a crca me o undersand he beneis and rsks of educaona research usng arge daa ses. Massve quanes of educaona daa can now be sored, anayzed, and shared. Sae ongudna daa sysems can rack ndvdua sudens from pre-K hrough coege and work. Dsrcs and schoos keep deaed daa on ndvdua academc performance, behavor, and educaona needs. Schoos provde poras for parens o check suden assgnmens and grades. Sotware deveopers and researchers coec daa from appcaons used for nsrucon ha ncude keysroke-eve nformaon abou suden decson makng. INTRODUCTION Meanme, “bg daa” are becomng a “bg concern” o parens and prvacy advocaes who worry ha suden nformaon w be paced “n unreabe hands or pu o nefarous uses.” 1 Commony voced concerns focus on prvacy breaches, hackng, he use of daa by commerca sotware deveopers for markeng purposes, and he possby ha sensve nformaon (e.g., regardng earnng dsabes, behavor probems, or es scores) mgh m fuure opporunes for sudens. These prvacy concerns have nspred some major pocy shts. Emergng sae and federa egsaon am o se ms on how much daa can be coeced, how ong hey are sored, and how hey are used for research, commerca, and oher purposes. A he federa eve, hese ncude cas for changes o he Famy Educaona Rghs and Prvacy Ac (FERPA), such as prohbng research ha s no amed a mprovng he nsrucon or esng of he specic sudens nvoved n he research, requrng parens o “op n” o a research, and requrng he deeon of suden records when a suden eaves an nsuon. 2  Saes have passed suden prvacy aws ha have varousy sough o resrc he ypes of nformaon ha can be coeced, m access o daa by hrd pares, and beef up sae agency prvacy safeguards. 3 As egsaors and he pubc dscuss sae and federa pocy changes, a fu accounng of educaona research beneis and rsks s necessary. Longudna daa sysems ha rack he progress of ndvdua sudens have enormous poena for umnang why some sudens hrve and ohers do no. There are many quesons o whch parens, praconers, and pocy makers woud ke answers ha can be expored by nkng admnsrave daa wh achevemen daa and rackng sudens over me. The use of earnng process daa s openng a wndow no suden hnkng and earnng n ways ha can subsanay mprove educaona oucomes. Ineracve echnooges are abe o record correc and ncorrec answers o quesons, response mes, sudens’ requess for hns, and he aspecs of he sotware wh whch sudens nerac. When such daa are nked o admnsrave daa, here s he promse of undersandng, wh greaer deph han ever before, how suden background and pror experences nerac wh earnng. Now personazed nsrucon can mprove movaon and earnng for hese sudens. Combnng admnsrave and earnng process daa can provde evdence abou cognve processes and nsrucona eecs ha were unmagned jus a decade ago. Researchers can use admnsrave daa and earnng process daa o nform poces ha mprove suden earnng and eachng for a sudens. 4  In order o undersand and mprove eachng and earnng, educaon daa mus be avaabe o researchers, and he prvacy of chdren and fames mus be proeced. A recen workshop hed by he Naona Academy of Educaon (NAEd), upon whch hs repor s based, revewed he beneis of educaona research usng modern daa sysems, he rsks o he prvacy of fames and chdren, and echnca and poca souons for maxmzng beneis and mnmzng rsks. For more nformaon on he workshop, ncudng a background paper, pane vdeos and summares and furher resources, vs naeducagdaa.  2 COMMON LANGUAGE Terms such as bg daa and prvacy are seen wh ncreasng frequency. There are weeky, f no day, news repors abou he use of arge-scae daa n research or as a means o sove rea-word probems. Prvacy concerns and secury breaches, ncudng he reease of personay deniabe nformaon, are aso wdey repored. Large governmen and prvae-secor daabases have been hacked, exposng bons of users o poena deny het; rused empoyees have soen cassied governmena nformaon and wdey dssemnaed ; and ceran organzaons prde hemseves on hackng no sysems and pubcy reeasng he nformaon. In debaes concernng educaona daa and prvacy, many common erms such as prvacy, secury, and conidenay are used nerchangeaby. Addonay, he scope and breadh of educaona bg daa are no aways fuy undersood. Foowng are deinons of hese erms n he conex of he dscusson, as we as a hepfu caegorzaon for usng hese erms n he educaon arena. big data privacy students schools educa on improving  learningteaching researcher       p      e      r      s      o      n      a        l trust access sharing inequies parents teachers researchtechnology IRBs breach security confidenality learning process administrave data demographic behavioral achievement aendance records test scores tall wide    f    i   n   e         i      n        t      e      r      v      a        l      s f ine-grained FERPA  COPPAPPRA standardized feedback cycle expectaons       g      u        i        d      a      n      c      e laws  states federal  3   Big data  are exremey arge daa ses ha defy radona daa-processng appcaons. Bu wha are “exremey arge” or “radona daa-processng appcaons”? In a much-ced arce, Laney (2001) descrbes bg daa n erms of voume, veocy, and varey. 5  In he educaon conex, hs refers o he “numbers of suden observaons, he frequency of observaons, and he number of ypes of observaons, respecvey.” 6   Moreover, n he educaona conex, bg daa ypcay ake he form of admnsrave daa and earnng process daa, wh each oerng her own promse for educaona research as we as rasng her own prvacy concerns. The beneis of bg daa for educaona research oten arse when daa ses are combned and merged. For exampe, earnng process daa, combned wh admnsrave daa such as demographcs and es scores, can provde nsghs no how o address educaona neques n faser feedback cyces. 7 What Are Big Data? Admnsrave Daa 8  are demographc, behavoral, and achevemen daa colleced hrough schools, governmenal agences, and her conracors. Admnsrave daa may conss of aendance records, es scores, ranscrps, and surveys. Educaonal admnsrave daa are colleced over many parcpans, oten longudnally a prescrbed, regular nervals (e.g., bannually or yearly). Examples of admnsrave daa ha are consdered “bg daa” are census daa, Naonal Assessmen of Educaonal Progress daa, nernaonal es scores, sae sandardzed es scores, and behavoral daa such as hose requred o be mananed and colleced for he U.S. Deparmen of Educaon’s Cvl Rghs Daa Collecon. Administrative Data   Learnng Process Daa 9  are connuous or near-connuous, ine-graned records, usually of dgal neracons of suden behavors o llumnae learnng processes. 10  Learnng process daa are “bg” daa because hey are “all” (nclude many parcpans); “wde” (nclude a large number of varables abou any one ndvdual); “ine” (nclude mulple ine-graned observaons aken across small me nervals); and “deep” (heorecally coded n a meanngful way). 11 Examples of learnng process daa are daa colleced n onlne assessmens and courses (ncludng massve open onlne courses [MOOCs]) or keysrokes and me laences colleced for neracve echnologes for K-12 sudens n a school year. Learning Process Data Educational Big DataAdministrativeLearningProcess  4 What Is Data Privacy? In he educaon conex, concerns abou daa prvacy oten focus on suden nformaon fang no he wrong hands or beng used for nefarous purposes. Ohers besdes sudens and parens may be ened o daa prvacy; dependng on he naure of he daa, hs coud ncude eachers, schoos, dsrcs, or coeges. Addonay, erms such as conidenay and daa secury are oten conlaed. Whe conidenay and secury low from prvacy, her scope s dependen on prvacy expecaons. DATA PRIVACY. Accordng o he U.S. Deparmen of Heah and Human Servces’ Oce for Human Research Proecons (OHRP), prvacy s “havng conro over he exen, mng, and crcumsances of sharng onesef (physcay, behavoray, or neecuay) wh ohers.” 12 Daa prvacy, aso caed nformaon prvacy, addresses he aby of an organzaon or ndvdua o deermne wha daa abou hem n a compuer sysem can be shared. CONFIDENTIALITY.  Once nformaon s shared, conidenay “perans o he reamen of nformaon ha an ndvdua has dscosed n a reaonshp of rus and wh he expecaon ha  w no be dvuged o ohers n ways ha are nconssen wh he undersandng of he orgna dscosure whou permsson.” 13   DATA SECURITY.  I hen foows ha daa secury s essena o prvacy as a means of “prevenng unauhorzed access o daa and ncudes sandards ha can be foowed o manan proper access o daa.” 14   SECURITY BREACH. A secury breach s he oss, het, or oher unauhorzed access o daa conanng sensve persona nformaon ha resus n he poena compromse of he conidenay of he daa. A secury breach ha voaes exsng aws can be nenona and unnenona.Whe unawfu secury breaches are consdered rare, a daa ncden ha does no rse o he eve of an ega breach can be as harmfu as an acua breach o he pubc percepon of daa coecon and sorage and consequeny research. A daa ncden can voae secury bes pracces bu s be whn he aw or  can break rus and expecaons. The pubc oten does no dsngush beween an unawfu breach and one ha voaes her rus. 15 Sakehoders, ncudng hose who have a prvacy neres, and daa coecors and users, ncudng saes, dsrcs, schoos, vendors, and researchers, mus work ogeher o ensure he approprae sharng of nformaon o benei educaona oucomes and opporunes. To accompsh hs hey mus work ogeher o form a “soca conrac” wh “negoaed norms of wha nformaon shoud be used and how” whn he educaona communy. 16 DataPrivacy Condentiality DataSecurity   SecurityBreach
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