{"id":127,"date":"2020-09-03T16:40:05","date_gmt":"2020-09-03T13:40:05","guid":{"rendered":"http:\/\/acikkitap.com.tr\/oaek\/chapter\/bolum-28-ogrenci-mahremiyetinin-ihlali\/"},"modified":"2020-09-03T16:40:05","modified_gmt":"2020-09-03T13:40:05","slug":"bolum-28-ogrenci-mahremiyetinin-ihlali","status":"publish","type":"chapter","link":"https:\/\/acikkitap.com.tr\/oaek\/chapter\/bolum-28-ogrenci-mahremiyetinin-ihlali\/","title":{"raw":"B\u00f6l\u00fcm 28 \u00d6\u011frenci Mahremiyetinin \u0130hlali","rendered":"B\u00f6l\u00fcm 28 \u00d6\u011frenci Mahremiyetinin \u0130hlali"},"content":{"raw":"\n

Elana Zeide<\/span><\/span><\/p>\n

Bilgi Teknolojileri Politikas\u0131 Merkezi, Princeton \u00dcniversitesi, ABD Bilgi Toplumu Projesi, Yale Hukuk Fak\u00fcltesi, ABD Bilgi Hukuku Enstit\u00fcs\u00fc, New York \u00dcniversitesi Hukuk Fak\u00fcltesi, ABD<\/span><\/span><\/p>\n

DOI: 10.18608\/hla17.028<\/span><\/span><\/p>\n\n

\u00d6Z<\/h2>\n

Bu kitapta tart\u0131\u015f\u0131lan \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi b\u00fcy\u00fck umutlar vaat etmektedir. Bununla beraber, b\u00fcy\u00fck veri odakl\u0131 e\u011fitimin muhtemel sonu\u00e7lar\u0131 \u00f6zellikle g\u00fcvenlik ve mahremiyet konular\u0131nda endi\u015fe uyand\u0131rmaktad\u0131r. Bu b\u00f6l\u00fcmde, \u00f6\u011frenci verilerini d\u00fczenleyen yasal \u00e7er\u00e7evenin, \u00f6\u011frenme analiti\u011finin ve e\u011fitsel veri madencili\u011finin mahremiyet ihlali ve buna kar\u015f\u0131 geli\u015ftirilecek proaktif yakla\u015f\u0131mlar a\u00e7\u0131klanmaktad\u0131r. Hukuki d\u00fczenlemelere daha az; konuyla ilgili endi\u015fe ve \u00e7\u00f6z\u00fcm \u00f6nerilerine daha fazla yer verilmi\u015ftir. Geleneksel \u00f6\u011frenci mahremiyet hukuku, ebeveynlerin veya okullar\u0131n \u00f6\u011frenci bilgilerinin a\u00e7\u0131klanmas\u0131n\u0131n onaylamas\u0131n\u0131 sa\u011flamaya odaklan\u0131r. Bu d\u00fczenlemeler \"\u00f6\u011frenci verileri\" ne de\u011fil \"e\u011fitim kay\u0131tlar\u0131\u201dna uygulanacak \u015fekilde tasarlanm\u0131\u015ft\u0131r. Sonu\u00e7 olarak bundan b\u00f6yle anlaml\u0131 bir g\u00f6zetim sa\u011flamamaktad\u0131r. \u00d6nceki federal \u00f6\u011frenci mahremiyeti mevzuat\u0131, do\u011frudan \u00f6\u011frencilerden toplanan \"\u00f6\u011frenen\" verilerini korumamaktad\u0131r ve ayn\u0131 zamanda ihlal ile ilgili do\u011frudan bir yapt\u0131r\u0131m da uygulamamaktad\u0131r. Yeni mevzuat d\u00fczenlemesi de mahremiyetle ilgili korumalar\u0131 \u00f6ng\u00f6rmemi\u015f olmakla birlikte; genellikle belirli uygulamalar\u0131n felaket etkisi olu\u015fturmas\u0131n\u0131 \u00f6nlemeye veya verilerin \u201cticari\u201d kullan\u0131m\u0131n\u0131 s\u0131n\u0131rland\u0131rmaya \u00e7al\u0131\u015fmaktad\u0131r. Bu g\u00f6z ard\u0131 edilmi\u015f etik problemler b\u00fcy\u00fck verilerin e\u011fitim ama\u00e7l\u0131 kullan\u0131m\u0131 ile s\u0131n\u0131rland\u0131r\u0131lmas\u0131 halinde dahi varl\u0131\u011f\u0131n\u0131 s\u00fcrd\u00fcrmektedir. Bu makalede salt yasal metni a\u00e7\u0131klaman\u0131n \u00f6tesine ge\u00e7en ve daha geni\u015f sonu\u00e7lar\u0131 ve etik problemleri g\u00f6z \u00f6n\u00fcnde bulunduran, a\u00e7\u0131k inceleme protokolleri uygulayan, anlaml\u0131 bir \u015feffafl\u0131k sa\u011flayan ve algoritmik hesap verebilirli\u011fi temin eden proaktif bir yakla\u015f\u0131m \u00f6neriyorum.<\/span><\/span><\/p>\n

Anahtar Kelimeler<\/span>: KA\u00c7D'ler, sanal \u00f6\u011frenme ortamlar\u0131, ki\u015fiselle\u015ftirilmi\u015f \u00f6\u011frenme, \u00f6\u011frenci mahremiyeti, e\u011fitim verileri, ed tech, AEHMY, \u00d6\u00c7KBKY, veri eti\u011fi, ara\u015ft\u0131rma eti\u011fi<\/span><\/span><\/p>\n

\u00d6ncelikle bir uyar\u0131da bulunmak isterim ki: yasal d\u00fczenlemeler ve ihtiya\u00e7lar s\u00fcrekli olarak de\u011fi\u015fti\u011finden, bu b\u00f6l\u00fcmde yer alan a\u00e7\u0131klamalar uyum i\u00e7in bir rehber olarak kullan\u0131lmamal\u0131d\u0131r. Bunun yerine, insanlar\u0131n \u201c\u00f6\u011frenci mahremiyeti\u201d ba\u015fl\u0131\u011f\u0131 alt\u0131nda tart\u0131\u015ft\u0131klar\u0131 konular\u0131 ve s\u0131kl\u0131kla ihmal edilen daha da geni\u015f konular\u0131 d\u00fc\u015f\u00fcnmenin farkl\u0131 yollar\u0131n\u0131 sunar. Amerika Birle\u015fik Devletleri'nde mahremiyet kurallar\u0131 farkl\u0131 sekt\u00f6rlere g\u00f6re de\u011fi\u015fiklik g\u00f6stermektedir. \u00d6\u011frencilerin ki\u015fisel bilgilerin gizlili\u011fine y\u00f6nelik geleneksel yakla\u015f\u0131mlar, \u00f6zellikle Aile E\u011fitimi Haklar\u0131 ve Mahremiyet Yasas\u0131 (AEHMY)1<\/a><\/sup> okullar\u0131n e\u011fitim kay\u0131tlar\u0131nda tutulan ki\u015fisel bilgi olarak da tan\u0131mlanabilen \u00f6\u011frenci bilgilerine eri\u015fimi ve bunlar\u0131n eri\u015fimine izin konular\u0131n\u0131 d\u00fczenlemeye odaklan\u0131r. Sadece e\u011fitim konusunda yetkilendirilmi\u015f akt\u00f6rlerin kurumsal a\u00e7\u0131klamalarda kullan\u0131m amac\u0131 ile, ki\u015fisel olarak tan\u0131mlanan \u00f6\u011frenci bilgilerine eri\u015fimini sa\u011flamak i\u00e7in, a\u00e7\u0131k r\u0131za ve kurumsal g\u00f6zetim \u015fart\u0131 aranmaktad\u0131r. Bu yakla\u015f\u0131m, 1970'lerin ba\u015f\u0131ndan bu yana \u00e7o\u011fu gizlilik d\u00fczenlemesinin \u00f6z\u00fcnde yer alan Adil Bilgi Uygulama \u0130lkeleri -bildirim, se\u00e7im, eri\u015fim ve de\u011fi\u015ftirme hakk\u0131- ile uyumludur. Bu ilk d\u00f6nem mahremiyet kurallar\u0131 ayr\u0131ca \u00f6ncelikli olarak e\u011fitimcilerin toplama, kullanma veya e\u011fitim kay\u0131tlar\u0131n\u0131n tutulmas\u0131n\u0131 ele almadan \u00f6\u011frenci bilgilerinin a\u00e7\u0131klanmas\u0131na (kullan\u0131lmas\u0131na) odaklanmaktad\u0131r.<\/span><\/p>\n

\u00d6\u011frencilerin mahremiyetine ili\u015fkin yeni yakla\u015f\u0131mlar, belirli uygulamalar\u0131 yasaklamak veya \u201ce\u011fitsel\u201d ama\u00e7lara hizmet etmelerini istemek e\u011filimindedir. K\u00fcnt yasaklar genellikle e\u011fitim veri ekosistemi i\u00e7inde var olmak bir yana b\u00fcy\u00fcmeye ve yenili\u011fe destek olmak i\u00e7in bile \u00e7ok kabaca haz\u0131rlanm\u0131\u015ft\u0131r. \"E\u011fitim\" ama\u00e7l\u0131 yap\u0131lan k\u0131s\u0131tlamalar, \u00f6\u011frenci verilerinin a\u00e7\u0131k \u201cticari\u201d kullan\u0131m\u0131n\u0131 s\u0131n\u0131rlamakla birlikte e\u011fitim ama\u00e7l\u0131 olarak e\u011fitimciler taraf\u0131ndan kullan\u0131ld\u0131\u011f\u0131nda bile \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi sonucunda ortaya \u00e7\u0131kan daha ayr\u0131nt\u0131l\u0131 sorunlar\u0131n \u00fczerinde durulmamaktad\u0131r. B\u00fcy\u00fck verilerin e\u011fitime hizmet ama\u00e7l\u0131 kullan\u0131m\u0131n\u0131n e\u011fitimin b\u00fct\u00fcn payda\u015flar\u0131n\u0131n \u00e7\u0131karlar\u0131na uymayabilece\u011fi \u00f6ng\u00f6r\u00fclememi\u015ftir. Kati suretle yasaklay\u0131c\u0131 olarak \u015fekillenen mevzuat\u0131n, teknolojik de\u011fi\u015fimin h\u0131zl\u0131 ilerleyi\u015fi ve \u00f6\u011frenme alanlar\u0131nda son derece ba\u011flamsalla\u015fm\u0131\u015f karar verme yetisi ile e\u015fle\u015fmesi i\u00e7in yeterince esnek olmas\u0131 zordur. \u00d6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi y\u00f6ntemini kullanan veri bilimcileri ve karar vericiler, veri g\u00fcd\u00fcml\u00fc ara\u00e7lar\u0131n hedeflerine ula\u015fmas\u0131n\u0131, e\u011fitim sistemine fayda sa\u011flamas\u0131n\u0131 ve daha geni\u015f bir toplumda e\u015fitli\u011fi te\u015fvik etmesini sa\u011flamak i\u00e7in ihtiyatl\u0131 \u00f6ng\u00f6r\u00fc, \u015feffafl\u0131k ve hesap verebilirlik yoluyla uyumlulu\u011fun \u00f6tesine ge\u00e7melidir.<\/span><\/p>\n\n

E\u011e\u0130T\u0130M KAYDI MAHREM\u0130YET\u0130<\/h2>\n

\u00d6\u011frenci gizlili\u011fi panik dalgas\u0131 1960'lar\u0131n sonu, 1970'lerin ba\u015f\u0131nda meydana geldi. Okullar \u00f6\u011frenciler hakk\u0131nda daha geni\u015f bir dizi bilgi toplamaya ba\u015flad\u0131. E\u011fitimciler ve y\u00f6neticiler d\u00fczenli olarak \u00f6\u011frenci bilgilerini ge\u00e7ici olarak ve genellikle belgesiz olarak payla\u015ft\u0131lar (Divoky, 1974).<\/span><\/p>\n\n

AEHMY'nin (Aile E\u011fitimi Haklar\u0131 ve Mahremiyet Yasas\u0131) Okul A\u00e7\u0131klamalar\u0131na \u0130tiraz\u0131<\/h3>\n

Buna kar\u015f\u0131l\u0131k, Kongre, 1974'te \u00f6\u011frenci verilerini d\u00fczenleyen birincil federal mevzuat\u0131n\u0131 kabul etti. AEHMY, ebeveynler ve 18 ya\u015f\u0131ndan b\u00fcy\u00fck \"uygun \u00f6\u011frencilere\" orta\u00f6\u011fretim sonras\u0131 i\u00e7in \u00fc\u00e7 kay\u0131t hakk\u0131 tan\u0131maktad\u0131r. (\"ebeveyn\" olarak k\u0131salt\u0131lm\u0131\u015ft\u0131r). Federal olarak finanse edilen okullar, il\u00e7eler ve devlete ba\u011fl\u0131 e\u011fitim kurumlar\u0131, ebeveynlere e\u011fitim kurumu veya arac\u0131 kurum (\u201ce\u011fitim akt\u00f6rleri\u201d, k\u0131saltma olarak) taraf\u0131ndan tutulan e\u011fitim kay\u0131tlar\u0131na eri\u015fimini ve bunlar\u0131n do\u011frulu\u011funu sorgulama imk\u00e2n\u0131 sa\u011flamal\u0131d\u0131r. E\u011fitim akt\u00f6rleri, okullar\u0131n kendi ad\u0131na izin vermesine yetki veren bir\u00e7ok istisnaya tabi olarak, ki\u015fisel olarak tan\u0131mlanabilir \u00f6\u011frenci bilgilerini payla\u015fmadan \u00f6nce ebeveynlerin iznini de almal\u0131d\u0131r.2<\/a><\/sup><\/span><\/p>\n

AEHMY, e\u011fitim kurumlar\u0131 ve kurumlar taraf\u0131ndan ki\u015fisel olarak tan\u0131mlanabilir \u00f6\u011frenci bilgilerinin yasal e\u011fitim \u00e7\u0131karlar\u0131 olan onaylanm\u0131\u015f al\u0131c\u0131lara a\u00e7\u0131klanmas\u0131n\u0131n s\u0131n\u0131rland\u0131r\u0131lmas\u0131na odaklanmaktad\u0131r. AEHMY'nin ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131lamak i\u00e7in, okullar, birka\u00e7 istisnadan biri olmad\u0131k\u00e7a, bir \u00f6\u011frencinin e\u011fitim kayd\u0131nda tutulan ki\u015fisel olarak tan\u0131mlanabilir bilgileri payla\u015fmadan \u00f6nce ebeveynlerin yaz\u0131l\u0131 onay\u0131n\u0131 almal\u0131d\u0131r. Uygulamada, mahremiyetle ilgili en \u00f6nemli kararlar\u0131 ebeveynler ya da \u00f6\u011frencilerden ziyade e\u011fitimciler al\u0131r ve istisnalar kural haline gelir. (Zeide, 2016a).<\/span><\/p>\n\n

\u201cE\u011fitsel\u201d \u0130lgi Alanlar\u0131na \u0130li\u015fkin A\u00e7\u0131klama Yapmaya \u0130zin Veren Okullar<\/h3>\n

Okul yetkilisi istisnas\u0131, veri ile ilgili karar vermenin b\u00fcy\u00fck k\u0131sm\u0131n\u0131 okullara ve il\u00e7elere devretmektedir. Okullar, al\u0131c\u0131 : 1) Ger\u00e7ekle\u015ftirilecek hizmetleri kendi adlar\u0131na y\u00fcr\u00fctmek 2) G\u00f6r\u00fcn\u00fcrde bu bilgilere eri\u015fmek i\u00e7in \"yasal bir e\u011fitim ilgisi (menfaati) varsa 3) Bilgilerin korunmas\u0131 ile ilgili gerekli \u00f6nlemleri alm\u0131\u015fsa \"ki\u015fisel olarak tan\u0131mlanabilen\" \u00f6\u011frenci bilgilerini izin almadan payla\u015fabilir.3<\/a><\/sup> E\u011fitimciler, birinin bir okul g\u00f6revlisi olman\u0131n neyi nitelendirdi\u011fine ve neyin me\u015fru bir e\u011fitim \u00e7\u0131kar\u0131n\u0131 olu\u015fturdu\u011funa karar verir ancak bu terimleri herhangi bir ayr\u0131nt\u0131l\u0131 \u015fekilde tan\u0131mlamak zorunda de\u011fildir (ABD E\u011fitim Bakanl\u0131\u011f\u0131, t.y.). Sonu\u00e7 olarak, \u00e7o\u011fu, neredeyse her durumu kapsayacak kadar geni\u015f kriterlere dayanmaktad\u0131r (Zeide, 2016a). Okullar, veriye al\u0131p kullananlar\u0131n ayr\u0131nt\u0131l\u0131 bilgi uygulamalar\u0131n\u0131 kontrol etmek i\u00e7in nadiren aktif \u00f6nlemler al\u0131r, bunun yerine taraflar aras\u0131ndaki \u201cdo\u011frudan kontrol\u201d arac\u0131 olarak hizmet \u015fartlar\u0131na veya s\u00f6zle\u015fmelere dayan\u0131r (Reidenberg vd., 2013).<\/span><\/p>\n\n

\u00d6\u011frenci Verilerinin Yeniden Kullan\u0131lmas\u0131ndan Engellenen Ara\u015ft\u0131rmac\u0131lar<\/h3>\n

AEHMY, e\u011fitim akt\u00f6rlerinin ara\u015ft\u0131rmac\u0131larla nas\u0131l bilgi payla\u015ft\u0131\u011f\u0131 konusunda daha kat\u0131 \u015fartlar getirmektedir. \u00c7al\u0131\u015fmalar\u0131 belirlenmi\u015f istisnalar ve belirli \u015fartlarla alt\u0131nda yap\u0131lm\u0131\u015f yaz\u0131l\u0131 bir s\u00f6zle\u015fme uyar\u0131nca yapmak zorundad\u0131rlar. \u00c7al\u0131\u015fmalar \u201ckestirimci testlerin geli\u015ftirilmesi, do\u011frulanmas\u0131 veya y\u00f6netilmesi; \u00f6\u011frenci yard\u0131m programlar\u0131n\u0131 y\u00f6netmek veya talimat\u0131 geli\u015ftirmek amac\u0131yla olmal\u0131d\u0131r.4<\/a><\/sup>\u201d Ara\u015ft\u0131rmac\u0131lar, yaln\u0131zca belirli ama\u00e7lar i\u00e7in ki\u015fisel olarak tan\u0131mlanabilir \u00f6\u011frenci bilgilerini kullanabilir ve art\u0131k gerekli olmad\u0131\u011f\u0131nda verileri yok etmelidirler.<\/span><\/p>\n\n

\u0130taat Odakl\u0131 \u0130cra<\/h3>\n

E\u011fitim akt\u00f6rlerin AEHMY ihlalleri i\u00e7in do\u011frudan hesap verebilirli\u011fi yoktur. Mevzuat, belirli gizlilik ihlallerini \u00f6nlemek yerine bir yap\u0131 in\u015fa etmekle ilgilidir. Neticede, bireysel ihlal \u00f6rnekleri i\u00e7in sonu\u00e7lar do\u011furmaz. \u00d6\u011frenciler ve e\u011fitimciler mevzuat (ABD Y\u00fcksek Mahkemesi, 2002) kapsam\u0131nda ihlallere dava a\u00e7amazlar. Bunun yerine, ABD E\u011fitim Bakanl\u0131\u011f\u0131 (EB), bir e\u011fitim kurumu veya kurulu\u015fu uyumsuzlu\u011fun bir \u201cPolitikas\u0131 veya uygulamas\u0131\u201d varsa federal \u00f6\u011frenci kredileri \u015feklinde destek de d\u00e2hil olmak \u00fczere t\u00fcm federal fonlar\u0131 geri \u00e7ekme g\u00fcc\u00fcne sahiptir.5<\/a><\/sup> Bununla birlikte, Bakanl\u0131k, bu dramatik eylemi, t\u00fcz\u00fc\u011f\u00fcn k\u0131rk y\u0131l \u00f6nce y\u00fcr\u00fcrl\u00fc\u011fe girmesinden bu yana hi\u00e7bir zaman ger\u00e7ekle\u015ftirmedi (Zeide, 2016b; Daggett, 2008). Bu kadar sert bir \u00f6nlem, AEHMY'nin korumak istedi\u011fi \u00f6\u011frencileri incitece\u011finden, bakanl\u0131k bunun yerine e\u011fitim kurumlar\u0131n\u0131n itaat etmesine odaklan\u0131yor. EB'nin b\u00f6yle bir \u201cn\u00fckleer\u201d se\u00e7ene\u011fi (solove, 2012) takip etmesi pek olas\u0131 g\u00f6r\u00fcnm\u00fcyor.<\/span><\/p>\n\n

\u00d6\u011eRENC\u0130 VER\u0130 MAHREM\u0130YET\u0130<\/h2>\n

Neredeyse k\u0131rk y\u0131ld\u0131r, payda\u015flar a\u011f\u0131rl\u0131kl\u0131 olarak asgari \u015feffafl\u0131k, bilgi \u00fczerinde bireysel kontrol veya uygulamada belirli mevzuat ihlali sonu\u00e7lar\u0131na ra\u011fmen AEHMY'nin korumas\u0131n\u0131 yeterli olarak kabul ettiler. AEHMY'nin d\u00fczenleyici mekanizmalar\u0131, (k\u0131smen) \u00f6\u011frenci verilerini de\u011fil, e\u011fitim kay\u0131tlar\u0131n\u0131 d\u00fczenledi\u011finden, payda\u015flar i\u00e7in yeterli g\u00fcvence sa\u011flamaz. Mevzuat, kapsad\u0131\u011f\u0131 bilgiler, ilgili eylemler ve b\u00fcy\u00fck veri \u00e7a\u011f\u0131nda ge\u00e7erli oldu\u011fu varl\u0131klar a\u00e7\u0131s\u0131ndan yaln\u0131zca dar bir koruma sa\u011flar.<\/span><\/p>\n\n

E\u011fitim Kay\u0131tlar\u0131ndan \u00d6\u011frenci Verilerine<\/h3>\n

D\u00fc\u015f\u00fck maliyetli depolama, \u00e7evrimi\u00e7i a\u011flar \u00fczerinden anl\u0131k veri aktar\u0131m ve bulut tabanl\u0131 sunucular benzeri g\u00f6r\u00fclmemi\u015f bir hacim, h\u0131z ve \u201cb\u00fcy\u00fck veri\" \u00e7e\u015fitlili\u011fi yarat\u0131r (Mayer-Schonberger ve Cukier, 2014). \u00d6\u011frenci bilgileri art\u0131k okul dosyalama dolaplar\u0131nda kilitli k\u00e2\u011f\u0131da bas\u0131lm\u0131\u015f \u201ce\u011fitim kay\u0131tlar\u0131\"\u0131ndan ibaret de\u011fildir, Bilgiler, art\u0131k, bulut sunucular\u0131nda saklanan birlikte \u00e7al\u0131\u015fabilen ve an\u0131nda aktar\u0131labilir verilerdir. \u00c7evrimi\u00e7i e\u011fitim ara\u00e7lar\u0131 ve platformlar \u00f6\u011frenciler ile ilgili daha \u00f6nce m\u00fcmk\u00fcn olandan daha fazla ayr\u0131nt\u0131 i\u00e7eren \u00e7ok fazla say\u0131da bilgi \u00fcretir. Okul kimli\u011fi co\u011frafi konumland\u0131rma ve sosyal medya gibi okul d\u0131\u015f\u0131 kaynaklardan elde edilen veriler, e\u011fitim kay\u0131tlar\u0131 ile ilgili geleneksel beklentilerin \u00e7ok \u00f6tesine ge\u00e7mektedir (Alamuddin, Brown ve Kurzweil, 2016). Veri Madencili\u011finden elde \u00f6\u011frenci bilgileri her ne kadar kamuya a\u00e7\u0131k alanlardan elde edilse bile, bir\u00e7ok payda\u015f sistematik olarak \u00f6\u011frenci bilgilenin toplan\u0131p analiz edilmesini rahats\u0131z edici bulmaktad\u0131rlar (Watters, 2015). \u00d6\u011frencilerle ilgili t\u0131klama d\u00fczeyindeki verilerin otomatik olarak yakalanmas\u0131, bulut bili\u015fim a\u011flar\u0131n\u0131n ge\u00e7irgenli\u011fi ve b\u00fcy\u00fck verilerin sonsuz kullan\u0131m\u0131 yeni gizlilik endi\u015feleri do\u011furur (Singer, 2013).<\/span><\/p>\n

AEHMY'nin (veri) al\u0131c\u0131lar\u0131n bilgi uygulamalar\u0131n\u0131n ebeveyn, \u00f6\u011frenci veya okul g\u00f6zetimine olan g\u00fcveni, b\u00fcy\u00fck verilerin miktar\u0131, karma\u015f\u0131kl\u0131\u011f\u0131 ve etkile\u015fimli, dijital arac\u0131l\u0131 ortamlarda otomatik bilgi iletimi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, pratik veya anlaml\u0131 olsa bile m\u00fcmk\u00fcn olmayabilir. Mevzuat, okullar\u0131n kendi gizlilik uygulamalar\u0131n\u0131 bile ele alm\u0131yor ya da \u201c\u00f6\u011frenme ortamlar\u0131nda\u201d kullan\u0131c\u0131lardan do\u011frudan bilgi toplayan ancak federal bir fon almayan kitlesel \u00e7evrimi\u00e7i a\u00e7\u0131k kurs (KA\u00c7D\"ler) gibi yeni ba\u011f\u0131ms\u0131z e\u011fitim sa\u011flay\u0131c\u0131lar\u0131n\u0131 kapsam\u0131yor. Payda\u015flar, okullar\u0131n ve \u015firketlerin ne t\u00fcr bilgi toplad\u0131klar\u0131 ve onlar\u0131 nas\u0131l kulland\u0131klar\u0131 hakk\u0131nda \u00e7ok az fikir sahibidir. (Barnes, 2014). E\u011fitimcilerin ve veri al\u0131c\u0131lar\u0131n\u0131n, -\u00f6zellikle AEHMY'nin mevzuat ihlal durumunda do\u011frudan hesap verme y\u00fck\u00fcml\u00fcl\u00fc\u011f\u00fc getirmedi\u011fi durumlarda- verdikleri mahremiyet vaatlerine bile uydu\u011fundan emin olamazlar.<\/span><\/p>\n\n

Yeni \u00d6\u011frenci Mahremiyet Korumalar\u0131n\u0131n Yayg\u0131nla\u015ft\u0131r\u0131lmas\u0131<\/h3>\n

2013 y\u0131l\u0131ndan bu yana, devleti y\u00f6neten politika yap\u0131c\u0131lar\u0131 410 \u00f6\u011frenci gizlilik kanun teklifini sunarak payda\u015f pani\u011fe cevap verdi: 36 eyalet tekliflerin 73 tanesini yasala\u015ft\u0131rd\u0131. Federal d\u00fczeyde, yasa koyucular AEHMY ve kanun teklifinde \u00f6\u011frenci bilgilerini alan \u015firketleri ve kurulu\u015flar\u0131 do\u011frudan d\u00fczenleyen de\u011fi\u015fiklikler \u00f6nerdi. Koruyucu \u00f6nlemlerin b\u00fcy\u00fck \u00e7o\u011funlu\u011fu Federal olarak finanse edilen P-12 devlet okullar\u0131 i\u00e7in ge\u00e7erlidir ancak neyin \u00f6nemli oldu\u011fu ve \u201c\u00f6\u011frenci mahremiyeti\u201dnin ne anlama geldi\u011fi konusunda bir fikir birli\u011fi yoktur. Bu eyaletlerin, ara\u015ft\u0131rmac\u0131lar\u0131n, kurumlar\u0131n, \u015firketlerin, devletlerin ve federal politika yap\u0131c\u0131lar\u0131n \u00f6\u011frenci verilerini korumay\u0131 \u00f6nerdikleri inan\u0131lmaz \u00e7e\u015fitlilikten a\u00e7\u0131k\u00e7a anla\u015f\u0131lmaktad\u0131r (Demokrasi ve Teknoloji Merkezi, 2016; DQC, 2016; Vance, 2016).<\/span><\/p>\n

Hemen hemen t\u00fcm reform \u00f6nlemleri, daha fazla \u015feffafl\u0131k, hesap verebilirlik ve temel veri g\u00fcvenli\u011fi, g\u00fcvenlik ve y\u00f6netim protokolleri ihtiyac\u0131n\u0131 yans\u0131tmaktad\u0131r. Bir\u00e7o\u011fu, AEHMY'nin okullar\u0131n \u00fc\u00e7\u00fcnc\u00fc taraf sat\u0131c\u0131larla ve e\u011fitim ara\u015ft\u0131rmac\u0131lar\u0131yla nas\u0131l bilgi payla\u015ft\u0131\u011f\u0131na odaklanmas\u0131na devam ediyor. Birka\u00e7 a\u00e7\u0131k\u00e7a bilgi belirli t\u00fcrde veya sosyal medya gibi d\u0131\u015f kaynaklardan okul koleksiyonunu yasaklamaktad\u0131r. Baz\u0131 tedbirler veri ba\u011f\u0131ml\u0131 servis sa\u011flay\u0131c\u0131lar\u0131 do\u011frudan d\u00fczenler (Demokrasi ve Teknoloji Merkezi, 2016; DQC, 2016; Vance, 2016).<\/span><\/p>\n\n

\u00d6z D\u00fczenleme Takviyeleri<\/h3>\n

Gizlilik y\u00f6netimine daha esnek yakla\u015f\u0131mlar, \u00f6z y\u00f6netimi i\u00e7erir. 300'den fazla \u015firket, Gizlilik Forumu'nun gelece\u011fi ve \u00f6\u011frenci verilerini satmamak gibi on ilke i\u00e7eren yaz\u0131l\u0131m ve Bilgi End\u00fcstrisi Derne\u011fi taraf\u0131ndan olu\u015fturulan bir \u00d6\u011frenci Gizlilik S\u00f6zle\u015fmesini6<\/a><\/sup> imzalad\u0131. \u0130mza verenler, s\u00f6zle\u015fmeye uymad\u0131klar\u0131nda BTK uygulamas\u0131n\u0131 riske atarlar (Singer, 2015). ABD E\u011fitim Bakanl\u0131\u011f\u0131, e\u011fitim kurulu\u015flar\u0131 ve gizlilik uzmanlar\u0131 s\u00fcrekli olarak yeni en iyi uygulama k\u0131lavuzlar\u0131n\u0131 ve gizlilik ara\u00e7lar\u0131n\u0131 yay\u0131nlamaktad\u0131r (Krueger, 2014; Gizlilik Teknik Yard\u0131m Merkezi, 2014). Bununla birlikte, payda\u015flar\u0131n bu kurallara yeterince g\u00fcvenmeleri i\u00e7in, bilgi uygulamalar\u0131, \u00f6\u011frenme analiti\u011fi ama\u00e7lar\u0131 ve potansiyel sonu\u00e7lar ve ihlal durumunda hesap verebilirlik hakk\u0131nda yeterli \u015feffafl\u0131k olmal\u0131d\u0131r.<\/span><\/p>\n\n

\u00d6\u011eRENC\u0130 G\u0130ZL\u0130L\u0130K BO\u015eLUKLARI<\/h2>\n

\u00d6\u011frenci gizlilik d\u00fczenlemesinin en son a\u015famas\u0131, \u00f6\u011frenci verilerinin daha a\u00e7\u0131k bir \u015fekilde y\u00f6netilmesine ve baz\u0131 ihtiya\u00e7 duyulan \u015feffafl\u0131klara yol a\u00e7sa da reform \u00f6nlemlerinin \u00e7o\u011fu hala AEHMY\u2019nin kusurlar\u0131ndan muzdariptir. \u00c7o\u011fu \u00f6\u011frenci ve payda\u015f\u0131n, e\u011fitim kay\u0131tlar\u0131nda hangi bilgilerin bulundu\u011funu, verilerin faydalar\u0131na nas\u0131l kullan\u0131labilece\u011fine dair belirsiz kavramlar\u0131 ve korumalar\u0131n yerine getirildi\u011fi konusunda asgari g\u00fcvenceleri hala somut bir anlam\u0131 yoktur (Prinsloo ve Rowe, 2015; Rubel ve Jones, 2016; Zeide, 2016a).<\/span><\/p>\n\n

Minimal Anlaml\u0131 Onay ve G\u00f6zetim<\/h3>\n

AEHMY ve benzeri kurallar, yaln\u0131zca uygun al\u0131c\u0131lar\u0131n \u00f6\u011frenci verilerine eri\u015febilmelerini sa\u011flamak i\u00e7in ebeveyn veya okul g\u00f6zetimine g\u00fcvenir. Bu b\u00fcy\u00fck verilerin miktar\u0131 ve karma\u015f\u0131kl\u0131\u011f\u0131 ve etkile\u015fimli, dijital arac\u0131l\u0131 ortamlarda otomatik bilgi iletimi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, pratik veya anlaml\u0131 olsa da m\u00fcmk\u00fcn olmayabilir. S\u00f6zle\u015fme h\u00fck\u00fcmleri k\u0131smen bir yap\u0131 olu\u015ftursa da okullar\u0131n \u00fc\u00e7\u00fcnc\u00fc taraf bilgi uygulamalar\u0131n\u0131 izlemelerini ve yapt\u0131r\u0131m i\u00e7in pahal\u0131 davalar getirmelerini gerektirir. Son olarak, a\u00e7\u0131klamay\u0131 s\u0131n\u0131rlamak, bu verileri \u201cme\u015fru e\u011fitim \u00e7\u0131karlar\u0131\u201d i\u00e7in ba\u015flang\u0131\u00e7ta kullanan al\u0131c\u0131lar genellikle ayn\u0131 anda kurumsal veya ara\u015ft\u0131rma \u00e7\u0131karlar\u0131na hizmet etti\u011finden (Young, 2015; Zeide, 2016a) \u00f6\u011frenci bilgilerinin uygunsuz kullan\u0131m\u0131n\u0131 \u00f6nlemek i\u00e7in de \u00e7al\u0131\u015fmaz.<\/span><\/p>\n\n

Basit Kategorik Yasaklamalar<\/h3>\n

Baz\u0131 d\u00fczenlemeler, belirli veri toplama, kullanma ve yeniden haz\u0131rlama i\u015flemlerini tamamen engelleyerek bu sorunu gidermeye \u00e7al\u0131\u015f\u0131r. Bu genellikle e\u011fitim sisteminde mevcut veri kullan\u0131m\u0131 ile \u00e7ak\u0131\u015fan ve gelecek vaat eden \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fini gereksiz yere k\u0131s\u0131tlayan \u00f6nemli sonu\u00e7lara yol a\u00e7ar. Florida'da, \u00f6rne\u011fin, biyometrik bilgi toplama yasa\u011f\u0131 mevcut uygulamalar ve \u00f6zel e\u011fitim \u00f6\u011frencileri ile ilgili yasal y\u00fck\u00fcml\u00fcl\u00fckleri ile \u00e7eli\u015fmektedir. Sonu\u00e7 olarak, bir\u00e7ok devlet, \u00f6\u011frenci gizlili\u011fini sa\u011flama konusundaki ilk giri\u015fimlerini ask\u0131ya almak veya de\u011fi\u015ftirmek zorunda kald\u0131. Silme kurallar\u0131, uzunlamas\u0131na \u00e7al\u0131\u015fmalar i\u00e7in potansiyeli ciddi \u015fekilde s\u0131n\u0131rland\u0131rmakta ve s\u0131kl\u0131kla devlet yasalar\u0131n\u0131n getirdi\u011fi di\u011fer kay\u0131t tutma y\u00fck\u00fcml\u00fcl\u00fckleriyle \u00e7eli\u015fmektedir (Vance, 2016).<\/span><\/p>\n\n

E\u011fitim Ama\u00e7l\u0131 K\u0131s\u0131tlamalar\u0131n S\u0131n\u0131rlar\u0131<\/h3>\n

Bir\u00e7ok yeni yasa, E\u011fitim (K-12) hizmetleri sunan varl\u0131klar\u0131 kapsayan Kaliforniya'n\u0131n \u00d6\u011frenci \u00c7evrimi\u00e7i Ki\u015fisel Bilgi Koruma Yasas\u0131 (\u00d6\u00c7KBKY) modelini takip eder. Bunlar (yasalar) akt\u00f6rlerin okul g\u00f6zetimi yoluyla bunu yapmaya \u00e7al\u0131\u015fmak yerine \u00f6\u011frenci bilgilerini do\u011frudan nas\u0131l kulland\u0131klar\u0131n\u0131 d\u00fczenlerler. Bu t\u00fcr hizmetlerin \u00e7evrimi\u00e7i sa\u011flay\u0131c\u0131lar\u0131, okullarla s\u00f6zle\u015fmelere sahip olmal\u0131, istek \u00fczerine \u00f6\u011frenci bilgilerini silmeli ve belirtilen \u201cK-12\" ama\u00e7lar\u0131na hizmet etmeyen \u00f6\u011frenen profilleri olu\u015fturamamal\u0131d\u0131r.\u201d7<\/a><\/sup><\/span><\/p>\n

\u00d6\u011frenci verilerini \u201ce\u011fitsel\u201d kullan\u0131m veya ama\u00e7lar ile s\u0131n\u0131rlayan d\u00fczenlemeler, k\u00e2r ama\u00e7l\u0131 ticari k\u00f6t\u00fcye kullan\u0131m\u0131 \u00f6nlemeye \u00e7al\u0131\u015f\u0131r. Bununla birlikte, ama\u00e7 s\u0131n\u0131rlamalar\u0131, \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi ile ortaya \u00e7\u0131kan daha ince ayr\u0131nt\u0131l\u0131 sorunlar\u0131 ele almazlar. Ama\u00e7 s\u0131n\u0131rlama kurallar\u0131, bir \"e\u011fitim\" amac\u0131n\u0131 neyin olu\u015fturdu\u011fu hakk\u0131ndaki fikir birli\u011fi varsay\u0131m\u0131na dayan\u0131r. Ancak kurumlar\u0131n veya ara\u015ft\u0131rmac\u0131lar\u0131n, bir taraftan kurumsal kaynaklar\u0131 y\u00f6netmek, e\u011fitim sistemini geli\u015ftirmek veya \u00f6\u011frenme bilimini i\u00e7g\u00f6r\u00fc ile ayd\u0131nlatmak i\u00e7in verileri hala yasal olarak kullan\u0131rken, di\u011fer taraftan \u00f6\u011frenen veri konular\u0131n\u0131n hemen e\u011fitim \u00e7\u0131karlar\u0131ndan ba\u015fka hedeflere \u00f6ncelik verebilece\u011fini dikkate almazlar. \u00d6rne\u011fin okullar, marjinal \u00f6\u011frencileri kaynaklar\u0131 korumada veya s\u0131ralamalar\u0131 iyile\u015ftirmede te\u015fvik etmek yerine onlar\u0131 hari\u00e7 tutmak i\u00e7in \u00f6ng\u00f6r\u00fcc\u00fc verileri kullanabilir (Ashman vd., 2014; Drachsler ve Greller, 2016; Rubel ve Jones, 2016; Selwyn, 2014; Slade, 2016).<\/span><\/p>\n\n

\u00d6\u011frenen Verisini D\u0131\u015far\u0131da B\u0131rakma (G\u00f6zard\u0131 etme)<\/h3>\n

\u00c7o\u011fu yeni yasa, y\u00fcksek\u00f6\u011fretim kurumlar\u0131nda tutulan bilgileri ele almaz. Geleneksel olarak federal finanse edilen e\u011fitim kurumlar\u0131ndan ba\u011f\u0131ms\u0131z olan sanal \u00f6\u011frenme ortamlar\u0131 taraf\u0131ndan toplanan \"\u00f6\u011frenen\" bilgilerini g\u00f6z ard\u0131 ederler. Bunun yerine, daha fazla izin verici ticari gizlilik rejimi, bu \u00f6zel kurulu\u015flar taraf\u0131ndan toplanan ve kullan\u0131lan verileri y\u00f6netir (ge\u00e7erli devlet yasas\u0131n\u0131n yoklu\u011funda). Bunun anlam\u0131, \u201c\u00f6\u011frenen\u201d verilerin kullan\u0131lmas\u0131 ve if\u015fa edilmesinin, anla\u015f\u0131lmaz, a\u015f\u0131r\u0131 geni\u015f ve haber verilmeksizin de\u011fi\u015fmeye a\u00e7\u0131k oldu\u011fu i\u00e7in k\u00f6t\u00fc \u015f\u00f6hretli olan t\u00fcketici gizlilik politikalar\u0131 ile s\u0131n\u0131rl\u0131 oldu\u011fudur (Jones ve Regner, 2015; Polonetsky ve Tene, 2014; Young, 2015; Zeide, 2016a).<\/span><\/p>\n\n

E\u011e\u0130T\u0130M VER\u0130S\u0130 ET\u0130\u011e\u0130<\/h2>\n

Toplum bu konularla sekt\u00f6rler i\u00e7erisinde bo\u011fu\u015fmakta ancak bunlar e\u011fitim ortamlar\u0131nda \u00f6zellikle \u015fiddetli durumdad\u0131rlar. E\u011fitim deneyimlerini geli\u015ftirmek isteyen ve d\u00fcr\u00fcstl\u00fckle \u00e7al\u0131\u015fan bireyler olarak, analitik ve e\u011fitim veri madencili\u011fi \u00f6\u011frenmesinde yer alan bilgi uygulamalar\u0131n\u0131n, geleneksel e\u011fitim bilgi uygulamalar\u0131 ve \u00f6\u011frenci verileriyle ilgili normlarla kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda ne kadar devrimci oldu\u011fu g\u00f6r\u00fc\u015f\u00fcn\u00fc kaybetmek kolayd\u0131r. \u00d6\u011frencilerin nadiren, ana ak\u0131m veri odakl\u0131 teknolojilerden vazge\u00e7me konusunda ger\u00e7ek\u00e7i bir se\u00e7enekleri bulunur. E\u011fitim verisi \u00f6zneleri, yaln\u0131zca \u00e7ocuk olabilece\u011finden de\u011fil, ayn\u0131 zamanda \u00f6\u011frenmenin entelekt\u00fcel geli\u015fim i\u00e7in bir dereceye kadar risk almay\u0131 gerektirmesinden dolay\u0131, genel t\u00fcketici ba\u011flam\u0131ndakilerden daha savunmas\u0131zd\u0131rlar. Bu ara\u00e7lar\u0131n, adil f\u0131rsatlar\u0131 geni\u015fletmek yerine kazara azaltabilece\u011fi, e\u011fitim sisteminin daha geni\u015f hedeflerini baltalayabilece\u011fi ve \u00f6\u011frencilere daha az faaliyete imk\u00e2n verebilece\u011fi ve onlar\u0131 daha az de\u011fil tersine daha fazla savunmas\u0131z hale getirebilece\u011fi konular\u0131nda hala \u00e7\u00f6z\u00fclmemi\u015f sorunlar vard\u0131r (Prinsloo ve Slade, 2016; Siemens, 2014).<\/span><\/p>\n\n

Adil Kazan\u0131mlar<\/h3>\n

\u00d6\u011frenci verileriyle \u00e7al\u0131\u015fanlar\u0131n, teorik ve teknolojik modellerin tarafs\u0131z alan\u0131 yerine ka\u00e7\u0131n\u0131lmaz olarak kusurlu bir ger\u00e7eklikte nas\u0131l sonu\u00e7lar\u0131n ortaya \u00e7\u0131kabilece\u011fini g\u00f6z \u00f6n\u00fcne almalar\u0131 \u00f6nemlidir. Algoritmik modeller az\u0131nl\u0131klara veya daha d\u00fc\u015f\u00fck sosyoekonomik stat\u00fcye sahip \u00f6\u011frencilere kar\u015f\u0131 kazara ayr\u0131mc\u0131l\u0131k yapabilir. Farkl\u0131 etkileri olabilir. \u00d6\u011frenci ba\u015far\u0131s\u0131n\u0131 tahmin eden ara\u00e7lar, daha fazla ba\u015far\u0131 ve yukar\u0131 do\u011fru hareketlili\u011fi te\u015fvik etmek yerine ge\u00e7mi\u015f e\u015fitsizlikleri tekrarlayabilir. G\u00f6r\u00fcn\u00fc\u015fte tarafs\u0131z olan politikalar, d\u00fczensiz uygulamalardan dolay\u0131 derinden adaletsiz sonu\u00e7lar yaratabilir (Boyd ve Crawford, 2011; Citron ve Pasquale, 2014; Barocas ve Selbst, 2014).<\/span><\/p>\n\n

Daha Geni\u015f E\u011fitim Etkileri<\/h3>\n

S\u0131n\u0131flardan, kameralardan veya sens\u00f6rlerden s\u00fcrekli olarak ayr\u0131nt\u0131l\u0131 bilgi toplamak daha geni\u015f kapsaml\u0131 sonu\u00e7lar ortaya \u00e7\u0131karabilir. Her yerde g\u00f6zetim ve g\u00f6m\u00fcl\u00fc de\u011ferlendirme \u00f6\u011frenci kat\u0131l\u0131m\u0131 ve ifadesi \u00fczerinde \u00fcrpertici bir etkiye sahip olabilir (Boninger ve Molnar, 2016; Vance ve Tucker, 2016). Bu uygulamalar, periyodik y\u00fcksek riskli testlere olan ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 azalt\u0131rken, \u00f6\u011frenme s\u00fcrecinin her an\u0131n\u0131 derinlemesine inceleme alt\u0131na al\u0131rlar. Bu durum sonu\u00e7ta \u00f6\u011frenme ortamlar\u0131nda gerekli olan entelekt\u00fcel risk almaya kar\u015f\u0131 bir so\u011fukluk olu\u015fturarak, veri odakl\u0131 e\u011fitim ara\u00e7lar\u0131na ve uygulay\u0131c\u0131lar\u0131na olan g\u00fcveni zay\u0131flatabilir.<\/span><\/p>\n\n

Kas\u0131ts\u0131z Yetki A\u015f\u0131m\u0131<\/h3>\n

\u00d6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi sadece pedagojik ve akademik kararlar\u0131n nas\u0131l verildi\u011fini de\u011fil kimlerin bu kararlar\u0131 verdi\u011fini de de\u011fi\u015ftirir. Geleneksel olarak, \u00f6\u011frenciler hakk\u0131nda de\u011ferlendirme yapan ve karar alan bireyler hemen ellerinin alt\u0131nda bulunan ki\u015fisel, ba\u011flamsalla\u015ft\u0131r\u0131lm\u0131\u015f g\u00f6zlem ve bilgilere dayanarak karar alm\u0131\u015ft\u0131r. Belirli sonu\u00e7larla ilgili endi\u015feleri olan ebeveynler, \u00f6\u011frenciler veya y\u00f6neticiler a\u00e7\u0131klama i\u00e7in do\u011frudan ilgili karar vericiye gidebilmekteydiler. Bu durum \u015feffafl\u0131k sa\u011flad\u0131 ve tazminat almak i\u00e7in kolay bir yol yaratt\u0131, dolay\u0131s\u0131yla hesap verebilirlik de sa\u011fland\u0131.<\/span><\/p>\n

Veri odakl\u0131 e\u011fitim ara\u00e7lar\u0131n\u0131 benimsemesi, e\u011fitimcilere \u00f6\u011frenmeyi \u00f6l\u00e7mek i\u00e7in neler gerekti\u011fi, e\u011fitim yoluyla hangi hedeflerin ama\u00e7land\u0131\u011f\u0131 ve bu kararlar\u0131 kimin ald\u0131\u011f\u0131 gibi hususlar\u0131 etkiler. Otomatik ve algoritmik pedagojik ve kurumsal karar verme, otoritenin konumunu geleneksel, fiziksel olarak mevcut bir insandan belirsiz teknolojilere veya uzak \u015firketler ve ara\u015ft\u0131rmac\u0131lara kayd\u0131r\u0131r. Veri odakl\u0131 e\u011fitim, ya\u015famlar\u0131 ve genel olarak e\u011fitim sistemini \u015fekillendiren \u00f6nemli kararlar\u0131 kimin almas\u0131 gerekti\u011fini de\u011fi\u015ftirir. Bunu, de\u011fi\u015fim a\u00e7\u0131k olmadan ve bir\u00e7ok durumda da kas\u0131tl\u0131 olmadan yapar. Verilere kimlerin eri\u015febilece\u011fi ve kullanabilece\u011fine y\u00f6nelik bu de\u011fi\u015fim, g\u00fc\u00e7 ili\u015fkilerini de de\u011fi\u015ftirir. G\u00fcvenlik uzman\u0131 Bruce Schneier (2008), \u201cverilerimizi kontrol eden ki\u015fi hayat\u0131m\u0131z\u0131 kontrol eder\u201d demektedir (paragraf 5). Verilerin teslimi ile ilgili olan yetki devri a\u00e7\u0131k\u00e7a dikkate al\u0131nmal\u0131d\u0131r.<\/span><\/p>\n\n

UYUMLULUK \u00d6TES\u0130NE GE\u00c7MEK<\/h2>\n

Mevcut ve geli\u015fmekte olan d\u00fczenleyici \u00e7er\u00e7evenin sa\u011flad\u0131\u011f\u0131 imk\u00e2nlar dahilinde, \u00f6\u011frenme analiti\u011fini ve e\u011fitsel veri madencili\u011fi uygulay\u0131c\u0131lar\u0131 ve t\u00fcketicileri bu g\u00fcc\u00fcn \u00e7o\u011funa sahip olacakt\u0131r. Buna g\u00f6re, \u00f6\u011frenci mahremiyetinin ne anlama geldi\u011fini tan\u0131mlama sorumlulu\u011funu \u00fcstlenmek zorunda kalacaklard\u0131r. Teknolojik yap\u0131lar, kavramsal modeller ve \u00f6\u011frenme kazan\u0131mlar\u0131 hakk\u0131ndaki verdikleri kararlar, \u00f6\u011frenme ortamlar\u0131ndaki bilgiye pratikte uygun d\u00fc\u015fen kurallar\u0131 i\u015fler, olu\u015fturur. Bu kararlar\u0131n etrafl\u0131ca d\u00fc\u015f\u00fcn\u00fclerek ve bilin\u00e7li bir \u015fekilde verilmesi gerekir. Ayn\u0131 zamanda \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fine y\u00f6nelik bireysel kat\u0131l\u0131m, kurumsal uygulama ve politika yap\u0131c\u0131 deste\u011fi i\u00e7in gerekli g\u00fcveni besleyerek bir alan olarak \u00f6\u011frenme analiti\u011fi ve genel olarak e\u011fitsel veri madencili\u011fine yarar sa\u011flar.<\/span><\/p>\n

Daha proaktif bir yakla\u015f\u0131m benimsemek i\u00e7in salt uyumlulu\u011fun \u00f6tesine ge\u00e7meyi tavsiye ederim. \u0130deal olarak, bu sadece potansiyel sorunlar\u0131 \u00f6ng\u00f6rmekle kalmaz, ayn\u0131 zamanda sorunlar ortaya \u00e7\u0131karsa uygulamalara karar vermek \u00fczere protokolleri devreye koymay\u0131 ve veri sahipleri ve payda\u015flarla ileti\u015fim kurmay\u0131 i\u00e7erir. Proaktif \u00f6\u011frenci mahremiyeti uygulamalar\u0131n\u0131n temel bile\u015fenleri \u015funlard\u0131r: 1) etik etkilerin g\u00f6z \u00f6n\u00fcne al\u0131nmas\u0131; 2) inceleme i\u00e7in a\u00e7\u0131k protokollerin olu\u015fturulmas\u0131; 3) veri uygulamalar\u0131, ama\u00e7lar\u0131 ve korumas\u0131 hakk\u0131nda veri sahipleri ve payda\u015flarla etkin olarak ileti\u015fim kurulmas\u0131 ve 4) algoritmik hesap verebilirli\u011fin sa\u011flanmas\u0131.<\/span><\/p>\n\n

Etik \u0130nceleme<\/h3>\n

\u00d6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi projeleri, gizil faydalar\u0131n proaktif olarak g\u00f6z \u00f6n\u00fcne al\u0131nmas\u0131n\u0131, toplumda, zaman i\u00e7inde da\u011f\u0131l\u0131m\u0131n\u0131, \u00f6\u011frenmeyle, daha geni\u015f toplumla ilgili beklenmedik kazan\u0131mlar\u0131 ve deney protokolleri ile nihai \u00f6nceliklere y\u00f6nelik etik sorular\u0131 i\u00e7ermelidir. Bunlar, insan denekler \u00fczerinde yap\u0131lan deneylere y\u00f6nelik 1978 y\u0131l\u0131nda Belmont raporunda ilan edilen ve daha sonra akademik ara\u015ft\u0131rmalar\u0131 onaylamakla sorumlu Kurumsal \u0130nceleme Kurullar\u0131 (K\u0130K) arac\u0131l\u0131\u011f\u0131yla sistemle\u015ftirilen ve kurumsalla\u015ft\u0131r\u0131lan \u00f6nemli konular\u0131 yans\u0131tmaktad\u0131r. Bununla birlikte kurumlar\u0131n i\u00e7inde veri kullan\u0131m\u0131, ara\u015ft\u0131rma yerine \u201coptimizasyon\u201d olarak s\u0131n\u0131fland\u0131r\u0131lan etkinlikler ve \u015firket uygulamalar\u0131 dikkate al\u0131nd\u0131\u011f\u0131nda benzer temel etik ilkeler nadiren g\u00f6z \u00f6n\u00fcnde bulundurulmaktad\u0131r.<\/span><\/p>\n

\u00d6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi alan\u0131ndaki uygulay\u0131c\u0131lar, konsorsiyumlar ve destekleyiciler bilgi uygulamalar\u0131n\u0131 y\u00f6nlendirmek i\u00e7in etik ilkeleri uygulamaya koymu\u015flard\u0131r. Bunlar, algoritmik analizin bilgi ve karma\u015f\u0131kl\u0131\u011f\u0131 ve hacmi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, \u00f6\u011frenme sistemleri i\u00e7inde verilerin nas\u0131l topland\u0131\u011f\u0131na, sakland\u0131\u011f\u0131na, i\u015flendi\u011fine ve payla\u015f\u0131ld\u0131\u011f\u0131na dair kullan\u0131c\u0131 bildirimi ve izninin \u00f6nemi ve zorlu\u011fu d\u00e2hil \u00f6nemli konular\u0131 g\u00fcndeme getirmektedir. Ayr\u0131ca, deneysel sonu\u00e7lar\u0131n \u201ctoplum yarar\u0131na\u201d (Drachsler ve Greller, 2016; Open University, 2017; Pardo ve Siemens, 2014; Sclater ve Bailey, 2015; Slade, 2016; Asilomar, 2014) hizmet edip etmedi\u011fini dikkate alan daha soyut adalet ve hay\u0131rseverlik kavramlar\u0131n\u0131 da i\u00e7erirler.<\/span><\/p>\n\n

A\u00e7\u0131k \u0130nceleme<\/h3>\n

Gizlilik ve etik hususlar, teknolojinin ve deneysel tasar\u0131m\u0131n ilk a\u015famalar\u0131ndan itibaren d\u00e2hil edilmelidir. En az\u0131ndan, veri odakl\u0131 e\u011fitim ara\u00e7lar\u0131, istenmeyen \u00f6nyarg\u0131ya, farkl\u0131 etkiye ve toplum genelinde risk ve faydalar\u0131n orant\u0131s\u0131z da\u011f\u0131l\u0131m\u0131na kar\u015f\u0131 denetlenmelidir. En iyi uygulama, olas\u0131 ama \u00f6ng\u00f6r\u00fclebilir, sorunlu sonu\u00e7lar\u0131 zaman\u0131nda ele almak i\u00e7in proaktif \u00f6nlemler olu\u015fturacakt\u0131r. \u00d6rne\u011fin, iki deneysel grup aras\u0131ndaki tutars\u0131zl\u0131\u011f\u0131n, ara\u015ft\u0131rmac\u0131lar ve e\u011fitimcilerin A\/B testini durdurmalar\u0131n\u0131 gerektirecek kadar y\u00fcksek oldu\u011fu bir nokta var m\u0131d\u0131r?<\/span><\/p>\n

Projeler, a\u00e7\u0131k hesap verebilirlik ve etik inceleme i\u00e7in \u00f6nceden belirlenmi\u015f noktalara\/a\u015famalara sahip olmal\u0131d\u0131r. Bir \u00e7ok \u015firket, \u00f6rne\u011fin, deneylerde ilerlemeden \u00f6nce ve yine yay\u0131nlanmadan \u00f6nce veri sahiplerinin ve daha geni\u015f bir toplulu\u011fun ilgi ve isteklerini g\u00f6z \u00f6n\u00fcnde bulundurmak i\u00e7in kendi \u201ct\u00fcketici ara\u015ft\u0131rma kurullar\u0131n\u0131\u201d \u00e7al\u0131\u015ft\u0131rmaya ba\u015flam\u0131\u015ft\u0131r (Calo, 2013; Jackman ve Kanerva, 2016; Tene ve Polonetsky, 2015).<\/span><\/p>\n\n

Sald\u0131rgan Saydaml\u0131k<\/h3>\n

\u0130deal olarak, \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi ara\u00e7lar\u0131 ve teknolojileri de anlaml\u0131 \u015feffafl\u0131k ve algoritmik hesap verebilirlik sa\u011flamal\u0131d\u0131r. \u015eeffafl\u0131k mikro -ve makro- d\u00fczeyde \u00f6nemlidir. Bilgi uygulamalar\u0131n\u0131 if\u015fa etmek, analitik ve e\u011fitsel veri madencili\u011fi veri uygulamalar\u0131n\u0131 \u00f6\u011frenme konusunda yeterince \u00f6zg\u00fcl ve haz\u0131r bilgilerin yoklu\u011funda panik yapabilecek payda\u015flara g\u00fcven vermeye yard\u0131mc\u0131 olur.<\/span><\/p>\n

Veri analizinin sadece -\u00e7ok uzak bir yerde gelecekteki bir \u00f6\u011frenciye de\u011fil- mevcut \u00f6\u011frenenlere de fayda sa\u011flayabilece\u011fi yollarla ilgili \u015feffafl\u0131k ve \u00f6tesine ge\u00e7i\u015f, payda\u015f korkular\u0131n\u0131 hafifletmeye yard\u0131mc\u0131 olur. A\u00e7\u0131k ve erken ileti\u015fim, k\u00fc\u00e7\u00fck bir elit bilim grubunun \u00f6\u011frenci deneyimleri ve sonu\u00e7lar\u0131 \u00fczerinde muazzam bir kontrole sahip oldu\u011fu ve eylemlerinin gizlilik i\u00e7inde \u00f6rt\u00fcld\u00fc\u011f\u00fc izlenimini azaltmaya yard\u0131mc\u0131 olur. Veri sahiplerine ve daha geni\u015f toplulu\u011fa ula\u015fman\u0131n yollar\u0131n\u0131 bulmak i\u00e7in kurumsal kaynaklar\u0131 \u00e7al\u0131\u015ft\u0131rmaya yard\u0131mc\u0131 olur.<\/span><\/p>\n\n

Algoritmik Sorumluluk<\/h3>\n

Ancak \u015feffafl\u0131k uygun bilgi uygulamalar\u0131n\u0131 sa\u011flamak i\u00e7in yeterli de\u011fildir. Bu bir \u00f6n ko\u015fuldur. Sorundaki riskler ve algoritmik karar vermenin belirsizli\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda belgelendirme ve hesap verebilirlik de \u00f6nemlidir. \u00d6\u011frenciler ve payda\u015flar pedagojik ve kurumsal karar vermeyi hangi kan\u0131tlar\u0131n destekledi\u011fini bilmek isteyecektir. \u0130deal olarak, \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi uygulay\u0131c\u0131lar\u0131 algoritmik hesap verebilirlik i\u00e7in ara\u00e7lar geli\u015ftirmelidir. Bunlar, algoritmik ara\u00e7lar\u0131n ama\u00e7land\u0131\u011f\u0131 gibi \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 ve asl\u0131nda vaat edilen sonu\u00e7lar\u0131 destekledi\u011fini \u00e7ifte kontrol eden denetimleri i\u00e7erir.<\/span><\/p>\n

\u00d6\u011frencilerin gelecekteki f\u0131rsatlar\u0131n\u0131 etkilemede giderek daha \u00f6nemli hale gelecek olan algoritmik hesap verebilirli\u011fin \u00f6nemli bir par\u00e7as\u0131, yasal s\u00fcrecin i\u015flemesi i\u00e7in algoritmik ve kurumsal karar vermeyi belgelendirme gere\u011fidir (Diakopoulos, 2016; Kobie), 2016; Kroll vd., 2017). \u00d6\u011freniciler, e\u011fitimciler ve kurumlar kan\u0131tlar\u0131 g\u00f6rmek ve akademik geli\u015fimlerini etkileyip yeterlilik belgesi veren sistemler hakk\u0131nda bilgi sahibi olmak ve kendilerini etkileyen kararlar\u0131 incelemek isteyeceklerdir (Zeide, 2016a; ayr\u0131ca bk. Citron ve Pasquale, 2014; Crawford ve Schultz, 2014). Veri bilimcileri ve veri odakl\u0131 karar vericiler, \u00f6nemli kararlar\u0131n adli olarak incelenmesini kolayla\u015ft\u0131rmak i\u00e7in haz\u0131rlanmal\u0131d\u0131r. \u00d6rne\u011fin, ebeveynler, \u00e7ocuklar\u0131n\u0131n neden bir \u00fcst s\u0131n\u0131fa ge\u00e7tikleri veya ge\u00e7mediklerine ili\u015fkin bir a\u00e7\u0131klama isteyeceklerdir. \u201c\u00c7\u00fcnk\u00fc algoritma \u00f6yle dedi\u201d yeterli bir cevap olmayacakt\u0131r.<\/span><\/p>\n\n

SONU\u00c7<\/h2>\n

G\u00fcven, entelekt\u00fcel deneyselli\u011fi ve b\u00fcy\u00fcmeyi te\u015fvik etmek isteyen \u00f6\u011frenme ortamlar\u0131 i\u00e7in \u00e7ok \u00f6nemlidir. Bu durum Beyaz Saray\u0131n b\u00fcy\u00fck verilerle ilgili 2014 tarihli bir raporunda da \u015f\u00f6yle belirtilmi\u015ftir: \u201c\u00d6\u011frenmenin kendisi bir deneme yan\u0131lma s\u00fcreci oldu\u011fu i\u00e7in, verileri bu yeniliklerin yararlar\u0131n\u0131 sa\u011flayacak ancak yine de \u00f6\u011frencilerin ke\u015ffetmek, hata yapmak ve \u00f6\u011frenme s\u00fcrecinin bir par\u00e7as\u0131 olan hatalar\u0131n uzun vadeli sonu\u00e7lar\u0131 olaca\u011f\u0131n\u0131 d\u00fc\u015f\u00fcnmeden \u00f6\u011frenmek i\u00e7in g\u00fcvenli bir alan yaratacak \u015fekilde kullanmak gerekir.\u201d<\/span><\/p>\n

Salt \u015fartlara uyman\u0131n \u00f6tesine de ge\u00e7erek, e\u011fitim verileri kendilerine emanet edilenler, en iyi niyetli projelerin bile ula\u015fmak istedikleri hedefleri zay\u0131flatabilecek olas\u0131 istenmeyen sonu\u00e7lar\u0131na kar\u015f\u0131 koruma sa\u011flayabilir. Bu el kitab\u0131n\u0131n okurlar\u0131, \u00f6\u011frenci verilerinin zenginli\u011fi ile emanet edilmi\u015f, yaln\u0131zca uyumu de\u011fil, daha geni\u015f sosyal, etik ve politik sonu\u00e7lar\u0131 g\u00f6z \u00f6n\u00fcnde bulunduracak \u015fekilde \u00f6tesine ge\u00e7en proaktif bir yakla\u015f\u0131m benimsemelidir. Bunu yapmak, veri odakl\u0131 e\u011fitime olan g\u00fcveni art\u0131racak ve \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011finin devrimci potansiyellerine ula\u015fmas\u0131n\u0131 sa\u011flayacakt\u0131r.<\/span><\/p>\n\n

KAYNAK\u00c7A<\/h2>\nAlamuddin, R., Brown, J., & Kurzweil, M. (2016). Student data in the digital era: An overview of current practices<\/i>. Ithaka S+R. doi:10.18665\/.283890 <\/span><\/span>\n\nAshman, H., Brailsford, T., Cristea, A. I., Sheng, Q. Z., Stewart, C., Toms, E. G., & Wade, V. (2014). The ethical and social implications of personalization technologies for e-learning. Information & Management, 51<\/i>(6), 819\u2013832. doi:10.1016\/j.im.2014.04.003 <\/span><\/span>\n\nAsilomar Convention for Learning Research in Higher Education. (2014). Student data policy and data use messaging for consideration at Asilomar II<\/i>. Asilomar, (CA). http:\/\/asilomar-highered.info\/ Barnes, K. (2014, March 6). Why a \u201cStudent Privacy Bill of Rights\u201d is desperately needed. Washington Post. http:\/\/www.washingtonpost.com\/blogs\/answer-sheet\/wp\/2014\/03\/06\/why-a-student-privacy-bill-of-rights-is-desperately-needed\/ <\/span><\/span>\n\nBarocas, S., & Selbst, A. D. (2014). Big data\u2019s disparate impact<\/i>. SSRN Scholarly Paper. Elsevier. http:\/\/papers.ssrn.com\/abstract=2477899 <\/span><\/span>\n\nBoninger, F., & Molnar, A. (2016). Learning to be watched: Surveillance culture at school<\/i>. Boulder, CO: National Education Policy Center. http:\/\/nepc.colorado.edu\/files\/publications\/RB%20Boninger-Molnar%20 Trends.pdf <\/span><\/span>\n\nBoyd, D., & Crawford, K. (2011). Six provocations for big data. SSRN Scholarly Paper. Elsevier. http:\/\/papers.ssrn.com\/abstract=1926431 <\/span><\/span>\n\nCalo, R. (2013). Consumer subject review boards: A thought experiment. Stanford Law Review Online, 66<\/i>, 97. <\/span><\/span>\n\nCenter for Democracy and Technology (2016, October 5). State student privacy law compendium<\/i>. https:\/\/cdt.org\/insight\/state-student-privacy-law-compendium\/ <\/span><\/span>\n\nCitron, D. K., & Pasquale, F. A. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89<\/i>. http:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2376209 <\/span><\/span>\n\nCrawford, K., & Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. Boston College Law Review, 55<\/i>(1), 93. <\/span><\/span>\n\nDaggett, L. M. (2008). FERPA in the twenty-first century: Failure to effectively regulate privacy for all students. Catholic University Law Review, 58<\/i>, 59. <\/span><\/span>\n\nDiakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59<\/i>(2), 56\u201362. doi:10.1145\/2844110 <\/span><\/span>\n\nDivoky, D. (1974, March 31). How secret school records can hurt your child. Parade Magazine<\/i>, 4\u20135. <\/span><\/span>\n\nDQC (Data Quality Campaign) (2016). Student data privacy legislation: A summary of 2016 state legislation<\/i>. http:\/\/2pido73em67o3eytaq1cp8au.wpengine.netdna-cdn.com\/wp-content\/uploads\/2016\/09\/DQC-Legislative-summary-09232016.pdf <\/span><\/span>\n\nDrachsler, H., & Greller, W. (2016). Privacy and analytics \u2014 it\u2019s a DELICATE issue. A checklist to establish trusted learning analytics. Open Universiteit. http:\/\/dspace.ou.nl\/bitstream\/1820\/6381\/1\/Privacy%20a%20 DELICATE%20issue%20(Drachsler%20%26%20Greller)%20-%20submitted.pdf <\/span><\/span>\n\nJackman, M., & Kanerva, L. (2016). Evolving the IRB: Building robust review for industry research. Washington and Lee Law Review Online, 72<\/i>(3), 442. <\/span><\/span>\n\nJones, M. L., & Regner, L. (2015, August 19). Users or students? Privacy in university MOOCS. Science and Engineering Ethics, 22<\/i>(5), 1473\u20131496. doi:10.1007\/s11948-015-9692-7 <\/span><\/span>\n\nKobie, N. (2016, January 29). Why algorithms need to be accountable. Wired UK<\/i>. http:\/\/www.wired.co.uk\/ news\/archive\/2016-01\/29\/make-algorithms-accountable <\/span><\/span>\n\nKroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165<\/i>. http:\/\/papers.ssrn.com\/sol3\/Papers.cfm?abstract_id=2765268 <\/span><\/span>\n\nKrueger, K.R. (2014). 10 steps that protect the privacy of student data. The Journal, 41<\/i>(6), 8. <\/span><\/span>\n\nMayer-Sch\u00f6nberger, V., & Cukier, K. (2014). Learning with big data<\/i>. Eamon Dolan \/ Houghton Mifflin Harcourt. <\/span><\/span>\n\nOpen University. (2017). Ethical use of student data for learning analytics policy. http:\/\/www.open.ac.uk\/students\/charter\/essential-documents\/ethical-use-student-data-learning-analytics-policy <\/span><\/span>\n\nPardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45<\/i>(3), 438\u2013450. doi: 10.1111 \/ bjet.12152<\/span><\/span>\n\nPolonetsky, J., & Tene, O. (2014). Who is reading whom now: Privacy in education from books to MOOCs. Vanderbilt Journal of Entertainment & Technology Law, 17<\/i>, 927. <\/span><\/span>\n\nPrinsloo, P., & Rowe, M. (2015). Ethical considerations in using student data in an era of \u201cbig data.\u201d In W. R. Kilfoil (Ed.), Moving beyond the Hype: A Contextualised View of Learning with Technology in Higher Education <\/i>(pp. 59\u201364). Pretoria, South Africa: Universities South Africa. <\/span><\/span>\n\nPrinsloo, P., & Slade, S. (2016). Student vulnerability, agency and learning analytics: An exploration. Journal of Learning Analytics, 3<\/i>(1), 159\u2013182. <\/span><\/span>\n\nPrivacy Technical Assistance Center. (2014) Protecting student privacy while using online educational services: Requirements and best practice<\/i>. http:\/\/ptac.ed.gov\/sites\/default\/files\/Student%20Privacy%20and%20 Online%20Educational%20Services%20%28February%202014%29.pdf <\/span><\/span>\n\nReidenberg, J. R., Russell, N. C., Kovnot, J., Norton, T. B., Cloutier, R., & Alvarado, D. (2013). Privacy and cloud computing in public schools<\/i>. Bronx, NY: Fordham Center on Law and Information Policy. http:\/\/ir.lawnet.fordham.edu\/clip\/2\/?utm_source=ir.lawnet.fordham.edu%2Fclip%2F2veutm_medium=PDFveutm_campaign=PDFCoverPages <\/span><\/span>\n\nRubel, A., & Jones, K. M. L. (2016). Student privacy in learning analytics: An information ethics perspective. The Information Society, 32<\/i>(2), 143\u2013159. doi:10.1080\/01972243.2016.1130502 <\/span><\/span>\n\nSchneier, B. (2008, May 15). Our data, ourselves. Wired Magazine<\/i>. http:\/\/www.wired.com\/2008\/05\/securitymatters-0515\/all\/1\/ <\/span><\/span>\n\nSclater, N., & Bailey, P. (2015). Code of practice for learning analytics<\/i>. https:\/\/www.jisc.ac.uk\/guides\/ code-of-practice-for-learning-analytics <\/span><\/span>\n\nSelwyn, N. (2014). Distrusting educational technology: Critical questions for changing times<\/i>. New York\/London: Routledge, Taylor & Francis Group. <\/span><\/span>\n\nSiemens, G. (2014, January 13). The vulnerability of learning. http:\/\/www.elearnspace.org\/blog\/2014\/01\/13\/ the-vulnerability-of-learning\/ <\/span><\/span>\n\nSinger, N. (2015, March 5). Digital learning companies falling short of student privacy pledge. New York Times<\/i>. http:\/\/bits.blogs.nytimes.com\/2015\/03\/05\/digital-learning-companies-falling-short-of-student-privacy-pledge\/ <\/span><\/span>\n\nSinger, N. (2013, December 13). Schools use web tools, and data is seen at risk. New York Times<\/i>. http:\/\/www.nytimes.com\/2013\/12\/13\/education\/schools-use-web-tools-and-data-is-seen-at-risk.html <\/span><\/span>\n\nSlade, S. (2016). Applications of student data in higher education: Issues and ethical considerations. Presented at Asilomar II: Student Data and Records in the Digital Era. Asilomar, (CA). <\/span><\/span>\n\nSolove, D.J. (2012). FERPA and the cloud: Why FERPA desperately needs reform. http:\/\/www.law.nyu.edu\/ sites \/ default \/ files \/ ECM_PRO_074960.pdf <\/span><\/span>\n\nTene, O., & Polonetsky, J. (2015). Beyond IRBs: Ethical guidelines for data research. Beyond IRBs: Ethical review processes for big data research<\/i>. https:\/\/bigdata.fpf.org\/wp-content\/uploads\/2015\/12\/Tene-Polonetsky-Beyond-IRBs-Ethical-Guidelines-for-Data-Research1.pdf <\/span><\/span>\n\nUS Department of Education (n.d.). FERPA frequently asked questions: FERPA for school officials. Family Policy Compliance Office. http:\/\/familypolicy.ed.gov\/faq-page\/ferpa-school-officials <\/span><\/span>\n\nUS Supreme Court. (2002). Gonzaga Univ. v. Doe, 536 U.S. 273. https:\/\/supreme.justia.com\/cases\/federal\/ us \/ 536\/273 \/ case.html <\/span><\/span>\n\nVance, A. (2016). Policymaking on education data privacy: Lessons learned<\/i>. Education Leaders Report, 2(1). Alexandria, VA: National Association of State Boards of Education. <\/span><\/span>\n\nVance, A., & Tucker, J. W. (2016). School surveillance: The consequences for equity and privacy<\/i>. Education Leaders Report, 2(4). Alexandria, VA: National Association of State Boards of Education. <\/span><\/span>\n\nWatters, A. (2015, March 17). Pearson, PARCC, privacy, surveillance, & trust. Hack Education: The History of the Future of Education Technology. http:\/\/hackeducation.com\/2015\/03\/17\/pearson-spyWhite House. (2014). Big data: Seizing opportunities, preserving values<\/i>. Washington, DC: Exceutive Office of the President. <\/span><\/span>\n\nYoung, E. (2015). Educational privacy in the online classroom: FERPA, MOOCs, and the big data conundrum. Harvard Journal of Law and Technology, 28<\/i>(2), 549\u2013593. <\/span><\/span>\n\nZeide, E. (2016a). Student privacy principles for the age of big data: Moving beyond FERPA and FIPPs. Drexel Law Review, 8<\/i>(2), 339. <\/span><\/span>\n\nZeide, E. (2016b, March 16). Interview with ED Chief Privacy Officer Kathleen Styles. Washington, D.C.<\/span><\/span>\n\n
\n\n
\n

1<\/a> Aile E\u011fitim Haklar\u0131 ve Mahremiyet Yasas\u0131 ((2014) i\u00e7in : bk. https:\/\/www2.ed.gov\/policy\/gen\/guid\/fpco\/ferpa\/index.html?src=rn and https:\/\/www2.ed.gov\/policy\/gen\/guid\/fpco\/pdf\/ferparegs.pdf<\/span><\/span><\/p>\n\n<\/div>\n

\n

2<\/a> Aile E\u011fitim Haklar\u0131 ve Mahremiyet Yasas\u0131 (AEHMY), 20 U. S. C. v\u00a7 1232g; 34 CFR Part 99 (2014); 34 CFR \u00a7 99.31 (exceptions): https:\/\/www.law.cornell.edu\/uscode\/text\/20\/1232g; https:\/\/www2.ed.gov\/policy\/gen\/guid\/fpco\/pdf\/ferparegs.pdf.<\/span><\/span><\/p>\n\n<\/div>\n

\n

3<\/a> Id. \u00a7 99.31(a)(1) (Okul Resmi \u0130stisna).<\/span><\/span><\/p>\n\n<\/div>\n

\n

4<\/a> Id. \u00a7 99.31(a)(6) (\u00c7al\u0131\u015fmalar Hari\u00e7).<\/span><\/span><\/p>\n\n<\/div>\n

\n

5<\/a> 20 U.S.C. \u00a7 1232g(b)(1)-(2) (Politika veya Uygulama H\u00fck\u00fcm).<\/span><\/span><\/p>\n\n<\/div>\n

\n

6<\/a> http:\/<\/sub>\/\/<\/sup>studentprivacypledge.org\/?page_id=45<\/span><\/span><\/p>\n\n<\/div>\n

\n

7<\/a> Bununla birlikte, t\u00fcz\u00fck, bir operat\u00f6r\u00fcn, \u00f6rt\u00fcl\u00fc bilgiler de d\u00e2hil olmak \u00fczere, uyarlanabilir veya ki\u015fiselle\u015ftirilmi\u015f \u00f6\u011frenci \u00f6\u011frenme ama\u00e7lar\u0131 i\u00e7in bilgi kullanma yetene\u011fini \u201cs\u0131n\u0131rlamad\u0131\u011f\u0131n\u0131\" belirten bir b\u00f6lmeyi i\u00e7erir. (L) \u201d \u00a7 22584. Bu b\u00f6l\u00fcm\u00fcn yaz\u0131m\u0131 itibariyle, bu kurallar\u0131n pratikte nas\u0131l \u00e7al\u0131\u015faca\u011f\u0131 a\u00e7\u0131k de\u011fildir. \u00d6\u011frenci \u00c7evrimi\u00e7i Ki\u015fisel Bilgileri Koruma Yasas\u0131 (\u00d6\u00c7KBKY), CAL. ve PROF. CODE \u00a7\u00a7 22584\u201322585 (2014), https:\/\/leginfo.legislature.ca.gov\/faces\/billNavClient.xhtml?bill_id=201320140SB1177.<\/span><\/span><\/p>\n\n<\/div>\n","rendered":"

Elana Zeide<\/span><\/span><\/p>\n

Bilgi Teknolojileri Politikas\u0131 Merkezi, Princeton \u00dcniversitesi, ABD Bilgi Toplumu Projesi, Yale Hukuk Fak\u00fcltesi, ABD Bilgi Hukuku Enstit\u00fcs\u00fc, New York \u00dcniversitesi Hukuk Fak\u00fcltesi, ABD<\/span><\/span><\/p>\n

DOI: 10.18608\/hla17.028<\/span><\/span><\/p>\n

\u00d6Z<\/h2>\n

Bu kitapta tart\u0131\u015f\u0131lan \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi b\u00fcy\u00fck umutlar vaat etmektedir. Bununla beraber, b\u00fcy\u00fck veri odakl\u0131 e\u011fitimin muhtemel sonu\u00e7lar\u0131 \u00f6zellikle g\u00fcvenlik ve mahremiyet konular\u0131nda endi\u015fe uyand\u0131rmaktad\u0131r. Bu b\u00f6l\u00fcmde, \u00f6\u011frenci verilerini d\u00fczenleyen yasal \u00e7er\u00e7evenin, \u00f6\u011frenme analiti\u011finin ve e\u011fitsel veri madencili\u011finin mahremiyet ihlali ve buna kar\u015f\u0131 geli\u015ftirilecek proaktif yakla\u015f\u0131mlar a\u00e7\u0131klanmaktad\u0131r. Hukuki d\u00fczenlemelere daha az; konuyla ilgili endi\u015fe ve \u00e7\u00f6z\u00fcm \u00f6nerilerine daha fazla yer verilmi\u015ftir. Geleneksel \u00f6\u011frenci mahremiyet hukuku, ebeveynlerin veya okullar\u0131n \u00f6\u011frenci bilgilerinin a\u00e7\u0131klanmas\u0131n\u0131n onaylamas\u0131n\u0131 sa\u011flamaya odaklan\u0131r. Bu d\u00fczenlemeler “\u00f6\u011frenci verileri” ne de\u011fil “e\u011fitim kay\u0131tlar\u0131\u201dna uygulanacak \u015fekilde tasarlanm\u0131\u015ft\u0131r. Sonu\u00e7 olarak bundan b\u00f6yle anlaml\u0131 bir g\u00f6zetim sa\u011flamamaktad\u0131r. \u00d6nceki federal \u00f6\u011frenci mahremiyeti mevzuat\u0131, do\u011frudan \u00f6\u011frencilerden toplanan “\u00f6\u011frenen” verilerini korumamaktad\u0131r ve ayn\u0131 zamanda ihlal ile ilgili do\u011frudan bir yapt\u0131r\u0131m da uygulamamaktad\u0131r. Yeni mevzuat d\u00fczenlemesi de mahremiyetle ilgili korumalar\u0131 \u00f6ng\u00f6rmemi\u015f olmakla birlikte; genellikle belirli uygulamalar\u0131n felaket etkisi olu\u015fturmas\u0131n\u0131 \u00f6nlemeye veya verilerin \u201cticari\u201d kullan\u0131m\u0131n\u0131 s\u0131n\u0131rland\u0131rmaya \u00e7al\u0131\u015fmaktad\u0131r. Bu g\u00f6z ard\u0131 edilmi\u015f etik problemler b\u00fcy\u00fck verilerin e\u011fitim ama\u00e7l\u0131 kullan\u0131m\u0131 ile s\u0131n\u0131rland\u0131r\u0131lmas\u0131 halinde dahi varl\u0131\u011f\u0131n\u0131 s\u00fcrd\u00fcrmektedir. Bu makalede salt yasal metni a\u00e7\u0131klaman\u0131n \u00f6tesine ge\u00e7en ve daha geni\u015f sonu\u00e7lar\u0131 ve etik problemleri g\u00f6z \u00f6n\u00fcnde bulunduran, a\u00e7\u0131k inceleme protokolleri uygulayan, anlaml\u0131 bir \u015feffafl\u0131k sa\u011flayan ve algoritmik hesap verebilirli\u011fi temin eden proaktif bir yakla\u015f\u0131m \u00f6neriyorum.<\/span><\/span><\/p>\n

Anahtar Kelimeler<\/span>: KA\u00c7D’ler, sanal \u00f6\u011frenme ortamlar\u0131, ki\u015fiselle\u015ftirilmi\u015f \u00f6\u011frenme, \u00f6\u011frenci mahremiyeti, e\u011fitim verileri, ed tech, AEHMY, \u00d6\u00c7KBKY, veri eti\u011fi, ara\u015ft\u0131rma eti\u011fi<\/span><\/span><\/p>\n

\u00d6ncelikle bir uyar\u0131da bulunmak isterim ki: yasal d\u00fczenlemeler ve ihtiya\u00e7lar s\u00fcrekli olarak de\u011fi\u015fti\u011finden, bu b\u00f6l\u00fcmde yer alan a\u00e7\u0131klamalar uyum i\u00e7in bir rehber olarak kullan\u0131lmamal\u0131d\u0131r. Bunun yerine, insanlar\u0131n \u201c\u00f6\u011frenci mahremiyeti\u201d ba\u015fl\u0131\u011f\u0131 alt\u0131nda tart\u0131\u015ft\u0131klar\u0131 konular\u0131 ve s\u0131kl\u0131kla ihmal edilen daha da geni\u015f konular\u0131 d\u00fc\u015f\u00fcnmenin farkl\u0131 yollar\u0131n\u0131 sunar. Amerika Birle\u015fik Devletleri’nde mahremiyet kurallar\u0131 farkl\u0131 sekt\u00f6rlere g\u00f6re de\u011fi\u015fiklik g\u00f6stermektedir. \u00d6\u011frencilerin ki\u015fisel bilgilerin gizlili\u011fine y\u00f6nelik geleneksel yakla\u015f\u0131mlar, \u00f6zellikle Aile E\u011fitimi Haklar\u0131 ve Mahremiyet Yasas\u0131 (AEHMY)1<\/a><\/sup> okullar\u0131n e\u011fitim kay\u0131tlar\u0131nda tutulan ki\u015fisel bilgi olarak da tan\u0131mlanabilen \u00f6\u011frenci bilgilerine eri\u015fimi ve bunlar\u0131n eri\u015fimine izin konular\u0131n\u0131 d\u00fczenlemeye odaklan\u0131r. Sadece e\u011fitim konusunda yetkilendirilmi\u015f akt\u00f6rlerin kurumsal a\u00e7\u0131klamalarda kullan\u0131m amac\u0131 ile, ki\u015fisel olarak tan\u0131mlanan \u00f6\u011frenci bilgilerine eri\u015fimini sa\u011flamak i\u00e7in, a\u00e7\u0131k r\u0131za ve kurumsal g\u00f6zetim \u015fart\u0131 aranmaktad\u0131r. Bu yakla\u015f\u0131m, 1970’lerin ba\u015f\u0131ndan bu yana \u00e7o\u011fu gizlilik d\u00fczenlemesinin \u00f6z\u00fcnde yer alan Adil Bilgi Uygulama \u0130lkeleri -bildirim, se\u00e7im, eri\u015fim ve de\u011fi\u015ftirme hakk\u0131- ile uyumludur. Bu ilk d\u00f6nem mahremiyet kurallar\u0131 ayr\u0131ca \u00f6ncelikli olarak e\u011fitimcilerin toplama, kullanma veya e\u011fitim kay\u0131tlar\u0131n\u0131n tutulmas\u0131n\u0131 ele almadan \u00f6\u011frenci bilgilerinin a\u00e7\u0131klanmas\u0131na (kullan\u0131lmas\u0131na) odaklanmaktad\u0131r.<\/span><\/p>\n

\u00d6\u011frencilerin mahremiyetine ili\u015fkin yeni yakla\u015f\u0131mlar, belirli uygulamalar\u0131 yasaklamak veya \u201ce\u011fitsel\u201d ama\u00e7lara hizmet etmelerini istemek e\u011filimindedir. K\u00fcnt yasaklar genellikle e\u011fitim veri ekosistemi i\u00e7inde var olmak bir yana b\u00fcy\u00fcmeye ve yenili\u011fe destek olmak i\u00e7in bile \u00e7ok kabaca haz\u0131rlanm\u0131\u015ft\u0131r. “E\u011fitim” ama\u00e7l\u0131 yap\u0131lan k\u0131s\u0131tlamalar, \u00f6\u011frenci verilerinin a\u00e7\u0131k \u201cticari\u201d kullan\u0131m\u0131n\u0131 s\u0131n\u0131rlamakla birlikte e\u011fitim ama\u00e7l\u0131 olarak e\u011fitimciler taraf\u0131ndan kullan\u0131ld\u0131\u011f\u0131nda bile \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi sonucunda ortaya \u00e7\u0131kan daha ayr\u0131nt\u0131l\u0131 sorunlar\u0131n \u00fczerinde durulmamaktad\u0131r. B\u00fcy\u00fck verilerin e\u011fitime hizmet ama\u00e7l\u0131 kullan\u0131m\u0131n\u0131n e\u011fitimin b\u00fct\u00fcn payda\u015flar\u0131n\u0131n \u00e7\u0131karlar\u0131na uymayabilece\u011fi \u00f6ng\u00f6r\u00fclememi\u015ftir. Kati suretle yasaklay\u0131c\u0131 olarak \u015fekillenen mevzuat\u0131n, teknolojik de\u011fi\u015fimin h\u0131zl\u0131 ilerleyi\u015fi ve \u00f6\u011frenme alanlar\u0131nda son derece ba\u011flamsalla\u015fm\u0131\u015f karar verme yetisi ile e\u015fle\u015fmesi i\u00e7in yeterince esnek olmas\u0131 zordur. \u00d6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi y\u00f6ntemini kullanan veri bilimcileri ve karar vericiler, veri g\u00fcd\u00fcml\u00fc ara\u00e7lar\u0131n hedeflerine ula\u015fmas\u0131n\u0131, e\u011fitim sistemine fayda sa\u011flamas\u0131n\u0131 ve daha geni\u015f bir toplumda e\u015fitli\u011fi te\u015fvik etmesini sa\u011flamak i\u00e7in ihtiyatl\u0131 \u00f6ng\u00f6r\u00fc, \u015feffafl\u0131k ve hesap verebilirlik yoluyla uyumlulu\u011fun \u00f6tesine ge\u00e7melidir.<\/span><\/p>\n

E\u011e\u0130T\u0130M KAYDI MAHREM\u0130YET\u0130<\/h2>\n

\u00d6\u011frenci gizlili\u011fi panik dalgas\u0131 1960’lar\u0131n sonu, 1970’lerin ba\u015f\u0131nda meydana geldi. Okullar \u00f6\u011frenciler hakk\u0131nda daha geni\u015f bir dizi bilgi toplamaya ba\u015flad\u0131. E\u011fitimciler ve y\u00f6neticiler d\u00fczenli olarak \u00f6\u011frenci bilgilerini ge\u00e7ici olarak ve genellikle belgesiz olarak payla\u015ft\u0131lar (Divoky, 1974).<\/span><\/p>\n

AEHMY’nin (Aile E\u011fitimi Haklar\u0131 ve Mahremiyet Yasas\u0131) Okul A\u00e7\u0131klamalar\u0131na \u0130tiraz\u0131<\/h3>\n

Buna kar\u015f\u0131l\u0131k, Kongre, 1974’te \u00f6\u011frenci verilerini d\u00fczenleyen birincil federal mevzuat\u0131n\u0131 kabul etti. AEHMY, ebeveynler ve 18 ya\u015f\u0131ndan b\u00fcy\u00fck “uygun \u00f6\u011frencilere” orta\u00f6\u011fretim sonras\u0131 i\u00e7in \u00fc\u00e7 kay\u0131t hakk\u0131 tan\u0131maktad\u0131r. (“ebeveyn” olarak k\u0131salt\u0131lm\u0131\u015ft\u0131r). Federal olarak finanse edilen okullar, il\u00e7eler ve devlete ba\u011fl\u0131 e\u011fitim kurumlar\u0131, ebeveynlere e\u011fitim kurumu veya arac\u0131 kurum (\u201ce\u011fitim akt\u00f6rleri\u201d, k\u0131saltma olarak) taraf\u0131ndan tutulan e\u011fitim kay\u0131tlar\u0131na eri\u015fimini ve bunlar\u0131n do\u011frulu\u011funu sorgulama imk\u00e2n\u0131 sa\u011flamal\u0131d\u0131r. E\u011fitim akt\u00f6rleri, okullar\u0131n kendi ad\u0131na izin vermesine yetki veren bir\u00e7ok istisnaya tabi olarak, ki\u015fisel olarak tan\u0131mlanabilir \u00f6\u011frenci bilgilerini payla\u015fmadan \u00f6nce ebeveynlerin iznini de almal\u0131d\u0131r.2<\/a><\/sup><\/span><\/p>\n

AEHMY, e\u011fitim kurumlar\u0131 ve kurumlar taraf\u0131ndan ki\u015fisel olarak tan\u0131mlanabilir \u00f6\u011frenci bilgilerinin yasal e\u011fitim \u00e7\u0131karlar\u0131 olan onaylanm\u0131\u015f al\u0131c\u0131lara a\u00e7\u0131klanmas\u0131n\u0131n s\u0131n\u0131rland\u0131r\u0131lmas\u0131na odaklanmaktad\u0131r. AEHMY’nin ihtiya\u00e7lar\u0131n\u0131 kar\u015f\u0131lamak i\u00e7in, okullar, birka\u00e7 istisnadan biri olmad\u0131k\u00e7a, bir \u00f6\u011frencinin e\u011fitim kayd\u0131nda tutulan ki\u015fisel olarak tan\u0131mlanabilir bilgileri payla\u015fmadan \u00f6nce ebeveynlerin yaz\u0131l\u0131 onay\u0131n\u0131 almal\u0131d\u0131r. Uygulamada, mahremiyetle ilgili en \u00f6nemli kararlar\u0131 ebeveynler ya da \u00f6\u011frencilerden ziyade e\u011fitimciler al\u0131r ve istisnalar kural haline gelir. (Zeide, 2016a).<\/span><\/p>\n

\u201cE\u011fitsel\u201d \u0130lgi Alanlar\u0131na \u0130li\u015fkin A\u00e7\u0131klama Yapmaya \u0130zin Veren Okullar<\/h3>\n

Okul yetkilisi istisnas\u0131, veri ile ilgili karar vermenin b\u00fcy\u00fck k\u0131sm\u0131n\u0131 okullara ve il\u00e7elere devretmektedir. Okullar, al\u0131c\u0131 : 1) Ger\u00e7ekle\u015ftirilecek hizmetleri kendi adlar\u0131na y\u00fcr\u00fctmek 2) G\u00f6r\u00fcn\u00fcrde bu bilgilere eri\u015fmek i\u00e7in “yasal bir e\u011fitim ilgisi (menfaati) varsa 3) Bilgilerin korunmas\u0131 ile ilgili gerekli \u00f6nlemleri alm\u0131\u015fsa “ki\u015fisel olarak tan\u0131mlanabilen” \u00f6\u011frenci bilgilerini izin almadan payla\u015fabilir.3<\/a><\/sup> E\u011fitimciler, birinin bir okul g\u00f6revlisi olman\u0131n neyi nitelendirdi\u011fine ve neyin me\u015fru bir e\u011fitim \u00e7\u0131kar\u0131n\u0131 olu\u015fturdu\u011funa karar verir ancak bu terimleri herhangi bir ayr\u0131nt\u0131l\u0131 \u015fekilde tan\u0131mlamak zorunda de\u011fildir (ABD E\u011fitim Bakanl\u0131\u011f\u0131, t.y.). Sonu\u00e7 olarak, \u00e7o\u011fu, neredeyse her durumu kapsayacak kadar geni\u015f kriterlere dayanmaktad\u0131r (Zeide, 2016a). Okullar, veriye al\u0131p kullananlar\u0131n ayr\u0131nt\u0131l\u0131 bilgi uygulamalar\u0131n\u0131 kontrol etmek i\u00e7in nadiren aktif \u00f6nlemler al\u0131r, bunun yerine taraflar aras\u0131ndaki \u201cdo\u011frudan kontrol\u201d arac\u0131 olarak hizmet \u015fartlar\u0131na veya s\u00f6zle\u015fmelere dayan\u0131r (Reidenberg vd., 2013).<\/span><\/p>\n

\u00d6\u011frenci Verilerinin Yeniden Kullan\u0131lmas\u0131ndan Engellenen Ara\u015ft\u0131rmac\u0131lar<\/h3>\n

AEHMY, e\u011fitim akt\u00f6rlerinin ara\u015ft\u0131rmac\u0131larla nas\u0131l bilgi payla\u015ft\u0131\u011f\u0131 konusunda daha kat\u0131 \u015fartlar getirmektedir. \u00c7al\u0131\u015fmalar\u0131 belirlenmi\u015f istisnalar ve belirli \u015fartlarla alt\u0131nda yap\u0131lm\u0131\u015f yaz\u0131l\u0131 bir s\u00f6zle\u015fme uyar\u0131nca yapmak zorundad\u0131rlar. \u00c7al\u0131\u015fmalar \u201ckestirimci testlerin geli\u015ftirilmesi, do\u011frulanmas\u0131 veya y\u00f6netilmesi; \u00f6\u011frenci yard\u0131m programlar\u0131n\u0131 y\u00f6netmek veya talimat\u0131 geli\u015ftirmek amac\u0131yla olmal\u0131d\u0131r.4<\/a><\/sup>\u201d Ara\u015ft\u0131rmac\u0131lar, yaln\u0131zca belirli ama\u00e7lar i\u00e7in ki\u015fisel olarak tan\u0131mlanabilir \u00f6\u011frenci bilgilerini kullanabilir ve art\u0131k gerekli olmad\u0131\u011f\u0131nda verileri yok etmelidirler.<\/span><\/p>\n

\u0130taat Odakl\u0131 \u0130cra<\/h3>\n

E\u011fitim akt\u00f6rlerin AEHMY ihlalleri i\u00e7in do\u011frudan hesap verebilirli\u011fi yoktur. Mevzuat, belirli gizlilik ihlallerini \u00f6nlemek yerine bir yap\u0131 in\u015fa etmekle ilgilidir. Neticede, bireysel ihlal \u00f6rnekleri i\u00e7in sonu\u00e7lar do\u011furmaz. \u00d6\u011frenciler ve e\u011fitimciler mevzuat (ABD Y\u00fcksek Mahkemesi, 2002) kapsam\u0131nda ihlallere dava a\u00e7amazlar. Bunun yerine, ABD E\u011fitim Bakanl\u0131\u011f\u0131 (EB), bir e\u011fitim kurumu veya kurulu\u015fu uyumsuzlu\u011fun bir \u201cPolitikas\u0131 veya uygulamas\u0131\u201d varsa federal \u00f6\u011frenci kredileri \u015feklinde destek de d\u00e2hil olmak \u00fczere t\u00fcm federal fonlar\u0131 geri \u00e7ekme g\u00fcc\u00fcne sahiptir.5<\/a><\/sup> Bununla birlikte, Bakanl\u0131k, bu dramatik eylemi, t\u00fcz\u00fc\u011f\u00fcn k\u0131rk y\u0131l \u00f6nce y\u00fcr\u00fcrl\u00fc\u011fe girmesinden bu yana hi\u00e7bir zaman ger\u00e7ekle\u015ftirmedi (Zeide, 2016b; Daggett, 2008). Bu kadar sert bir \u00f6nlem, AEHMY’nin korumak istedi\u011fi \u00f6\u011frencileri incitece\u011finden, bakanl\u0131k bunun yerine e\u011fitim kurumlar\u0131n\u0131n itaat etmesine odaklan\u0131yor. EB’nin b\u00f6yle bir \u201cn\u00fckleer\u201d se\u00e7ene\u011fi (solove, 2012) takip etmesi pek olas\u0131 g\u00f6r\u00fcnm\u00fcyor.<\/span><\/p>\n

\u00d6\u011eRENC\u0130 VER\u0130 MAHREM\u0130YET\u0130<\/h2>\n

Neredeyse k\u0131rk y\u0131ld\u0131r, payda\u015flar a\u011f\u0131rl\u0131kl\u0131 olarak asgari \u015feffafl\u0131k, bilgi \u00fczerinde bireysel kontrol veya uygulamada belirli mevzuat ihlali sonu\u00e7lar\u0131na ra\u011fmen AEHMY’nin korumas\u0131n\u0131 yeterli olarak kabul ettiler. AEHMY’nin d\u00fczenleyici mekanizmalar\u0131, (k\u0131smen) \u00f6\u011frenci verilerini de\u011fil, e\u011fitim kay\u0131tlar\u0131n\u0131 d\u00fczenledi\u011finden, payda\u015flar i\u00e7in yeterli g\u00fcvence sa\u011flamaz. Mevzuat, kapsad\u0131\u011f\u0131 bilgiler, ilgili eylemler ve b\u00fcy\u00fck veri \u00e7a\u011f\u0131nda ge\u00e7erli oldu\u011fu varl\u0131klar a\u00e7\u0131s\u0131ndan yaln\u0131zca dar bir koruma sa\u011flar.<\/span><\/p>\n

E\u011fitim Kay\u0131tlar\u0131ndan \u00d6\u011frenci Verilerine<\/h3>\n

D\u00fc\u015f\u00fck maliyetli depolama, \u00e7evrimi\u00e7i a\u011flar \u00fczerinden anl\u0131k veri aktar\u0131m ve bulut tabanl\u0131 sunucular benzeri g\u00f6r\u00fclmemi\u015f bir hacim, h\u0131z ve \u201cb\u00fcy\u00fck veri” \u00e7e\u015fitlili\u011fi yarat\u0131r (Mayer-Schonberger ve Cukier, 2014). \u00d6\u011frenci bilgileri art\u0131k okul dosyalama dolaplar\u0131nda kilitli k\u00e2\u011f\u0131da bas\u0131lm\u0131\u015f \u201ce\u011fitim kay\u0131tlar\u0131”\u0131ndan ibaret de\u011fildir, Bilgiler, art\u0131k, bulut sunucular\u0131nda saklanan birlikte \u00e7al\u0131\u015fabilen ve an\u0131nda aktar\u0131labilir verilerdir. \u00c7evrimi\u00e7i e\u011fitim ara\u00e7lar\u0131 ve platformlar \u00f6\u011frenciler ile ilgili daha \u00f6nce m\u00fcmk\u00fcn olandan daha fazla ayr\u0131nt\u0131 i\u00e7eren \u00e7ok fazla say\u0131da bilgi \u00fcretir. Okul kimli\u011fi co\u011frafi konumland\u0131rma ve sosyal medya gibi okul d\u0131\u015f\u0131 kaynaklardan elde edilen veriler, e\u011fitim kay\u0131tlar\u0131 ile ilgili geleneksel beklentilerin \u00e7ok \u00f6tesine ge\u00e7mektedir (Alamuddin, Brown ve Kurzweil, 2016). Veri Madencili\u011finden elde \u00f6\u011frenci bilgileri her ne kadar kamuya a\u00e7\u0131k alanlardan elde edilse bile, bir\u00e7ok payda\u015f sistematik olarak \u00f6\u011frenci bilgilenin toplan\u0131p analiz edilmesini rahats\u0131z edici bulmaktad\u0131rlar (Watters, 2015). \u00d6\u011frencilerle ilgili t\u0131klama d\u00fczeyindeki verilerin otomatik olarak yakalanmas\u0131, bulut bili\u015fim a\u011flar\u0131n\u0131n ge\u00e7irgenli\u011fi ve b\u00fcy\u00fck verilerin sonsuz kullan\u0131m\u0131 yeni gizlilik endi\u015feleri do\u011furur (Singer, 2013).<\/span><\/p>\n

AEHMY’nin (veri) al\u0131c\u0131lar\u0131n bilgi uygulamalar\u0131n\u0131n ebeveyn, \u00f6\u011frenci veya okul g\u00f6zetimine olan g\u00fcveni, b\u00fcy\u00fck verilerin miktar\u0131, karma\u015f\u0131kl\u0131\u011f\u0131 ve etkile\u015fimli, dijital arac\u0131l\u0131 ortamlarda otomatik bilgi iletimi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, pratik veya anlaml\u0131 olsa bile m\u00fcmk\u00fcn olmayabilir. Mevzuat, okullar\u0131n kendi gizlilik uygulamalar\u0131n\u0131 bile ele alm\u0131yor ya da \u201c\u00f6\u011frenme ortamlar\u0131nda\u201d kullan\u0131c\u0131lardan do\u011frudan bilgi toplayan ancak federal bir fon almayan kitlesel \u00e7evrimi\u00e7i a\u00e7\u0131k kurs (KA\u00c7D”ler) gibi yeni ba\u011f\u0131ms\u0131z e\u011fitim sa\u011flay\u0131c\u0131lar\u0131n\u0131 kapsam\u0131yor. Payda\u015flar, okullar\u0131n ve \u015firketlerin ne t\u00fcr bilgi toplad\u0131klar\u0131 ve onlar\u0131 nas\u0131l kulland\u0131klar\u0131 hakk\u0131nda \u00e7ok az fikir sahibidir. (Barnes, 2014). E\u011fitimcilerin ve veri al\u0131c\u0131lar\u0131n\u0131n, -\u00f6zellikle AEHMY’nin mevzuat ihlal durumunda do\u011frudan hesap verme y\u00fck\u00fcml\u00fcl\u00fc\u011f\u00fc getirmedi\u011fi durumlarda- verdikleri mahremiyet vaatlerine bile uydu\u011fundan emin olamazlar.<\/span><\/p>\n

Yeni \u00d6\u011frenci Mahremiyet Korumalar\u0131n\u0131n Yayg\u0131nla\u015ft\u0131r\u0131lmas\u0131<\/h3>\n

2013 y\u0131l\u0131ndan bu yana, devleti y\u00f6neten politika yap\u0131c\u0131lar\u0131 410 \u00f6\u011frenci gizlilik kanun teklifini sunarak payda\u015f pani\u011fe cevap verdi: 36 eyalet tekliflerin 73 tanesini yasala\u015ft\u0131rd\u0131. Federal d\u00fczeyde, yasa koyucular AEHMY ve kanun teklifinde \u00f6\u011frenci bilgilerini alan \u015firketleri ve kurulu\u015flar\u0131 do\u011frudan d\u00fczenleyen de\u011fi\u015fiklikler \u00f6nerdi. Koruyucu \u00f6nlemlerin b\u00fcy\u00fck \u00e7o\u011funlu\u011fu Federal olarak finanse edilen P-12 devlet okullar\u0131 i\u00e7in ge\u00e7erlidir ancak neyin \u00f6nemli oldu\u011fu ve \u201c\u00f6\u011frenci mahremiyeti\u201dnin ne anlama geldi\u011fi konusunda bir fikir birli\u011fi yoktur. Bu eyaletlerin, ara\u015ft\u0131rmac\u0131lar\u0131n, kurumlar\u0131n, \u015firketlerin, devletlerin ve federal politika yap\u0131c\u0131lar\u0131n \u00f6\u011frenci verilerini korumay\u0131 \u00f6nerdikleri inan\u0131lmaz \u00e7e\u015fitlilikten a\u00e7\u0131k\u00e7a anla\u015f\u0131lmaktad\u0131r (Demokrasi ve Teknoloji Merkezi, 2016; DQC, 2016; Vance, 2016).<\/span><\/p>\n

Hemen hemen t\u00fcm reform \u00f6nlemleri, daha fazla \u015feffafl\u0131k, hesap verebilirlik ve temel veri g\u00fcvenli\u011fi, g\u00fcvenlik ve y\u00f6netim protokolleri ihtiyac\u0131n\u0131 yans\u0131tmaktad\u0131r. Bir\u00e7o\u011fu, AEHMY’nin okullar\u0131n \u00fc\u00e7\u00fcnc\u00fc taraf sat\u0131c\u0131larla ve e\u011fitim ara\u015ft\u0131rmac\u0131lar\u0131yla nas\u0131l bilgi payla\u015ft\u0131\u011f\u0131na odaklanmas\u0131na devam ediyor. Birka\u00e7 a\u00e7\u0131k\u00e7a bilgi belirli t\u00fcrde veya sosyal medya gibi d\u0131\u015f kaynaklardan okul koleksiyonunu yasaklamaktad\u0131r. Baz\u0131 tedbirler veri ba\u011f\u0131ml\u0131 servis sa\u011flay\u0131c\u0131lar\u0131 do\u011frudan d\u00fczenler (Demokrasi ve Teknoloji Merkezi, 2016; DQC, 2016; Vance, 2016).<\/span><\/p>\n

\u00d6z D\u00fczenleme Takviyeleri<\/h3>\n

Gizlilik y\u00f6netimine daha esnek yakla\u015f\u0131mlar, \u00f6z y\u00f6netimi i\u00e7erir. 300’den fazla \u015firket, Gizlilik Forumu’nun gelece\u011fi ve \u00f6\u011frenci verilerini satmamak gibi on ilke i\u00e7eren yaz\u0131l\u0131m ve Bilgi End\u00fcstrisi Derne\u011fi taraf\u0131ndan olu\u015fturulan bir \u00d6\u011frenci Gizlilik S\u00f6zle\u015fmesini6<\/a><\/sup> imzalad\u0131. \u0130mza verenler, s\u00f6zle\u015fmeye uymad\u0131klar\u0131nda BTK uygulamas\u0131n\u0131 riske atarlar (Singer, 2015). ABD E\u011fitim Bakanl\u0131\u011f\u0131, e\u011fitim kurulu\u015flar\u0131 ve gizlilik uzmanlar\u0131 s\u00fcrekli olarak yeni en iyi uygulama k\u0131lavuzlar\u0131n\u0131 ve gizlilik ara\u00e7lar\u0131n\u0131 yay\u0131nlamaktad\u0131r (Krueger, 2014; Gizlilik Teknik Yard\u0131m Merkezi, 2014). Bununla birlikte, payda\u015flar\u0131n bu kurallara yeterince g\u00fcvenmeleri i\u00e7in, bilgi uygulamalar\u0131, \u00f6\u011frenme analiti\u011fi ama\u00e7lar\u0131 ve potansiyel sonu\u00e7lar ve ihlal durumunda hesap verebilirlik hakk\u0131nda yeterli \u015feffafl\u0131k olmal\u0131d\u0131r.<\/span><\/p>\n

\u00d6\u011eRENC\u0130 G\u0130ZL\u0130L\u0130K BO\u015eLUKLARI<\/h2>\n

\u00d6\u011frenci gizlilik d\u00fczenlemesinin en son a\u015famas\u0131, \u00f6\u011frenci verilerinin daha a\u00e7\u0131k bir \u015fekilde y\u00f6netilmesine ve baz\u0131 ihtiya\u00e7 duyulan \u015feffafl\u0131klara yol a\u00e7sa da reform \u00f6nlemlerinin \u00e7o\u011fu hala AEHMY\u2019nin kusurlar\u0131ndan muzdariptir. \u00c7o\u011fu \u00f6\u011frenci ve payda\u015f\u0131n, e\u011fitim kay\u0131tlar\u0131nda hangi bilgilerin bulundu\u011funu, verilerin faydalar\u0131na nas\u0131l kullan\u0131labilece\u011fine dair belirsiz kavramlar\u0131 ve korumalar\u0131n yerine getirildi\u011fi konusunda asgari g\u00fcvenceleri hala somut bir anlam\u0131 yoktur (Prinsloo ve Rowe, 2015; Rubel ve Jones, 2016; Zeide, 2016a).<\/span><\/p>\n

Minimal Anlaml\u0131 Onay ve G\u00f6zetim<\/h3>\n

AEHMY ve benzeri kurallar, yaln\u0131zca uygun al\u0131c\u0131lar\u0131n \u00f6\u011frenci verilerine eri\u015febilmelerini sa\u011flamak i\u00e7in ebeveyn veya okul g\u00f6zetimine g\u00fcvenir. Bu b\u00fcy\u00fck verilerin miktar\u0131 ve karma\u015f\u0131kl\u0131\u011f\u0131 ve etkile\u015fimli, dijital arac\u0131l\u0131 ortamlarda otomatik bilgi iletimi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, pratik veya anlaml\u0131 olsa da m\u00fcmk\u00fcn olmayabilir. S\u00f6zle\u015fme h\u00fck\u00fcmleri k\u0131smen bir yap\u0131 olu\u015ftursa da okullar\u0131n \u00fc\u00e7\u00fcnc\u00fc taraf bilgi uygulamalar\u0131n\u0131 izlemelerini ve yapt\u0131r\u0131m i\u00e7in pahal\u0131 davalar getirmelerini gerektirir. Son olarak, a\u00e7\u0131klamay\u0131 s\u0131n\u0131rlamak, bu verileri \u201cme\u015fru e\u011fitim \u00e7\u0131karlar\u0131\u201d i\u00e7in ba\u015flang\u0131\u00e7ta kullanan al\u0131c\u0131lar genellikle ayn\u0131 anda kurumsal veya ara\u015ft\u0131rma \u00e7\u0131karlar\u0131na hizmet etti\u011finden (Young, 2015; Zeide, 2016a) \u00f6\u011frenci bilgilerinin uygunsuz kullan\u0131m\u0131n\u0131 \u00f6nlemek i\u00e7in de \u00e7al\u0131\u015fmaz.<\/span><\/p>\n

Basit Kategorik Yasaklamalar<\/h3>\n

Baz\u0131 d\u00fczenlemeler, belirli veri toplama, kullanma ve yeniden haz\u0131rlama i\u015flemlerini tamamen engelleyerek bu sorunu gidermeye \u00e7al\u0131\u015f\u0131r. Bu genellikle e\u011fitim sisteminde mevcut veri kullan\u0131m\u0131 ile \u00e7ak\u0131\u015fan ve gelecek vaat eden \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fini gereksiz yere k\u0131s\u0131tlayan \u00f6nemli sonu\u00e7lara yol a\u00e7ar. Florida’da, \u00f6rne\u011fin, biyometrik bilgi toplama yasa\u011f\u0131 mevcut uygulamalar ve \u00f6zel e\u011fitim \u00f6\u011frencileri ile ilgili yasal y\u00fck\u00fcml\u00fcl\u00fckleri ile \u00e7eli\u015fmektedir. Sonu\u00e7 olarak, bir\u00e7ok devlet, \u00f6\u011frenci gizlili\u011fini sa\u011flama konusundaki ilk giri\u015fimlerini ask\u0131ya almak veya de\u011fi\u015ftirmek zorunda kald\u0131. Silme kurallar\u0131, uzunlamas\u0131na \u00e7al\u0131\u015fmalar i\u00e7in potansiyeli ciddi \u015fekilde s\u0131n\u0131rland\u0131rmakta ve s\u0131kl\u0131kla devlet yasalar\u0131n\u0131n getirdi\u011fi di\u011fer kay\u0131t tutma y\u00fck\u00fcml\u00fcl\u00fckleriyle \u00e7eli\u015fmektedir (Vance, 2016).<\/span><\/p>\n

E\u011fitim Ama\u00e7l\u0131 K\u0131s\u0131tlamalar\u0131n S\u0131n\u0131rlar\u0131<\/h3>\n

Bir\u00e7ok yeni yasa, E\u011fitim (K-12) hizmetleri sunan varl\u0131klar\u0131 kapsayan Kaliforniya’n\u0131n \u00d6\u011frenci \u00c7evrimi\u00e7i Ki\u015fisel Bilgi Koruma Yasas\u0131 (\u00d6\u00c7KBKY) modelini takip eder. Bunlar (yasalar) akt\u00f6rlerin okul g\u00f6zetimi yoluyla bunu yapmaya \u00e7al\u0131\u015fmak yerine \u00f6\u011frenci bilgilerini do\u011frudan nas\u0131l kulland\u0131klar\u0131n\u0131 d\u00fczenlerler. Bu t\u00fcr hizmetlerin \u00e7evrimi\u00e7i sa\u011flay\u0131c\u0131lar\u0131, okullarla s\u00f6zle\u015fmelere sahip olmal\u0131, istek \u00fczerine \u00f6\u011frenci bilgilerini silmeli ve belirtilen \u201cK-12″ ama\u00e7lar\u0131na hizmet etmeyen \u00f6\u011frenen profilleri olu\u015fturamamal\u0131d\u0131r.\u201d7<\/a><\/sup><\/span><\/p>\n

\u00d6\u011frenci verilerini \u201ce\u011fitsel\u201d kullan\u0131m veya ama\u00e7lar ile s\u0131n\u0131rlayan d\u00fczenlemeler, k\u00e2r ama\u00e7l\u0131 ticari k\u00f6t\u00fcye kullan\u0131m\u0131 \u00f6nlemeye \u00e7al\u0131\u015f\u0131r. Bununla birlikte, ama\u00e7 s\u0131n\u0131rlamalar\u0131, \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi ile ortaya \u00e7\u0131kan daha ince ayr\u0131nt\u0131l\u0131 sorunlar\u0131 ele almazlar. Ama\u00e7 s\u0131n\u0131rlama kurallar\u0131, bir “e\u011fitim” amac\u0131n\u0131 neyin olu\u015fturdu\u011fu hakk\u0131ndaki fikir birli\u011fi varsay\u0131m\u0131na dayan\u0131r. Ancak kurumlar\u0131n veya ara\u015ft\u0131rmac\u0131lar\u0131n, bir taraftan kurumsal kaynaklar\u0131 y\u00f6netmek, e\u011fitim sistemini geli\u015ftirmek veya \u00f6\u011frenme bilimini i\u00e7g\u00f6r\u00fc ile ayd\u0131nlatmak i\u00e7in verileri hala yasal olarak kullan\u0131rken, di\u011fer taraftan \u00f6\u011frenen veri konular\u0131n\u0131n hemen e\u011fitim \u00e7\u0131karlar\u0131ndan ba\u015fka hedeflere \u00f6ncelik verebilece\u011fini dikkate almazlar. \u00d6rne\u011fin okullar, marjinal \u00f6\u011frencileri kaynaklar\u0131 korumada veya s\u0131ralamalar\u0131 iyile\u015ftirmede te\u015fvik etmek yerine onlar\u0131 hari\u00e7 tutmak i\u00e7in \u00f6ng\u00f6r\u00fcc\u00fc verileri kullanabilir (Ashman vd., 2014; Drachsler ve Greller, 2016; Rubel ve Jones, 2016; Selwyn, 2014; Slade, 2016).<\/span><\/p>\n

\u00d6\u011frenen Verisini D\u0131\u015far\u0131da B\u0131rakma (G\u00f6zard\u0131 etme)<\/h3>\n

\u00c7o\u011fu yeni yasa, y\u00fcksek\u00f6\u011fretim kurumlar\u0131nda tutulan bilgileri ele almaz. Geleneksel olarak federal finanse edilen e\u011fitim kurumlar\u0131ndan ba\u011f\u0131ms\u0131z olan sanal \u00f6\u011frenme ortamlar\u0131 taraf\u0131ndan toplanan “\u00f6\u011frenen” bilgilerini g\u00f6z ard\u0131 ederler. Bunun yerine, daha fazla izin verici ticari gizlilik rejimi, bu \u00f6zel kurulu\u015flar taraf\u0131ndan toplanan ve kullan\u0131lan verileri y\u00f6netir (ge\u00e7erli devlet yasas\u0131n\u0131n yoklu\u011funda). Bunun anlam\u0131, \u201c\u00f6\u011frenen\u201d verilerin kullan\u0131lmas\u0131 ve if\u015fa edilmesinin, anla\u015f\u0131lmaz, a\u015f\u0131r\u0131 geni\u015f ve haber verilmeksizin de\u011fi\u015fmeye a\u00e7\u0131k oldu\u011fu i\u00e7in k\u00f6t\u00fc \u015f\u00f6hretli olan t\u00fcketici gizlilik politikalar\u0131 ile s\u0131n\u0131rl\u0131 oldu\u011fudur (Jones ve Regner, 2015; Polonetsky ve Tene, 2014; Young, 2015; Zeide, 2016a).<\/span><\/p>\n

E\u011e\u0130T\u0130M VER\u0130S\u0130 ET\u0130\u011e\u0130<\/h2>\n

Toplum bu konularla sekt\u00f6rler i\u00e7erisinde bo\u011fu\u015fmakta ancak bunlar e\u011fitim ortamlar\u0131nda \u00f6zellikle \u015fiddetli durumdad\u0131rlar. E\u011fitim deneyimlerini geli\u015ftirmek isteyen ve d\u00fcr\u00fcstl\u00fckle \u00e7al\u0131\u015fan bireyler olarak, analitik ve e\u011fitim veri madencili\u011fi \u00f6\u011frenmesinde yer alan bilgi uygulamalar\u0131n\u0131n, geleneksel e\u011fitim bilgi uygulamalar\u0131 ve \u00f6\u011frenci verileriyle ilgili normlarla kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda ne kadar devrimci oldu\u011fu g\u00f6r\u00fc\u015f\u00fcn\u00fc kaybetmek kolayd\u0131r. \u00d6\u011frencilerin nadiren, ana ak\u0131m veri odakl\u0131 teknolojilerden vazge\u00e7me konusunda ger\u00e7ek\u00e7i bir se\u00e7enekleri bulunur. E\u011fitim verisi \u00f6zneleri, yaln\u0131zca \u00e7ocuk olabilece\u011finden de\u011fil, ayn\u0131 zamanda \u00f6\u011frenmenin entelekt\u00fcel geli\u015fim i\u00e7in bir dereceye kadar risk almay\u0131 gerektirmesinden dolay\u0131, genel t\u00fcketici ba\u011flam\u0131ndakilerden daha savunmas\u0131zd\u0131rlar. Bu ara\u00e7lar\u0131n, adil f\u0131rsatlar\u0131 geni\u015fletmek yerine kazara azaltabilece\u011fi, e\u011fitim sisteminin daha geni\u015f hedeflerini baltalayabilece\u011fi ve \u00f6\u011frencilere daha az faaliyete imk\u00e2n verebilece\u011fi ve onlar\u0131 daha az de\u011fil tersine daha fazla savunmas\u0131z hale getirebilece\u011fi konular\u0131nda hala \u00e7\u00f6z\u00fclmemi\u015f sorunlar vard\u0131r (Prinsloo ve Slade, 2016; Siemens, 2014).<\/span><\/p>\n

Adil Kazan\u0131mlar<\/h3>\n

\u00d6\u011frenci verileriyle \u00e7al\u0131\u015fanlar\u0131n, teorik ve teknolojik modellerin tarafs\u0131z alan\u0131 yerine ka\u00e7\u0131n\u0131lmaz olarak kusurlu bir ger\u00e7eklikte nas\u0131l sonu\u00e7lar\u0131n ortaya \u00e7\u0131kabilece\u011fini g\u00f6z \u00f6n\u00fcne almalar\u0131 \u00f6nemlidir. Algoritmik modeller az\u0131nl\u0131klara veya daha d\u00fc\u015f\u00fck sosyoekonomik stat\u00fcye sahip \u00f6\u011frencilere kar\u015f\u0131 kazara ayr\u0131mc\u0131l\u0131k yapabilir. Farkl\u0131 etkileri olabilir. \u00d6\u011frenci ba\u015far\u0131s\u0131n\u0131 tahmin eden ara\u00e7lar, daha fazla ba\u015far\u0131 ve yukar\u0131 do\u011fru hareketlili\u011fi te\u015fvik etmek yerine ge\u00e7mi\u015f e\u015fitsizlikleri tekrarlayabilir. G\u00f6r\u00fcn\u00fc\u015fte tarafs\u0131z olan politikalar, d\u00fczensiz uygulamalardan dolay\u0131 derinden adaletsiz sonu\u00e7lar yaratabilir (Boyd ve Crawford, 2011; Citron ve Pasquale, 2014; Barocas ve Selbst, 2014).<\/span><\/p>\n

Daha Geni\u015f E\u011fitim Etkileri<\/h3>\n

S\u0131n\u0131flardan, kameralardan veya sens\u00f6rlerden s\u00fcrekli olarak ayr\u0131nt\u0131l\u0131 bilgi toplamak daha geni\u015f kapsaml\u0131 sonu\u00e7lar ortaya \u00e7\u0131karabilir. Her yerde g\u00f6zetim ve g\u00f6m\u00fcl\u00fc de\u011ferlendirme \u00f6\u011frenci kat\u0131l\u0131m\u0131 ve ifadesi \u00fczerinde \u00fcrpertici bir etkiye sahip olabilir (Boninger ve Molnar, 2016; Vance ve Tucker, 2016). Bu uygulamalar, periyodik y\u00fcksek riskli testlere olan ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 azalt\u0131rken, \u00f6\u011frenme s\u00fcrecinin her an\u0131n\u0131 derinlemesine inceleme alt\u0131na al\u0131rlar. Bu durum sonu\u00e7ta \u00f6\u011frenme ortamlar\u0131nda gerekli olan entelekt\u00fcel risk almaya kar\u015f\u0131 bir so\u011fukluk olu\u015fturarak, veri odakl\u0131 e\u011fitim ara\u00e7lar\u0131na ve uygulay\u0131c\u0131lar\u0131na olan g\u00fcveni zay\u0131flatabilir.<\/span><\/p>\n

Kas\u0131ts\u0131z Yetki A\u015f\u0131m\u0131<\/h3>\n

\u00d6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi sadece pedagojik ve akademik kararlar\u0131n nas\u0131l verildi\u011fini de\u011fil kimlerin bu kararlar\u0131 verdi\u011fini de de\u011fi\u015ftirir. Geleneksel olarak, \u00f6\u011frenciler hakk\u0131nda de\u011ferlendirme yapan ve karar alan bireyler hemen ellerinin alt\u0131nda bulunan ki\u015fisel, ba\u011flamsalla\u015ft\u0131r\u0131lm\u0131\u015f g\u00f6zlem ve bilgilere dayanarak karar alm\u0131\u015ft\u0131r. Belirli sonu\u00e7larla ilgili endi\u015feleri olan ebeveynler, \u00f6\u011frenciler veya y\u00f6neticiler a\u00e7\u0131klama i\u00e7in do\u011frudan ilgili karar vericiye gidebilmekteydiler. Bu durum \u015feffafl\u0131k sa\u011flad\u0131 ve tazminat almak i\u00e7in kolay bir yol yaratt\u0131, dolay\u0131s\u0131yla hesap verebilirlik de sa\u011fland\u0131.<\/span><\/p>\n

Veri odakl\u0131 e\u011fitim ara\u00e7lar\u0131n\u0131 benimsemesi, e\u011fitimcilere \u00f6\u011frenmeyi \u00f6l\u00e7mek i\u00e7in neler gerekti\u011fi, e\u011fitim yoluyla hangi hedeflerin ama\u00e7land\u0131\u011f\u0131 ve bu kararlar\u0131 kimin ald\u0131\u011f\u0131 gibi hususlar\u0131 etkiler. Otomatik ve algoritmik pedagojik ve kurumsal karar verme, otoritenin konumunu geleneksel, fiziksel olarak mevcut bir insandan belirsiz teknolojilere veya uzak \u015firketler ve ara\u015ft\u0131rmac\u0131lara kayd\u0131r\u0131r. Veri odakl\u0131 e\u011fitim, ya\u015famlar\u0131 ve genel olarak e\u011fitim sistemini \u015fekillendiren \u00f6nemli kararlar\u0131 kimin almas\u0131 gerekti\u011fini de\u011fi\u015ftirir. Bunu, de\u011fi\u015fim a\u00e7\u0131k olmadan ve bir\u00e7ok durumda da kas\u0131tl\u0131 olmadan yapar. Verilere kimlerin eri\u015febilece\u011fi ve kullanabilece\u011fine y\u00f6nelik bu de\u011fi\u015fim, g\u00fc\u00e7 ili\u015fkilerini de de\u011fi\u015ftirir. G\u00fcvenlik uzman\u0131 Bruce Schneier (2008), \u201cverilerimizi kontrol eden ki\u015fi hayat\u0131m\u0131z\u0131 kontrol eder\u201d demektedir (paragraf 5). Verilerin teslimi ile ilgili olan yetki devri a\u00e7\u0131k\u00e7a dikkate al\u0131nmal\u0131d\u0131r.<\/span><\/p>\n

UYUMLULUK \u00d6TES\u0130NE GE\u00c7MEK<\/h2>\n

Mevcut ve geli\u015fmekte olan d\u00fczenleyici \u00e7er\u00e7evenin sa\u011flad\u0131\u011f\u0131 imk\u00e2nlar dahilinde, \u00f6\u011frenme analiti\u011fini ve e\u011fitsel veri madencili\u011fi uygulay\u0131c\u0131lar\u0131 ve t\u00fcketicileri bu g\u00fcc\u00fcn \u00e7o\u011funa sahip olacakt\u0131r. Buna g\u00f6re, \u00f6\u011frenci mahremiyetinin ne anlama geldi\u011fini tan\u0131mlama sorumlulu\u011funu \u00fcstlenmek zorunda kalacaklard\u0131r. Teknolojik yap\u0131lar, kavramsal modeller ve \u00f6\u011frenme kazan\u0131mlar\u0131 hakk\u0131ndaki verdikleri kararlar, \u00f6\u011frenme ortamlar\u0131ndaki bilgiye pratikte uygun d\u00fc\u015fen kurallar\u0131 i\u015fler, olu\u015fturur. Bu kararlar\u0131n etrafl\u0131ca d\u00fc\u015f\u00fcn\u00fclerek ve bilin\u00e7li bir \u015fekilde verilmesi gerekir. Ayn\u0131 zamanda \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fine y\u00f6nelik bireysel kat\u0131l\u0131m, kurumsal uygulama ve politika yap\u0131c\u0131 deste\u011fi i\u00e7in gerekli g\u00fcveni besleyerek bir alan olarak \u00f6\u011frenme analiti\u011fi ve genel olarak e\u011fitsel veri madencili\u011fine yarar sa\u011flar.<\/span><\/p>\n

Daha proaktif bir yakla\u015f\u0131m benimsemek i\u00e7in salt uyumlulu\u011fun \u00f6tesine ge\u00e7meyi tavsiye ederim. \u0130deal olarak, bu sadece potansiyel sorunlar\u0131 \u00f6ng\u00f6rmekle kalmaz, ayn\u0131 zamanda sorunlar ortaya \u00e7\u0131karsa uygulamalara karar vermek \u00fczere protokolleri devreye koymay\u0131 ve veri sahipleri ve payda\u015flarla ileti\u015fim kurmay\u0131 i\u00e7erir. Proaktif \u00f6\u011frenci mahremiyeti uygulamalar\u0131n\u0131n temel bile\u015fenleri \u015funlard\u0131r: 1) etik etkilerin g\u00f6z \u00f6n\u00fcne al\u0131nmas\u0131; 2) inceleme i\u00e7in a\u00e7\u0131k protokollerin olu\u015fturulmas\u0131; 3) veri uygulamalar\u0131, ama\u00e7lar\u0131 ve korumas\u0131 hakk\u0131nda veri sahipleri ve payda\u015flarla etkin olarak ileti\u015fim kurulmas\u0131 ve 4) algoritmik hesap verebilirli\u011fin sa\u011flanmas\u0131.<\/span><\/p>\n

Etik \u0130nceleme<\/h3>\n

\u00d6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi projeleri, gizil faydalar\u0131n proaktif olarak g\u00f6z \u00f6n\u00fcne al\u0131nmas\u0131n\u0131, toplumda, zaman i\u00e7inde da\u011f\u0131l\u0131m\u0131n\u0131, \u00f6\u011frenmeyle, daha geni\u015f toplumla ilgili beklenmedik kazan\u0131mlar\u0131 ve deney protokolleri ile nihai \u00f6nceliklere y\u00f6nelik etik sorular\u0131 i\u00e7ermelidir. Bunlar, insan denekler \u00fczerinde yap\u0131lan deneylere y\u00f6nelik 1978 y\u0131l\u0131nda Belmont raporunda ilan edilen ve daha sonra akademik ara\u015ft\u0131rmalar\u0131 onaylamakla sorumlu Kurumsal \u0130nceleme Kurullar\u0131 (K\u0130K) arac\u0131l\u0131\u011f\u0131yla sistemle\u015ftirilen ve kurumsalla\u015ft\u0131r\u0131lan \u00f6nemli konular\u0131 yans\u0131tmaktad\u0131r. Bununla birlikte kurumlar\u0131n i\u00e7inde veri kullan\u0131m\u0131, ara\u015ft\u0131rma yerine \u201coptimizasyon\u201d olarak s\u0131n\u0131fland\u0131r\u0131lan etkinlikler ve \u015firket uygulamalar\u0131 dikkate al\u0131nd\u0131\u011f\u0131nda benzer temel etik ilkeler nadiren g\u00f6z \u00f6n\u00fcnde bulundurulmaktad\u0131r.<\/span><\/p>\n

\u00d6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi alan\u0131ndaki uygulay\u0131c\u0131lar, konsorsiyumlar ve destekleyiciler bilgi uygulamalar\u0131n\u0131 y\u00f6nlendirmek i\u00e7in etik ilkeleri uygulamaya koymu\u015flard\u0131r. Bunlar, algoritmik analizin bilgi ve karma\u015f\u0131kl\u0131\u011f\u0131 ve hacmi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, \u00f6\u011frenme sistemleri i\u00e7inde verilerin nas\u0131l topland\u0131\u011f\u0131na, sakland\u0131\u011f\u0131na, i\u015flendi\u011fine ve payla\u015f\u0131ld\u0131\u011f\u0131na dair kullan\u0131c\u0131 bildirimi ve izninin \u00f6nemi ve zorlu\u011fu d\u00e2hil \u00f6nemli konular\u0131 g\u00fcndeme getirmektedir. Ayr\u0131ca, deneysel sonu\u00e7lar\u0131n \u201ctoplum yarar\u0131na\u201d (Drachsler ve Greller, 2016; Open University, 2017; Pardo ve Siemens, 2014; Sclater ve Bailey, 2015; Slade, 2016; Asilomar, 2014) hizmet edip etmedi\u011fini dikkate alan daha soyut adalet ve hay\u0131rseverlik kavramlar\u0131n\u0131 da i\u00e7erirler.<\/span><\/p>\n

A\u00e7\u0131k \u0130nceleme<\/h3>\n

Gizlilik ve etik hususlar, teknolojinin ve deneysel tasar\u0131m\u0131n ilk a\u015famalar\u0131ndan itibaren d\u00e2hil edilmelidir. En az\u0131ndan, veri odakl\u0131 e\u011fitim ara\u00e7lar\u0131, istenmeyen \u00f6nyarg\u0131ya, farkl\u0131 etkiye ve toplum genelinde risk ve faydalar\u0131n orant\u0131s\u0131z da\u011f\u0131l\u0131m\u0131na kar\u015f\u0131 denetlenmelidir. En iyi uygulama, olas\u0131 ama \u00f6ng\u00f6r\u00fclebilir, sorunlu sonu\u00e7lar\u0131 zaman\u0131nda ele almak i\u00e7in proaktif \u00f6nlemler olu\u015fturacakt\u0131r. \u00d6rne\u011fin, iki deneysel grup aras\u0131ndaki tutars\u0131zl\u0131\u011f\u0131n, ara\u015ft\u0131rmac\u0131lar ve e\u011fitimcilerin A\/B testini durdurmalar\u0131n\u0131 gerektirecek kadar y\u00fcksek oldu\u011fu bir nokta var m\u0131d\u0131r?<\/span><\/p>\n

Projeler, a\u00e7\u0131k hesap verebilirlik ve etik inceleme i\u00e7in \u00f6nceden belirlenmi\u015f noktalara\/a\u015famalara sahip olmal\u0131d\u0131r. Bir \u00e7ok \u015firket, \u00f6rne\u011fin, deneylerde ilerlemeden \u00f6nce ve yine yay\u0131nlanmadan \u00f6nce veri sahiplerinin ve daha geni\u015f bir toplulu\u011fun ilgi ve isteklerini g\u00f6z \u00f6n\u00fcnde bulundurmak i\u00e7in kendi \u201ct\u00fcketici ara\u015ft\u0131rma kurullar\u0131n\u0131\u201d \u00e7al\u0131\u015ft\u0131rmaya ba\u015flam\u0131\u015ft\u0131r (Calo, 2013; Jackman ve Kanerva, 2016; Tene ve Polonetsky, 2015).<\/span><\/p>\n

Sald\u0131rgan Saydaml\u0131k<\/h3>\n

\u0130deal olarak, \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi ara\u00e7lar\u0131 ve teknolojileri de anlaml\u0131 \u015feffafl\u0131k ve algoritmik hesap verebilirlik sa\u011flamal\u0131d\u0131r. \u015eeffafl\u0131k mikro -ve makro- d\u00fczeyde \u00f6nemlidir. Bilgi uygulamalar\u0131n\u0131 if\u015fa etmek, analitik ve e\u011fitsel veri madencili\u011fi veri uygulamalar\u0131n\u0131 \u00f6\u011frenme konusunda yeterince \u00f6zg\u00fcl ve haz\u0131r bilgilerin yoklu\u011funda panik yapabilecek payda\u015flara g\u00fcven vermeye yard\u0131mc\u0131 olur.<\/span><\/p>\n

Veri analizinin sadece -\u00e7ok uzak bir yerde gelecekteki bir \u00f6\u011frenciye de\u011fil- mevcut \u00f6\u011frenenlere de fayda sa\u011flayabilece\u011fi yollarla ilgili \u015feffafl\u0131k ve \u00f6tesine ge\u00e7i\u015f, payda\u015f korkular\u0131n\u0131 hafifletmeye yard\u0131mc\u0131 olur. A\u00e7\u0131k ve erken ileti\u015fim, k\u00fc\u00e7\u00fck bir elit bilim grubunun \u00f6\u011frenci deneyimleri ve sonu\u00e7lar\u0131 \u00fczerinde muazzam bir kontrole sahip oldu\u011fu ve eylemlerinin gizlilik i\u00e7inde \u00f6rt\u00fcld\u00fc\u011f\u00fc izlenimini azaltmaya yard\u0131mc\u0131 olur. Veri sahiplerine ve daha geni\u015f toplulu\u011fa ula\u015fman\u0131n yollar\u0131n\u0131 bulmak i\u00e7in kurumsal kaynaklar\u0131 \u00e7al\u0131\u015ft\u0131rmaya yard\u0131mc\u0131 olur.<\/span><\/p>\n

Algoritmik Sorumluluk<\/h3>\n

Ancak \u015feffafl\u0131k uygun bilgi uygulamalar\u0131n\u0131 sa\u011flamak i\u00e7in yeterli de\u011fildir. Bu bir \u00f6n ko\u015fuldur. Sorundaki riskler ve algoritmik karar vermenin belirsizli\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda belgelendirme ve hesap verebilirlik de \u00f6nemlidir. \u00d6\u011frenciler ve payda\u015flar pedagojik ve kurumsal karar vermeyi hangi kan\u0131tlar\u0131n destekledi\u011fini bilmek isteyecektir. \u0130deal olarak, \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi uygulay\u0131c\u0131lar\u0131 algoritmik hesap verebilirlik i\u00e7in ara\u00e7lar geli\u015ftirmelidir. Bunlar, algoritmik ara\u00e7lar\u0131n ama\u00e7land\u0131\u011f\u0131 gibi \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 ve asl\u0131nda vaat edilen sonu\u00e7lar\u0131 destekledi\u011fini \u00e7ifte kontrol eden denetimleri i\u00e7erir.<\/span><\/p>\n

\u00d6\u011frencilerin gelecekteki f\u0131rsatlar\u0131n\u0131 etkilemede giderek daha \u00f6nemli hale gelecek olan algoritmik hesap verebilirli\u011fin \u00f6nemli bir par\u00e7as\u0131, yasal s\u00fcrecin i\u015flemesi i\u00e7in algoritmik ve kurumsal karar vermeyi belgelendirme gere\u011fidir (Diakopoulos, 2016; Kobie), 2016; Kroll vd., 2017). \u00d6\u011freniciler, e\u011fitimciler ve kurumlar kan\u0131tlar\u0131 g\u00f6rmek ve akademik geli\u015fimlerini etkileyip yeterlilik belgesi veren sistemler hakk\u0131nda bilgi sahibi olmak ve kendilerini etkileyen kararlar\u0131 incelemek isteyeceklerdir (Zeide, 2016a; ayr\u0131ca bk. Citron ve Pasquale, 2014; Crawford ve Schultz, 2014). Veri bilimcileri ve veri odakl\u0131 karar vericiler, \u00f6nemli kararlar\u0131n adli olarak incelenmesini kolayla\u015ft\u0131rmak i\u00e7in haz\u0131rlanmal\u0131d\u0131r. \u00d6rne\u011fin, ebeveynler, \u00e7ocuklar\u0131n\u0131n neden bir \u00fcst s\u0131n\u0131fa ge\u00e7tikleri veya ge\u00e7mediklerine ili\u015fkin bir a\u00e7\u0131klama isteyeceklerdir. \u201c\u00c7\u00fcnk\u00fc algoritma \u00f6yle dedi\u201d yeterli bir cevap olmayacakt\u0131r.<\/span><\/p>\n

SONU\u00c7<\/h2>\n

G\u00fcven, entelekt\u00fcel deneyselli\u011fi ve b\u00fcy\u00fcmeyi te\u015fvik etmek isteyen \u00f6\u011frenme ortamlar\u0131 i\u00e7in \u00e7ok \u00f6nemlidir. Bu durum Beyaz Saray\u0131n b\u00fcy\u00fck verilerle ilgili 2014 tarihli bir raporunda da \u015f\u00f6yle belirtilmi\u015ftir: \u201c\u00d6\u011frenmenin kendisi bir deneme yan\u0131lma s\u00fcreci oldu\u011fu i\u00e7in, verileri bu yeniliklerin yararlar\u0131n\u0131 sa\u011flayacak ancak yine de \u00f6\u011frencilerin ke\u015ffetmek, hata yapmak ve \u00f6\u011frenme s\u00fcrecinin bir par\u00e7as\u0131 olan hatalar\u0131n uzun vadeli sonu\u00e7lar\u0131 olaca\u011f\u0131n\u0131 d\u00fc\u015f\u00fcnmeden \u00f6\u011frenmek i\u00e7in g\u00fcvenli bir alan yaratacak \u015fekilde kullanmak gerekir.\u201d<\/span><\/p>\n

Salt \u015fartlara uyman\u0131n \u00f6tesine de ge\u00e7erek, e\u011fitim verileri kendilerine emanet edilenler, en iyi niyetli projelerin bile ula\u015fmak istedikleri hedefleri zay\u0131flatabilecek olas\u0131 istenmeyen sonu\u00e7lar\u0131na kar\u015f\u0131 koruma sa\u011flayabilir. Bu el kitab\u0131n\u0131n okurlar\u0131, \u00f6\u011frenci verilerinin zenginli\u011fi ile emanet edilmi\u015f, yaln\u0131zca uyumu de\u011fil, daha geni\u015f sosyal, etik ve politik sonu\u00e7lar\u0131 g\u00f6z \u00f6n\u00fcnde bulunduracak \u015fekilde \u00f6tesine ge\u00e7en proaktif bir yakla\u015f\u0131m benimsemelidir. Bunu yapmak, veri odakl\u0131 e\u011fitime olan g\u00fcveni art\u0131racak ve \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011finin devrimci potansiyellerine ula\u015fmas\u0131n\u0131 sa\u011flayacakt\u0131r.<\/span><\/p>\n

KAYNAK\u00c7A<\/h2>\n

Alamuddin, R., Brown, J., & Kurzweil, M. (2016). Student data in the digital era: An overview of current practices<\/i>. Ithaka S+R. doi:10.18665\/.283890 <\/span><\/span><\/p>\n

Ashman, H., Brailsford, T., Cristea, A. I., Sheng, Q. Z., Stewart, C., Toms, E. G., & Wade, V. (2014). The ethical and social implications of personalization technologies for e-learning. Information & Management, 51<\/i>(6), 819\u2013832. doi:10.1016\/j.im.2014.04.003 <\/span><\/span><\/p>\n

Asilomar Convention for Learning Research in Higher Education. (2014). Student data policy and data use messaging for consideration at Asilomar II<\/i>. Asilomar, (CA). http:\/\/asilomar-highered.info\/ Barnes, K. (2014, March 6). Why a \u201cStudent Privacy Bill of Rights\u201d is desperately needed. Washington Post. http:\/\/www.washingtonpost.com\/blogs\/answer-sheet\/wp\/2014\/03\/06\/why-a-student-privacy-bill-of-rights-is-desperately-needed\/ <\/span><\/span><\/p>\n

Barocas, S., & Selbst, A. D. (2014). Big data\u2019s disparate impact<\/i>. SSRN Scholarly Paper. Elsevier. http:\/\/papers.ssrn.com\/abstract=2477899 <\/span><\/span><\/p>\n

Boninger, F., & Molnar, A. (2016). Learning to be watched: Surveillance culture at school<\/i>. Boulder, CO: National Education Policy Center. http:\/\/nepc.colorado.edu\/files\/publications\/RB%20Boninger-Molnar%20 Trends.pdf <\/span><\/span><\/p>\n

Boyd, D., & Crawford, K. (2011). Six provocations for big data. SSRN Scholarly Paper. Elsevier. http:\/\/papers.ssrn.com\/abstract=1926431 <\/span><\/span><\/p>\n

Calo, R. (2013). Consumer subject review boards: A thought experiment. Stanford Law Review Online, 66<\/i>, 97. <\/span><\/span><\/p>\n

Center for Democracy and Technology (2016, October 5). State student privacy law compendium<\/i>. https:\/\/cdt.org\/insight\/state-student-privacy-law-compendium\/ <\/span><\/span><\/p>\n

Citron, D. K., & Pasquale, F. A. (2014). The scored society: Due process for automated predictions. Washington Law Review, 89<\/i>. http:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2376209 <\/span><\/span><\/p>\n

Crawford, K., & Schultz, J. (2014). Big data and due process: Toward a framework to redress predictive privacy harms. Boston College Law Review, 55<\/i>(1), 93. <\/span><\/span><\/p>\n

Daggett, L. M. (2008). FERPA in the twenty-first century: Failure to effectively regulate privacy for all students. Catholic University Law Review, 58<\/i>, 59. <\/span><\/span><\/p>\n

Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59<\/i>(2), 56\u201362. doi:10.1145\/2844110 <\/span><\/span><\/p>\n

Divoky, D. (1974, March 31). How secret school records can hurt your child. Parade Magazine<\/i>, 4\u20135. <\/span><\/span><\/p>\n

DQC (Data Quality Campaign) (2016). Student data privacy legislation: A summary of 2016 state legislation<\/i>. http:\/\/2pido73em67o3eytaq1cp8au.wpengine.netdna-cdn.com\/wp-content\/uploads\/2016\/09\/DQC-Legislative-summary-09232016.pdf <\/span><\/span><\/p>\n

Drachsler, H., & Greller, W. (2016). Privacy and analytics \u2014 it\u2019s a DELICATE issue. A checklist to establish trusted learning analytics. Open Universiteit. http:\/\/dspace.ou.nl\/bitstream\/1820\/6381\/1\/Privacy%20a%20 DELICATE%20issue%20(Drachsler%20%26%20Greller)%20-%20submitted.pdf <\/span><\/span><\/p>\n

Jackman, M., & Kanerva, L. (2016). Evolving the IRB: Building robust review for industry research. Washington and Lee Law Review Online, 72<\/i>(3), 442. <\/span><\/span><\/p>\n

Jones, M. L., & Regner, L. (2015, August 19). Users or students? Privacy in university MOOCS. Science and Engineering Ethics, 22<\/i>(5), 1473\u20131496. doi:10.1007\/s11948-015-9692-7 <\/span><\/span><\/p>\n

Kobie, N. (2016, January 29). Why algorithms need to be accountable. Wired UK<\/i>. http:\/\/www.wired.co.uk\/ news\/archive\/2016-01\/29\/make-algorithms-accountable <\/span><\/span><\/p>\n

Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165<\/i>. http:\/\/papers.ssrn.com\/sol3\/Papers.cfm?abstract_id=2765268 <\/span><\/span><\/p>\n

Krueger, K.R. (2014). 10 steps that protect the privacy of student data. The Journal, 41<\/i>(6), 8. <\/span><\/span><\/p>\n

Mayer-Sch\u00f6nberger, V., & Cukier, K. (2014). Learning with big data<\/i>. Eamon Dolan \/ Houghton Mifflin Harcourt. <\/span><\/span><\/p>\n

Open University. (2017). Ethical use of student data for learning analytics policy. http:\/\/www.open.ac.uk\/students\/charter\/essential-documents\/ethical-use-student-data-learning-analytics-policy <\/span><\/span><\/p>\n

Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45<\/i>(3), 438\u2013450. doi: 10.1111 \/ bjet.12152<\/span><\/span><\/p>\n

Polonetsky, J., & Tene, O. (2014). Who is reading whom now: Privacy in education from books to MOOCs. Vanderbilt Journal of Entertainment & Technology Law, 17<\/i>, 927. <\/span><\/span><\/p>\n

Prinsloo, P., & Rowe, M. (2015). Ethical considerations in using student data in an era of \u201cbig data.\u201d In W. R. Kilfoil (Ed.), Moving beyond the Hype: A Contextualised View of Learning with Technology in Higher Education <\/i>(pp. 59\u201364). Pretoria, South Africa: Universities South Africa. <\/span><\/span><\/p>\n

Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency and learning analytics: An exploration. Journal of Learning Analytics, 3<\/i>(1), 159\u2013182. <\/span><\/span><\/p>\n

Privacy Technical Assistance Center. (2014) Protecting student privacy while using online educational services: Requirements and best practice<\/i>. http:\/\/ptac.ed.gov\/sites\/default\/files\/Student%20Privacy%20and%20 Online%20Educational%20Services%20%28February%202014%29.pdf <\/span><\/span><\/p>\n

Reidenberg, J. R., Russell, N. C., Kovnot, J., Norton, T. B., Cloutier, R., & Alvarado, D. (2013). Privacy and cloud computing in public schools<\/i>. Bronx, NY: Fordham Center on Law and Information Policy. http:\/\/ir.lawnet.fordham.edu\/clip\/2\/?utm_source=ir.lawnet.fordham.edu%2Fclip%2F2veutm_medium=PDFveutm_campaign=PDFCoverPages <\/span><\/span><\/p>\n

Rubel, A., & Jones, K. M. L. (2016). Student privacy in learning analytics: An information ethics perspective. The Information Society, 32<\/i>(2), 143\u2013159. doi:10.1080\/01972243.2016.1130502 <\/span><\/span><\/p>\n

Schneier, B. (2008, May 15). Our data, ourselves. Wired Magazine<\/i>. http:\/\/www.wired.com\/2008\/05\/securitymatters-0515\/all\/1\/ <\/span><\/span><\/p>\n

Sclater, N., & Bailey, P. (2015). Code of practice for learning analytics<\/i>. https:\/\/www.jisc.ac.uk\/guides\/ code-of-practice-for-learning-analytics <\/span><\/span><\/p>\n

Selwyn, N. (2014). Distrusting educational technology: Critical questions for changing times<\/i>. New York\/London: Routledge, Taylor & Francis Group. <\/span><\/span><\/p>\n

Siemens, G. (2014, January 13). The vulnerability of learning. http:\/\/www.elearnspace.org\/blog\/2014\/01\/13\/ the-vulnerability-of-learning\/ <\/span><\/span><\/p>\n

Singer, N. (2015, March 5). Digital learning companies falling short of student privacy pledge. New York Times<\/i>. http:\/\/bits.blogs.nytimes.com\/2015\/03\/05\/digital-learning-companies-falling-short-of-student-privacy-pledge\/ <\/span><\/span><\/p>\n

Singer, N. (2013, December 13). Schools use web tools, and data is seen at risk. New York Times<\/i>. http:\/\/www.nytimes.com\/2013\/12\/13\/education\/schools-use-web-tools-and-data-is-seen-at-risk.html <\/span><\/span><\/p>\n

Slade, S. (2016). Applications of student data in higher education: Issues and ethical considerations. Presented at Asilomar II: Student Data and Records in the Digital Era. Asilomar, (CA). <\/span><\/span><\/p>\n

Solove, D.J. (2012). FERPA and the cloud: Why FERPA desperately needs reform. http:\/\/www.law.nyu.edu\/ sites \/ default \/ files \/ ECM_PRO_074960.pdf <\/span><\/span><\/p>\n

Tene, O., & Polonetsky, J. (2015). Beyond IRBs: Ethical guidelines for data research. Beyond IRBs: Ethical review processes for big data research<\/i>. https:\/\/bigdata.fpf.org\/wp-content\/uploads\/2015\/12\/Tene-Polonetsky-Beyond-IRBs-Ethical-Guidelines-for-Data-Research1.pdf <\/span><\/span><\/p>\n

US Department of Education (n.d.). FERPA frequently asked questions: FERPA for school officials. Family Policy Compliance Office. http:\/\/familypolicy.ed.gov\/faq-page\/ferpa-school-officials <\/span><\/span><\/p>\n

US Supreme Court. (2002). Gonzaga Univ. v. Doe, 536 U.S. 273. https:\/\/supreme.justia.com\/cases\/federal\/ us \/ 536\/273 \/ case.html <\/span><\/span><\/p>\n

Vance, A. (2016). Policymaking on education data privacy: Lessons learned<\/i>. Education Leaders Report, 2(1). Alexandria, VA: National Association of State Boards of Education. <\/span><\/span><\/p>\n

Vance, A., & Tucker, J. W. (2016). School surveillance: The consequences for equity and privacy<\/i>. Education Leaders Report, 2(4). Alexandria, VA: National Association of State Boards of Education. <\/span><\/span><\/p>\n

Watters, A. (2015, March 17). Pearson, PARCC, privacy, surveillance, & trust. Hack Education: The History of the Future of Education Technology. http:\/\/hackeducation.com\/2015\/03\/17\/pearson-spyWhite House. (2014). Big data: Seizing opportunities, preserving values<\/i>. Washington, DC: Exceutive Office of the President. <\/span><\/span><\/p>\n

Young, E. (2015). Educational privacy in the online classroom: FERPA, MOOCs, and the big data conundrum. Harvard Journal of Law and Technology, 28<\/i>(2), 549\u2013593. <\/span><\/span><\/p>\n

Zeide, E. (2016a). Student privacy principles for the age of big data: Moving beyond FERPA and FIPPs. Drexel Law Review, 8<\/i>(2), 339. <\/span><\/span><\/p>\n

Zeide, E. (2016b, March 16). Interview with ED Chief Privacy Officer Kathleen Styles. Washington, D.C.<\/span><\/span><\/p>\n

\n
\n

1<\/a> Aile E\u011fitim Haklar\u0131 ve Mahremiyet Yasas\u0131 ((2014) i\u00e7in : bk. https:\/\/www2.ed.gov\/policy\/gen\/guid\/fpco\/ferpa\/index.html?src=rn and https:\/\/www2.ed.gov\/policy\/gen\/guid\/fpco\/pdf\/ferparegs.pdf<\/span><\/span><\/p>\n<\/div>\n

\n

2<\/a> Aile E\u011fitim Haklar\u0131 ve Mahremiyet Yasas\u0131 (AEHMY), 20 U. S. C. v\u00a7 1232g; 34 CFR Part 99 (2014); 34 CFR \u00a7 99.31 (exceptions): https:\/\/www.law.cornell.edu\/uscode\/text\/20\/1232g; https:\/\/www2.ed.gov\/policy\/gen\/guid\/fpco\/pdf\/ferparegs.pdf.<\/span><\/span><\/p>\n<\/div>\n

\n

3<\/a> Id. \u00a7 99.31(a)(1) (Okul Resmi \u0130stisna).<\/span><\/span><\/p>\n<\/div>\n

\n

4<\/a> Id. \u00a7 99.31(a)(6) (\u00c7al\u0131\u015fmalar Hari\u00e7).<\/span><\/span><\/p>\n<\/div>\n

\n

5<\/a> 20 U.S.C. \u00a7 1232g(b)(1)-(2) (Politika veya Uygulama H\u00fck\u00fcm).<\/span><\/span><\/p>\n<\/div>\n

\n

6<\/a> http:\/<\/sub>\/\/<\/sup>studentprivacypledge.org\/?page_id=45<\/span><\/span><\/p>\n<\/div>\n

\n

7<\/a> Bununla birlikte, t\u00fcz\u00fck, bir operat\u00f6r\u00fcn, \u00f6rt\u00fcl\u00fc bilgiler de d\u00e2hil olmak \u00fczere, uyarlanabilir veya ki\u015fiselle\u015ftirilmi\u015f \u00f6\u011frenci \u00f6\u011frenme ama\u00e7lar\u0131 i\u00e7in bilgi kullanma yetene\u011fini \u201cs\u0131n\u0131rlamad\u0131\u011f\u0131n\u0131” belirten bir b\u00f6lmeyi i\u00e7erir. (L) \u201d \u00a7 22584. Bu b\u00f6l\u00fcm\u00fcn yaz\u0131m\u0131 itibariyle, bu kurallar\u0131n pratikte nas\u0131l \u00e7al\u0131\u015faca\u011f\u0131 a\u00e7\u0131k de\u011fildir. \u00d6\u011frenci \u00c7evrimi\u00e7i Ki\u015fisel Bilgileri Koruma Yasas\u0131 (\u00d6\u00c7KBKY), CAL. ve PROF. CODE \u00a7\u00a7 22584\u201322585 (2014), https:\/\/leginfo.legislature.ca.gov\/faces\/billNavClient.xhtml?bill_id=201320140SB1177.<\/span><\/span><\/p>\n<\/div>\n","protected":false},"author":1,"menu_order":5,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[48],"contributor":[],"license":[],"class_list":["post-127","chapter","type-chapter","status-publish","hentry","chapter-type-numberless"],"part":109,"_links":{"self":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/chapters\/127","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":0,"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/chapters\/127\/revisions"}],"part":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/parts\/109"}],"metadata":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/chapters\/127\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/wp\/v2\/media?parent=127"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/chapter-type?post=127"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/wp\/v2\/contributor?post=127"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/wp\/v2\/license?post=127"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}