{"id":77,"date":"2020-09-03T16:39:14","date_gmt":"2020-09-03T13:39:14","guid":{"rendered":"http:\/\/acikkitap.com.tr\/oaek\/chapter\/bolum-18-kacdlerde-farkli-buyuk-veri-ve-rastgele-alan-deneyimleri\/"},"modified":"2020-09-03T16:39:14","modified_gmt":"2020-09-03T13:39:14","slug":"bolum-18-kacdlerde-farkli-buyuk-veri-ve-rastgele-alan-deneyimleri","status":"publish","type":"chapter","link":"https:\/\/acikkitap.com.tr\/oaek\/chapter\/bolum-18-kacdlerde-farkli-buyuk-veri-ve-rastgele-alan-deneyimleri\/","title":{"raw":"B\u00f6l\u00fcm 18 KA\u00c7D'lerde Farkl\u0131 B\u00fcy\u00fck Veri ve Rastgele Alan Deneyimleri","rendered":"B\u00f6l\u00fcm 18 KA\u00c7D’lerde Farkl\u0131 B\u00fcy\u00fck Veri ve Rastgele Alan Deneyimleri"},"content":{"raw":"\n

Rene F. Kizilcec1<\/sup>, Christopher Brooks2<\/sup><\/span><\/span><\/p>\n

1<\/sup>\u0130leti\u015fim B\u00f6l\u00fcm\u00fc, Stanford \u00dcniversitesi, ABD <\/span><\/span><\/p>\n

2<\/sup>Bilgi Okulu, Michigan \u00dcniversitesi, ABD<\/span><\/span><\/p>\n

DOI: 10.18608\/hla17.018<\/span><\/span><\/p>\n\n

\u00d6Z<\/h2>\n

B\u00fcy\u00fck \u00f6l\u00e7ekli e\u011fitim i\u00e7eri\u011finin da\u011f\u0131t\u0131m\u0131nda yeni bir mekanizma olan, kitlesel a\u00e7\u0131k \u00e7evrimi\u00e7i dersler (KA\u00c7D) d\u00fcnya \u00e7ap\u0131nda milyonlarca \u00f6\u011frenenin ilgisini \u00e7ekmektedir. KA\u00c7D'lerin yak\u0131n tarihinin k\u0131sa bir \u00f6zeti olan bu b\u00f6l\u00fcm, ara\u015ft\u0131rma arac\u0131 olarak onlar\u0131n potansiyellerine odaklanmaktad\u0131r. \u00d6\u011frenme analiti\u011fi ve daha geni\u015f anlamda \u00f6\u011frenme bilimi hakk\u0131ndaki ara\u015ft\u0131rmalar\u0131 daha ileri seviyeye ta\u015f\u0131mak i\u00e7in bu ortamlar\u0131n iki \u00f6nemli sa\u011flay\u0131c\u0131s\u0131n\u0131 irdelemekteyiz. Bunlardan ilki, e\u011fitimde \u00e7e\u015fitli b\u00fcy\u00fck verilerin mevcudiyetidir. Heterojen \u00f6\u011frenen \u00f6rnekleri ile yap\u0131lan ara\u015ft\u0131rmalar, daha az elde edilen e\u011fitsel veri k\u00fcmelerinde geleneksel olarak yeterince temsil edilmeyen demografik ve sosyok\u00fclt\u00fcrel gruplardan gelen insanlar\u0131 daha iyi a\u00e7\u0131klayan daha kapsaml\u0131 bir \u00f6\u011frenme bilimini ilerletebilir. \u0130kinci sa\u011flay\u0131c\u0131 ise b\u00fcy\u00fck \u00f6l\u00e7ekli saha deneylerini minimum maliyetle yapabilme yetene\u011fidir. Ara\u015ft\u0131rmac\u0131lar \u00e7oklu teoriye dayal\u0131 m\u00fcdahaleleri h\u0131zl\u0131 bir \u015fekilde de\u011ferlendirebilirler ve bu m\u00fcdahalelerin otantik bir \u00f6\u011frenme ortam\u0131ndaki etkilikleri hakk\u0131nda tesad\u00fcfi \u00e7\u0131kar\u0131mlara varabilirler. Farkl\u0131 t\u00fcrdeki b\u00fcy\u00fck veri ve deneyleme bir arada bireysel farkl\u0131l\u0131klar\u0131n nedenini a\u00e7\u0131klayabilecek \u201ckim i\u00e7in neyin i\u015fe yarad\u0131\u011f\u0131\u201d teorilerine kan\u0131t sa\u011flar ve materyalleri etkili bir \u015fekilde belirlemeye y\u00f6nelik giri\u015fimleri ve \u00e7evrim i\u00e7i \u00f6\u011frenme ortamlar\u0131nda olan yap\u0131lar\u0131 destekler.<\/span><\/span><\/p>\n

Anahtar Kelimeler<\/span>: Ara\u015ft\u0131rma metodolojisi, \u00e7ok \u00e7e\u015fitli veriler, randomize saha deneyleri, kapsay\u0131c\u0131 bilim, KA\u00c7D<\/span><\/span><\/p>\n

Kitlesel a\u00e7\u0131k \u00e7evrimi\u00e7i dersler (KA\u00c7D), d\u00fcnya \u00e7ap\u0131nda bir izleyici kitlesine d\u00fc\u015f\u00fck maliyetli e\u011fitim deneyimleri sa\u011flayan teknolojik bir yeniliktir. 2012 y\u0131l\u0131nda, ilk KA\u00c7D'lerden baz\u0131lar\u0131, y\u00fcksek\u00f6\u011frenimdeki aksakl\u0131klar\u0131n giderilmesine ivme kazand\u0131rarak d\u00fcnya \u00e7ap\u0131nda \u00fclkelerden y\u00fcz binlerce insan\u0131n ilgisini \u00e7ekmi\u015ftir(Waldrop, 2013). Sadece birka\u00e7 y\u0131l sonra, d\u00fcnya \u00e7ap\u0131nda y\u00fczlerce kurum, KA\u00c7D'leri Coursera, EdX ve FutureLearn gibi \u00e7evrimi\u00e7i \u00f6\u011frenme platformlar\u0131nda sunmaya ba\u015flad\u0131. Y\u00fcksek\u00f6\u011frenime eri\u015fimin geni\u015fletilmesinin \u00f6tesinde, KA\u00c7D'ler mevcut akademik topluluklardaki burslar\u0131 besleyen ve tarihsel olarak \u00f6\u011frenme bilimlerine daha az d\u00e2hil olan disiplinlere ilgi uyand\u0131ran e\u015fi g\u00f6r\u00fclmemi\u015f miktarda e\u011fitsel veri \u00fcretmi\u015ftir. Bu mevcut disiplinler aras\u0131 topluluklardaki ara\u015ft\u0131rmalar\u0131 g\u00fc\u00e7lendirmi\u015f ve e\u011fitim, bilgisayar bilimi, insan fakt\u00f6rleri ile istatistiklerin kesi\u015fme noktalar\u0131nda tamamen yeni topluluklar\u0131n olu\u015fumuna yol a\u00e7m\u0131\u015ft\u0131r. \u00d6\u011frenme analiti\u011fi alan\u0131nda, yeni nesil ara\u015ft\u0131rmay\u0131 geli\u015ftirebilecek KA\u00c7D'lerin iki yeni \u00f6zelli\u011fini vurguluyoruz: e\u011fitsel verilerin sadece b\u00fcy\u00fck de\u011fil ayn\u0131 zamanda \u00e7e\u015fitli \u00f6\u011frenen d\u00fczeyinde bulunmas\u0131 ve b\u00fcy\u00fck \u00e7evrimi\u00e7i alan deneylerini d\u00fc\u015f\u00fck maliyetle yapma imk\u00e2n\u0131.<\/span><\/p>\n

KA\u00c7D'lerin yenilik\u00e7i ara\u015ft\u0131rmay\u0131 destekleyen ilk \u00f6zelli\u011fi toplanabilecek verilerin miktar\u0131 ve niteli\u011fidir. KA\u00c7D'ler derin ve geni\u015f kapsaml\u0131 \u00f6\u011frenen verilerini toplar: \u00e7ok say\u0131da \u00f6\u011frenen i\u00e7in her bir \u00f6\u011frencinin \u00f6\u011frenme ortam\u0131ndaki i\u00e7erikle etkile\u015fimlerinden elde edilen elveri\u015fli kay\u0131tlar (Thille vd., 2014). Son zamanlarda bu mevcut verilerin boyutlar\u0131 makine \u00f6\u011frenmesi uygulamalar\u0131na ve daha \u00f6nceden m\u00fcmk\u00fcn olmayan veri madencili\u011fi tekniklerinin kullan\u0131lmas\u0131na imk\u00e2n vermektedir. Bununla birlikte, b\u00fcy\u00fck \u00f6l\u00e7e\u011fin \u00f6tesinde, \u00f6\u011frenen n\u00fcfusu da KA\u00c7D'lerde genel y\u00fcksek\u00f6\u011frenim derslerine g\u00f6re \u00e7ok daha \u00e7e\u015fitlidir. KA\u00c7D'ler, \u00e7o\u011fu deneysel sosyal bilimin dayand\u0131\u011f\u0131 pop\u00fclasyon olan Bat\u0131l\u0131, E\u011fitimli, Sanayile\u015fmi\u015f, Zengin ve Demokratik (BESZD) \u00fclkelerin d\u0131\u015f\u0131ndan da daha fazla \u00f6\u011frenen \u00e7ekmektedir (Henrich, Heine ve Norenzayan, 2010). Daha geni\u015f bir kitleye uygulanan farkl\u0131 veri kapsay\u0131c\u0131 bilimsel teorilerin ve e\u011fitsel uygulamalar\u0131n geli\u015ftirilmesi i\u00e7in son derece \u00f6nemlidir. Ayr\u0131ca, b\u00fcy\u00fck \u00e7e\u015fitlilikteki veriler, mevcut ara\u015ft\u0131rmalarda k\u00fc\u00e7\u00fck veya homojen \u00f6rneklerle ara\u015ft\u0131r\u0131lamayan demografik ve sosyok\u00fclt\u00fcrel gruplar (\u00f6r. bir m\u00fcdahalenin heterojen etkileri) aras\u0131ndaki bireysel farkl\u0131l\u0131klar\u0131 tan\u0131mlayan ara\u015ft\u0131rmay\u0131 m\u00fcmk\u00fcn k\u0131lar.<\/span><\/p>\n

E\u011fitim ara\u015ft\u0131rmalar\u0131n\u0131n h\u0131z\u0131n\u0131 ve etkisini art\u0131rmay\u0131 vaat eden KA\u00c7D'lerin ikinci \u00f6zelli\u011fi, \u00e7evrimi\u00e7i deneyleri h\u0131zl\u0131, ekonomik ve y\u00fcksek kalitede ger\u00e7ekle\u015ftirme kabiliyetidir (Reich, 2015). Teknoloji sekt\u00f6r\u00fcnde, bu, bireylerin rastgele olarak iki test ko\u015fulundan birine y\u00f6nlendirildi\u011fini iletmek i\u00e7in A \/ B testi olarak adland\u0131r\u0131l\u0131r. \u00c7evrimi\u00e7i deneme, teorileri ve uygulamalar\u0131 test etmek i\u00e7in h\u0131zl\u0131 yinelemeye olanak sa\u011flar, \u00e7\u00fcnk\u00fc \u00e7oklu deneyler paralel olarak \u00e7al\u0131\u015fabilir ve ara\u015ft\u0131rmac\u0131lar testleri ger\u00e7ek zamanl\u0131 ve d\u00fc\u015f\u00fck maliyetle ekleyebilir, silebilir ve de\u011fi\u015ftirebilir. \u00d6rne\u011fin bir ara\u015ft\u0131rmac\u0131 bir dersin ders videolar\u0131n\u0131n farkl\u0131 \u00f6rneklerin kar\u015f\u0131la\u015ft\u0131rabilir (\u00f6r. bir konuya ait giri\u015fin ve kavramlara ait sunumlar\u0131n nas\u0131l oldu\u011funa ili\u015fkin \u00e7e\u015fitlili\u011fi) ve daha sonraki de\u011ferlendirmeler \u00fczerine ger\u00e7ekle\u015ftirilen performans\u0131 g\u00f6zlemleyebilir. Yeterli veri topland\u0131ktan sonra, ara\u015ft\u0131rmac\u0131 en d\u00fc\u015f\u00fck puanlarla ili\u015fkili ders s\u00fcr\u00fcmlerinden vazge\u00e7ebilir teoriye ve mevcut s\u00fcr\u00fcmlerin sonu\u00e7lar\u0131na dayanarak yeni s\u00fcr\u00fcmler ekleyebilir ve yinelemeye devam edebilir. Bu s\u00fcre\u00e7te ara\u015ft\u0131rmac\u0131 belli bir \u00f6rnek \u00e7al\u0131\u015fman\u0131n belirli bir \u00f6\u011frenen grubu i\u00e7in \u00f6rne\u011fin daha az e\u011fitimli \u00f6\u011frenenlere en iyi sonucu verdi\u011fini g\u00f6rebilir. Bu durum yeni teorik bilgiler sa\u011flayabilir ve \u00f6\u011frenmeyi iyile\u015ftirmek i\u00e7in i\u00e7eri\u011fin uyarlamal\u0131 sunumunu gerektirir. Bireysel farkl\u0131l\u0131klar\u0131n ke\u015ffi ve i\u00e7eri\u011fin duyarl\u0131 bir \u015fekilde uyarlanmas\u0131, KA\u00c7D'lerde oldu\u011fu gibi b\u00fcy\u00fck heterojen \u00f6\u011frenen \u00f6rnekleriyle dijital \u00f6\u011frenme ortamlar\u0131nda da m\u00fcmk\u00fcnd\u00fcr.<\/span><\/p>\n

Bu b\u00f6l\u00fcm\u00fcn amac\u0131, \u00f6\u011frenme analiti\u011fi alan\u0131n\u0131n geli\u015fimi \u0131\u015f\u0131\u011f\u0131nda, KA\u00c7D'lerin bu iki \u00f6zelli\u011fini ortaya koymak ve bu \u00f6zelliklerin \u00f6\u011frenme, \u00f6\u011fretme teorisi ve prati\u011finin nas\u0131l geli\u015ftirilebilece\u011fini tart\u0131\u015fmakt\u0131r. Bu b\u00f6l\u00fcme, KA\u00c7D inisiyatiflerinin ortaya \u00e7\u0131k\u0131\u015f\u0131 ve geli\u015fimi hakk\u0131nda k\u0131sa bir tarihsel bak\u0131\u015fla ba\u015fl\u0131yoruz. Ara\u015ft\u0131rma i\u00e7in b\u00fcy\u00fck verinin avantajlar\u0131n\u0131 ve farkl\u0131 \u00f6\u011frenen \u00f6rneklerini g\u00f6r\u00fc\u015f\u00fcyoruz ve bunlar aras\u0131ndaki ili\u015fkilerden yararlanmak i\u00e7in ortaya \u00e7\u0131kan \u00e7al\u0131\u015fmalar\u0131 g\u00f6zden ge\u00e7iriyoruz. Daha sonra, deney ve h\u0131zl\u0131 yineleme yoluyla ortaya \u00e7\u0131kan f\u0131rsatlar\u0131 ele al\u0131yoruz ve bunlar\u0131n bug\u00fcne kadar KA\u00c7D platformlar\u0131nda nas\u0131l kullan\u0131ld\u0131\u011f\u0131n\u0131 tart\u0131\u015f\u0131yoruz. Mevcut k\u0131s\u0131tlamalar\u0131n ve b\u00fcy\u00fck \u00f6l\u00e7ekli dijital \u00f6\u011frenme ortamlar\u0131n\u0131n sundu\u011fu f\u0131rsatlardan \u00e7ok daha etkin bir \u015fekilde yararlanman\u0131n yollar\u0131n\u0131 konu\u015farak bu b\u00f6l\u00fcm\u00fc sonland\u0131r\u0131yoruz.<\/span><\/p>\n\n

KA\u00c7D'LER\u0130N D\u00dcN\u00dc VE BUG\u00dcN\u00dc<\/h2>\n

KA\u00c7D'lerin geli\u015fimi, uzaktan e\u011fitim (\u00f6r. uyum okullar\u0131, radyo e\u011fitimi), a\u00e7\u0131k eri\u015fim \u00fcniversiteleri ve a\u00e7\u0131k e\u011fitim kaynaklar\u0131 (Simonson, Smaldino, Albright ve Zvacek d\u00e2hil) gibi e\u011fitime eri\u015fimi artt\u0131rma \u00e7abalar\u0131 gelene\u011fi ba\u011flam\u0131nda ger\u00e7ekle\u015fti., 2011). Bununla birlikte 2008 y\u0131l\u0131nda; New York Times'\u0131n \"KA\u00c7D y\u0131l\u0131\" ilan etti\u011fi 2012 ve George Siemens ve Stephen Downes ilk KA\u00c7D\u2019yi kolayla\u015ft\u0131rd\u0131\u011f\u0131 (Siemens, 2013), y\u0131l\u0131na kadar KA\u00c7D'yi neyin te\u015fkil etti\u011fi kavram\u0131 temelden de\u011fi\u015fikli\u011fe u\u011fram\u0131\u015ft\u0131r. (Pappano, 2012). Bu de\u011fi\u015fim, Siemens'in (2013), kat\u0131 bir kurs yap\u0131s\u0131n\u0131 uygulamadan kolektif bilgi yaratmay\u0131 vurgulayan ba\u011flant\u0131c\u0131 pedagojik modeline dayanan orijinal bKA\u00c7D'lerini daha sonra \u00e7o\u011funlukla de\u011ferlendirmeleri ve kat\u0131 bir ders yap\u0131s\u0131n\u0131 i\u00e7eren ders-tabanl\u0131 \u00f6\u011fretim modeline dayanan gKA\u00c7D'lerden (yani 2012 ve sonras\u0131 KA\u00c7D'lerden) ayr\u0131lmas\u0131n\u0131 sa\u011flam\u0131\u015ft\u0131r. Stanford \u00dcniversitesi Profes\u00f6rleri Sebastian Thrun, Daphne Koller ve KA\u00c7D'leri ders s\u0131n\u0131flar\u0131n\u0131n daha geni\u015f bir izleyici kitlesine ula\u015fmalar\u0131 i\u00e7in dijital y\u00fckseltmeleri olarak yeniden tasarlad\u0131klar\u0131n\u0131 belirten Andrew Ng, bu ideolojik kaymaya yol a\u00e7t\u0131. Bu vizyon, ba\u015fta Coursera, Udacity, EdX ve FutureLearn olmak \u00fczere, \u00e7e\u015fitli kurumsal ve k\u00e2r amac\u0131 g\u00fctmeyen KA\u00c7D sa\u011flayan kurulu\u015flar\u0131n olu\u015fmas\u0131n\u0131 sa\u011flam\u0131\u015ft\u0131r. D\u00fcnya \u00e7ap\u0131ndaki y\u00fcksek\u00f6\u011fretim kurumlar\u0131, her biri on binlerce \u00f6\u011freneni \u00e7eken, artan say\u0131da derse katk\u0131da bulunmak i\u00e7in \u00e7aba harcad\u0131 (Waldrop, 2013).<\/span><\/p>\n

\u0130lk heyecan ve ivme, KA\u00c7D'lerin evrensel d\u00fc\u015f\u00fck maliyetli y\u00fcksek\u00f6\u011frenim sa\u011flama vaadi yerine getirmekten mahrum kald\u0131\u011f\u0131 ortaya \u00e7\u0131kt\u0131\u011f\u0131nda s\u00f6nmeye ba\u015flad\u0131. \u0130lk \u00e7arp\u0131c\u0131 kan\u0131t, bir kursa ba\u015flayan \u00f6\u011frenenlerin sadece k\u00fc\u00e7\u00fck bir y\u00fczdesinin dersi tamamlamaya gayret etmesiydi (Clow, 2013; Breslow vd., 2013) ve tamamlama herkesin hedefi olmasa da (K\u0131z\u0131lcec ve Schneider, 2015; K\u0131z\u0131lcec, Piech ve Schneider, 2013), bu \u00f6r\u00fcnt\u00fc kritik engellerin a\u015f\u0131lmadan kald\u0131\u011f\u0131n\u0131 g\u00f6stermektedir. \u0130kinci \u00fcz\u00fcc\u00fc fark\u0131ndal\u0131k, tarihsel olarak yoksul kitleler i\u00e7in eri\u015fimi geli\u015ftirme vaadiyle ilgiliydi. Bir\u00e7ok KA\u00c7D \u00f6\u011frenenleri zaten olduk\u00e7a e\u011fitimlidir (Emanuel, 2013). Ayr\u0131ca, Amerika Birle\u015fik Devletleri'ndeki \u00f6\u011frenenler daha zengin b\u00f6lgelerde ya\u015fama e\u011filimindedir ve daha fazla sosyoekonomik kaynaklara sahip bireylerin sertifika kazanma olas\u0131l\u0131klar\u0131 daha y\u00fcksektir (Hansen ve Reich, 2015). Di\u011fer kan\u0131tlar, KA\u00c7D'lerdeki sosyoekonomik ba\u015far\u0131 bo\u015fluklar\u0131n\u0131n d\u00fcnya genelinde e\u011fitim seviyeleri ve ulusal geli\u015fim seviyeleri (Kizilcec, Saltarelli, Reich ve Cohen, 2017) ve ayr\u0131ca kad\u0131nlar\u0131n erkeklere g\u00f6re daha d\u00fc\u015f\u00fck performans sergiledi\u011fini g\u00f6stermektedir (K\u0131z\u0131lcec ve Halawa, 2015). Bu \u00f6r\u00fcnt\u00fcler k\u0131smen yap\u0131sal, k\u00fclt\u00fcrel ve e\u011fitsel engellerden kaynaklanabilir (\u00f6r. \u0130nternet eri\u015fimi, \u00f6nceki bilgiler, dil becerileri, k\u00fclt\u00fcre \u00f6zg\u00fc \u00f6\u011fretim y\u00f6ntemleri). Ek olarak, \u00f6\u011frenenler sosyal gruplar\u0131 nedeniyle (yani sosyal kimlik tehdidi nedeniyle) daha az yetenekli olarak g\u00f6r\u00fclme korkusu ve se\u00e7kin Bat\u0131 kurumlar\u0131ndan KA\u00c7D'lere ait olmalar\u0131ndan emin olmad\u0131klar\u0131 gibi sosyal psikolojik engellerle de kar\u015f\u0131la\u015fabilirler (K\u0131z\u0131lcec vd., 2017). Steele, Spencer ve Joshua, 2002; Walton ve Cohen, 2007). En az\u0131ndan tamamlanma oranlar\u0131 a\u00e7\u0131s\u0131ndan, Kuzey Amerika KA\u00c7D'leri orant\u0131s\u0131z bir \u015fekilde daha ayr\u0131cal\u0131kl\u0131 \u00f6\u011frenenlere fayda sa\u011flad\u0131 ve e\u011fitim hakk\u0131n\u0131 geli\u015ftirmek i\u00e7in tasarlanan bir teknoloji i\u00e7in kritik bir zorluk te\u015fkil etti. Bu teknolojinin tamamlay\u0131c\u0131 g\u00fcc\u00fcn\u00fc vurgulamaktad\u0131r; yani yeni teknolojilerin \u00f6nemi ile ilgili etkin tedbirler al\u0131nmad\u0131k\u00e7a bu durum mevcut e\u015fitsizliklerin yans\u0131mas\u0131na neden olacakt\u0131r. Asl\u0131nda haks\u0131z pop\u00fclasyon i\u00e7in verilen deste\u011fe ili\u015fkin yetersizli\u011fin kan\u0131t\u0131 ortaya \u00e7\u0131kt\u0131k\u00e7a platform sa\u011flay\u0131c\u0131lar\u0131 ba\u015flang\u0131\u00e7ta dikkatlerini se\u00e7kin ABD ortaklar\u0131na yo\u011funla\u015ft\u0131rd\u0131lar ve ard\u0131ndan uluslararas\u0131 \u00fcniversite ortaklar\u0131, STK'lar ve yabanc\u0131 h\u00fck\u00fbmetler takip etti. \u00d6nceki KA\u00c7D platformlar\u0131, masa\u00fcst\u00fc tabanl\u0131 \u00f6\u011frenme deneyimleri sa\u011flamaya odaklan\u0131rken, platform geli\u015ftirme \u00e7abalar\u0131, mobil \u0130nternetin yayg\u0131n oldu\u011fu geli\u015fmekte olan \u00fclkelerde eri\u015fimi art\u0131rman\u0131n bir yolu olarak mobil cihazlara y\u00f6nelik deste\u011fin geni\u015fletilmesine de y\u00f6nelmi\u015ftir.<\/span><\/p>\n

KA\u00c7D \u00f6\u011frenme etkinlikleri i\u00e7in akreditasyon ve belgelendirme konusu, KA\u00c7D ak\u0131m\u0131 olgunla\u015ft\u0131k\u00e7a s\u00fcrekli de\u011ferlendirilmektedir. \u0130\u00e7erik ba\u015flang\u0131\u00e7ta \u00fccretsiz olarak sunulurken bir sertifikan\u0131n \u00f6nemi (ve bireyin kimli\u011finin ders etkinlikleriyle daha yak\u0131ndan ba\u011flant\u0131l\u0131 olan \u201conaylanm\u0131\u015f bir sertifika\u201d n\u0131n \u00f6nemi) i\u00e7eri\u011fe eri\u015fmek i\u00e7in \u00f6deme yapmak isteyen ya da sonunda bir sertifika almak isteyen \u00f6\u011frenenlerin ilgisini \u00e7ekmi\u015ftir. Sertifika yeterlilik belgesi de zamanla geli\u015fmi\u015ftir. Baz\u0131 kurumlar akademik kurumlardan ba\u011f\u0131ms\u0131z dereceler sunar (\u00f6r. Udacity'nin Nanodegrees uygulamas\u0131); KA\u00c7D'leri, liberal sanat kurslar\u0131n\u0131 \u00e7evrimi\u00e7i olarak tamamlamak i\u00e7in bir ge\u00e7it f\u0131rsat\u0131 olarak kullan\u0131r (\u00f6r. Arizona Devlet \u00dcniversitesi ve EdX Freshman Academy ortakl\u0131\u011f\u0131); bu lisans\u0131 geleneksel bir lisans derecesine ge\u00e7i\u015f yolu olarak kullanma se\u00e7ene\u011fi ile kompakt \u00e7evrimi\u00e7i lisans\u00fcst\u00fc programlar (\u00f6r. MIT Microdegrees) olu\u015fturur ve \u00e7evrimi\u00e7i olarak tam lisans\u00fcst\u00fc programlar sunarlar (\u00f6r. Illinois \u00dcniversitesi\u2019nin \u0130MBA ve Coursera platformundaki Veri Bilimi programlar\u0131). \u00d6zellikle veri bilimi gibi pop\u00fcler konularda, \u00e7e\u015fitli kurumlar\u0131n artan say\u0131da kurs ve k\u0131sa program\u0131 vard\u0131r. \u0130\u015fverenleri ve e\u011fitim kurumlar\u0131n\u0131 daha iyi ba\u011flayan daha verimli pazarlar geli\u015ftik\u00e7e, \u00f6\u011frenenleri derslerine \u00e7ekmek ve e\u011fitim alan\u0131nda \u00fcst\u00fcn i\u015fyeri performans\u0131 ve kariyer f\u0131rsatlar\u0131 g\u00f6stermelerini sa\u011flayacak kurslar sunmak amac\u0131yla kurumlar aras\u0131ndaki rekabetin artaca\u011f\u0131n\u0131 umuyoruz.<\/span><\/p>\n\n

E\u011e\u0130T\u0130MDE \u00c7E\u015e\u0130TL\u0130 T\u00dcRDEK\u0130 B\u00dcY\u00dcK VER\u0130LERLE ZENG\u0130NLE\u015eT\u0130RME TEOR\u0130S\u0130 VE UYGULAMASI<\/h2>\n

\u00d6\u011frenme ve \u00f6\u011fretme kuramlar\u0131 karma\u015f\u0131k bir sistemin b\u00f6l\u00fcmlerini tan\u0131mlar (Mitchell, 2009). Bu nedenle bir \u00f6\u011fretim y\u00f6ntemini veya bir \u00f6\u011frenme stratejisini inceleyen herhangi bir ara\u015ft\u0131rma; \u00f6rne\u011fin kat\u0131l\u0131mc\u0131lar\u0131n \u00f6nceki bilgileri veya konu alan\u0131 gibi kendi ba\u011flam\u0131 ile s\u0131n\u0131rl\u0131d\u0131r. Y\u00fczlerce potansiyel ba\u011flamsal nitelikten hangisinin belirli bir durumda \u00f6nemli oldu\u011funu tahmin etmek zordur ve denemek m\u00fcmk\u00fcn de\u011fildir. Bu nedenle bu karma\u015f\u0131kl\u0131\u011f\u0131 s\u0131n\u0131rlamak ve \u00f6nemli olan de\u011fi\u015fkenleri tan\u0131mlamak i\u00e7in bilimsel teoriye g\u00fcveniriz (Koedinger, Booth ve Klahr, 2013). Bununla birlikte e\u011fitim teorisi asla nihai ya da hepsini kapsay\u0131c\u0131 de\u011fildir. E\u011fitimdeki deneysel ara\u015ft\u0131rmalar ve genel olarak sosyal bilimler, d\u0131\u015f ge\u00e7erlik pahas\u0131na karma\u015f\u0131kl\u0131\u011f\u0131 azaltmak i\u00e7in belirli ba\u011flamlara odaklanma e\u011filimindedir. \u00d6zellikle, sosyal bilimlerde yap\u0131lan deneysel ara\u015ft\u0131rmalar, psikoloji laboratuvar\u0131 \u00e7al\u0131\u015fmalar\u0131na kat\u0131lan ABD'li \u00fcniversite \u00f6\u011frencileri gibi BESZD ba\u011flam\u0131ndaki insanlar\u0131n \u00e7al\u0131\u015fmalar\u0131na dayanmaktad\u0131r (Henrich, Heine ve Norenzayan, 2010). Bu mevcut sonu\u00e7lar\u0131n ve modellerin farkl\u0131 ba\u011flamlara ve kitlelere genellenebilirli\u011fi hakk\u0131nda sorular\u0131 g\u00fcndeme getirmektedir. Bu kayg\u0131lar ayn\u0131 zamanda \u00f6zellikle teknoloji ile g\u00fc\u00e7lendirilmi\u015f e\u011fitim ara\u015ft\u0131rmalar\u0131yla ilgili olarak g\u00fcndeme gelmi\u015ftir(Ocumpaugh, Baker, Gowda, Heffernan ve Heffernan, 2014; Blanchard, 2012). Bu zorlu\u011fun \u00fcstesinden gelmek i\u00e7in ara\u015ft\u0131rmac\u0131lar\u0131n geleneksel olarak elde edilenden daha b\u00fcy\u00fck ve daha \u00e7e\u015fitli olan \u00f6\u011frenen \u00f6rneklerine eri\u015fmeleri gerekir. Bu t\u00fcr \u00e7e\u015fitli \u00f6\u011frenen \u00f6rnekleri, KA\u00c7D'lerde yayg\u0131nd\u0131r.<\/span><\/p>\n

KA\u00c7D platformlar\u0131nda mevcut olan derslerin tedariki ve \u00e7e\u015fitlili\u011fi, ilk sunumlar\u0131ndan bu yana d\u00fczenli bir \u015fekilde artm\u0131\u015ft\u0131r (Shah, 2015). Bu kurslar, \u00fcniversitelerin, m\u00fczelerin ve ulusal enstit\u00fclerinde d\u00e2hil oldu\u011fu d\u00fcnyadaki kurumlar taraf\u0131ndan olu\u015fturulmu\u015ftur. 2016'n\u0131n ba\u015flar\u0131nda, Coursera d\u00fcnya \u00e7ap\u0131nda 18 milyon \u00f6\u011frenciye ula\u015ft\u0131\u011f\u0131n\u0131 a\u00e7\u0131klam\u0131\u015ft\u0131r1<\/a><\/sup>. \u00c7o\u011fu \u00f6\u011frenen Amerika Birle\u015fik Devletleri, \u00c7in, Hindistan ve Brezilya'da bulunmaktad\u0131r ve kay\u0131tlar\u0131n \u00f6zellikle art\u0131\u015fta oldu\u011fu yerler Meksika, Kolombiya, Brezilya ve Rusya\u2019 d\u0131r. Ortalamada her on \u00f6\u011frenenden d\u00f6rd\u00fcn\u00fc kad\u0131nlar olu\u015fturmaktad\u0131r ancak cinsiyet oran\u0131 Nijerya'da %22'den Filipinler'de %55'e kadar de\u011fi\u015fkenlik g\u00f6stermektedir. Ayn\u0131 \u015fekilde, \u00e7e\u015fitli kurs konular\u0131na ilgi cinsiyete ve b\u00f6lgeye g\u00f6re de\u011fi\u015fiklik g\u00f6stermektedir: \u0130\u015f alan\u0131 ile ilgili kurslar Fransa'da \u00e7ok daha yayg\u0131nken Polonyal\u0131 \u00f6\u011frenenler en az toplumsal cinsiyet dengesi (k\u00fcresel olarak) ile bilgisayar bilimleri konu alan\u0131n\u0131 tercih etmektedirler. Kurstaki \u00f6\u011frenenler iyi e\u011fitimli olma e\u011filimindedir: 2015 y\u0131l\u0131nda yap\u0131lan bir ara\u015ft\u0131rmaya g\u00f6re, yakla\u015f\u0131k %80'i lisans derecesini \u00e7oktan alm\u0131\u015ft\u0131 (Zhenghao vd., 2015). Bu \u00f6r\u00fcnt\u00fc, 2016 y\u0131l\u0131nda 3 milyon \u00f6\u011frenen taraf\u0131ndan kullan\u0131lan, \u0130ngiltere merkezli bir KA\u00c7D platformu olan FutureLearn'\u00fcnkine benziyor: %732<\/a><\/sup> lisans derecesi ve Coursera'n\u0131n aksine %62'si kad\u0131nd\u0131r. Di\u011fer iki b\u00fcy\u00fck KA\u00c7D sa\u011flay\u0131c\u0131s\u0131 olan EdX3<\/a><\/sup> ve Udacity4<\/a><\/sup>, 2016 y\u0131l\u0131na kadar s\u0131ras\u0131yla alt\u0131 milyon ve iki milyon \u00f6\u011frenciye hizmet sunmu\u015ftur. Di\u011fer pek \u00e7ok kurum, KA\u00c7D'leri ya geleneksel \u00f6\u011frenme y\u00f6netim sistemleri (\u00f6r. Canvas Network), kurumsal olarak yerle\u015ftirilmi\u015f a\u00e7\u0131k kaynak platformlar\u0131 (\u00f6r. Open EdX) ya da \u00f6zel ya da \u00f6zel olarak geli\u015ftirilen platformlar arac\u0131l\u0131\u011f\u0131yla sunmaktad\u0131r. Bununla birlikte, Class Central taraf\u0131ndan toplanan verilere g\u00f6re (Shah, 2015), 550 kurum, d\u00fcnya \u00e7ap\u0131nda 35 milyondan fazla insan\u0131n dikkat \u00e7ekici \u015fekilde heterojen bir pop\u00fclasyona ula\u015fan neredeyse t\u00fcm disiplinleri kapsayan 4200 kurs olu\u015fturmu\u015ftur.<\/span><\/p>\n

Bir KA\u00c7D i\u00e7erisinde toplanan veriler b\u00fcy\u00fck verilerin \u00fc\u00e7 \u00f6zelli\u011finden ikisi olan h\u0131z ve hacim bak\u0131m\u0131ndan y\u00fcksek olsa da (Laney, 2001), \u00e7e\u015fitlili\u011fi sa\u011flamak i\u00e7in \u00f6nlemler al\u0131nmad\u0131\u011f\u0131 s\u00fcrece, \u00e7e\u015fitlilik a\u00e7\u0131s\u0131ndan s\u0131n\u0131rl\u0131 kalabilir. Geleneksel e\u011fitim sistemleri hem ayr\u0131nt\u0131l\u0131 demografik bilgileri (\u00f6r. cinsiyet, etnik k\u00f6ken, sosyoekonomik stat\u00fc proxy'leri) hem de \u00f6nceki bilgi d\u00fczeyini (\u00f6r. \u00f6nceki okul kay\u0131tlar\u0131, lise notlar\u0131, standartla\u015ft\u0131r\u0131lm\u0131\u015f test puanlar\u0131) toplar. Bununla birlikte, bu de\u011fi\u015fkenler giri\u015f engelini azaltmak i\u00e7in KA\u00c7D'lerde otomatik olarak toplanmaz. B\u00f6ylece bir\u00e7ok KA\u00c7D sa\u011flayan kurum bu verileri iste\u011fe ba\u011fl\u0131 anketler yoluyla toplamaya ba\u015flam\u0131\u015ft\u0131r. Kursu tamamlama konusunda daha kararl\u0131 olma e\u011filiminde olan ve kendi tercihleriyle se\u00e7ilen bir grup \u00f6\u011frenen de bu anketleri tamamlama e\u011filimindedir. Reich (2014), kabaca kay\u0131tl\u0131 \u00f6\u011frenenlerin yakla\u015f\u0131k d\u00f6rtte birinin bir ders anketi doldurmas\u0131n\u0131 \u00f6nermektedir. Toplanan anket verilen cevaplar\u0131n toplam hacmi y\u00fcksek olsa da (\u00e7o\u011fu zaman onbinlerce), bu veriler genellikle daha fazla motive olmu\u015f \u00f6\u011frenenlerin \u00e7arp\u0131k bir \u00f6rne\u011fini temsil etti\u011fini hat\u0131rlamak \u00f6nemlidir. \u00d6\u011frenenlerin ge\u00e7mi\u015fine dair dikkat \u00e7ekmeden kapsaml\u0131 bilgi edinmek i\u00e7in mevcut k\u0131s\u0131tlamalar\u0131 a\u015fan veri toplama i\u00e7in geli\u015ftirilmi\u015f mekanizmalara ihtiya\u00e7 vard\u0131r. Bununla birlikte, \u015fu anda mevcut olan anket verileri KA\u00c7D \u00f6\u011frenenlerinin d\u00fcnyan\u0131n d\u00f6rt bir yan\u0131ndan nispeten farkl\u0131 yap\u0131da bir pop\u00fclasyon olu\u015fturdu\u011fu varsay\u0131m\u0131n\u0131 desteklemektedir.<\/span><\/p>\n

Farkl\u0131 yap\u0131daki bir \u00f6\u011frenen pop\u00fclasyonuna eri\u015fim, e\u011fitim teorisi ve prati\u011fini ilerletmek i\u00e7in iki b\u00fcy\u00fck avantaj sa\u011flar. \u0130lk olarak, farkl\u0131 yap\u0131daki bir \u00f6rnek \u00fczerinde bir \u00f6\u011fretim y\u00f6ntemi veya analitik model de\u011ferlendirilirken, sonu\u00e7lar daha az temsil edilen gruplar i\u00e7in olumsuz \u00e7\u0131kar\u0131mlar\u0131 olan sonu\u00e7 \u00e7\u0131karma olas\u0131l\u0131\u011f\u0131n\u0131 azaltan \u00e7e\u015fitli \u00f6\u011frenen k\u00fcmelerini daha iyi temsil etmektedir. Farkl\u0131 yap\u0131lardaki \u00f6rneklerden elde edilen kan\u0131tlara dayanan teoriyi geni\u015fletmek, ayr\u0131ca farkl\u0131 ge\u00e7mi\u015flerden gelen \u00f6\u011frenenleri destekleyen daha kapsay\u0131c\u0131 ortamlar\u0131n geli\u015ftirilmesini de te\u015fvik eder. Farkl\u0131 yap\u0131lardaki \u00f6\u011frenen \u00f6rneklerinin ikinci b\u00fcy\u00fck avantaj\u0131, bireysel farkl\u0131l\u0131klar\u0131 ortaya \u00e7\u0131karabilmeleridir. \u00c7e\u015fitlilik, kurs materyallerinin ve \u00f6\u011fretim y\u00f6ntemlerinin etkili bir \u015fekilde uyarlanmas\u0131n\u0131 sa\u011flayan i\u00e7g\u00f6r\u00fcn\u00fcn ve kimin i\u00e7in neyin i\u015fe yarad\u0131\u011f\u0131n\u0131n anla\u015f\u0131lmas\u0131 i\u00e7in temel bir bile\u015fendir. As\u0131l \u00f6\u011frenenlerden hi\u00e7birine benzemeyen \u201cortalama \u00f6\u011frenen\u201d e g\u00f6re uyarlaman\u0131n \u00f6tesinde, iyile\u015ftirme i\u00e7in \u00f6nemli bir alan vard\u0131r (Rose, 2016). Asl\u0131nda, \u00f6\u011frenme bilimi insanlar\u0131, \u00f6nceki bilgiler, bili\u015fsel kontrol, zihinsel yetenek ve ki\u015filik d\u00e2hil olmak \u00fczere \u00f6\u011fretim y\u00f6ntemlerinin etkinli\u011fini etkileyen say\u0131s\u0131z de\u011fi\u015fken tan\u0131mlam\u0131\u015ft\u0131r (Jonassen ve Grabowski, 1993). \u00d6rne\u011fin, \u00f6n bilgi iyi belgelenmi\u015f bireysel bir farkt\u0131r (Ambrose, Bridges, DiPietro, Lovett ve Norman, 2010), \u00f6yle ki yeni ba\u015flayanlar i\u00e7in g\u00f6receli olarak etkili olan \u00f6\u011fretim y\u00f6ntemlerinin, alan bilgisini artt\u0131rmay\u0131 \u00f6\u011frenen ki\u015filer i\u00e7in uzmanl\u0131\u011f\u0131n tersine \u00e7evrilmesi olarak bilinen bir olgu i\u00e7in etkisiz, hatta verimsiz olabilmesi gibi (Kalyuga, Ayres, Chandler ve Sweller, 2003). Birlikte ele al\u0131nd\u0131\u011f\u0131nda, \u00e7e\u015fitli b\u00fcy\u00fck veriler ortalamalara g\u00f6re uyarlaman\u0131n \u00f6tesine ge\u00e7en daha kapsaml\u0131 bir bilimi ilerletebilir.<\/span><\/p>\n

Ara\u015ft\u0131rmac\u0131lar say\u0131s\u0131z bireysel farkl\u0131l\u0131\u011f\u0131 incelemesine ra\u011fmen, e\u011fitim alan\u0131nda yinelenen \u00e7al\u0131\u015fmalar\u0131n\u0131n say\u0131s\u0131 azd\u0131r. Yinelenen \u00e7al\u0131\u015fmalar, 100 b\u00fcy\u00fck dergide yay\u0131nlanan makalelerin yaln\u0131zca %0,13'\u00fcn\u00fc olu\u015fturmaktad\u0131r (Makel ve Plucker, 2014) ve bu \u00e7al\u0131\u015fmalar\u0131n \u00e7o\u011fu BESZD \u00fclkelerindeki nispeten farkl\u0131 t\u00fcrdeki \u00f6\u011frenci pop\u00fclasyonuna dayanmaktad\u0131r. Farkl\u0131 \u00f6\u011frenme ba\u011flamlar\u0131ndaki bireysel \u00e7al\u0131\u015fmalar\u0131n \u00fcst analizini i\u00e7eren \u00e7al\u0131\u015fma \u00f6rnekleri \u00e7ok daha \u00e7e\u015fitli olsa bile \u00e7o\u011funun g\u00f6zlemlenmemi\u015f olmas\u0131 ve buna ba\u011fl\u0131 olarak dikkate al\u0131nmamas\u0131 \u00f6\u011fretim ko\u015fullar\u0131ndaki de\u011fi\u015fiklikleri anla\u015f\u0131lmaz k\u0131lacakt\u0131r (Ga\u0161evi\u0107, Dawson, Rogers ve Ga\u0161evi\u0107, 2016). KA\u00c7D'ler ve \u00e7evrimi\u00e7i \u00f6\u011frenme ortamlar\u0131 daha genel olarak bu acil sorunu ele almaya ba\u015flayabilir. Bu ortamlar, otantik \u00f6\u011frenme ba\u011flam\u0131nda farkl\u0131 \u00f6rneklerle geni\u015f \u00f6l\u00e7ekli \u00e7al\u0131\u015fmalar yapmak i\u00e7in \u00f6zellikle uygundur ve ayn\u0131 dersi tekrar kullanarak veya ayn\u0131 \u00e7al\u0131\u015fmay\u0131 ba\u015fka bir yere yerle\u015ftirerek bir KA\u00c7D'de tam bir \u00e7o\u011faltma \u00e7al\u0131\u015fmas\u0131n\u0131 y\u00fcr\u00fctmek b\u00fcy\u00fck \u00f6l\u00e7\u00fcde daha h\u0131zl\u0131 ve daha ucuzdur. Asl\u0131nda, KA\u00c7D'ler farkl\u0131 \u00f6\u011fretim gruplar\u0131 i\u00e7in hangi \u00f6\u011fretim yakla\u015f\u0131mlar\u0131n\u0131n daha etkili oldu\u011fu konusunda disiplin ara\u015ft\u0131rmalar\u0131 yapmak i\u00e7in \u00f6zellikle uygundur. \u00d6rne\u011fin, fizik e\u011fitiminde termodinami\u011fin ikinci yasas\u0131n\u0131 \u00f6\u011fretmeye y\u00f6nelik farkl\u0131 yakla\u015f\u0131mlar \u00f6nerilmi\u015f ve test edilmi\u015ftir (\u00f6r. Cochran ve Heron, 2006) ancak d\u00fcnyan\u0131n farkl\u0131 b\u00f6lgelerinden gelen \u00f6\u011frenenler i\u00e7in hangi yakla\u015f\u0131m\u0131n daha etkili oldu\u011fu belirsizdir.<\/span><\/p>\n

KA\u00c7D'lerde \u00e7e\u015fitlili\u011fin kritik bir boyutu co\u011frafya ve dolay\u0131s\u0131yla k\u00fclt\u00fcrd\u00fcr. KA\u00c7D'ler, Bat\u0131 ve Do\u011fu \u00fclkelerinden gelen \u00f6\u011frenenleri, \u00f6\u011frenmenin farkl\u0131 k\u00fclt\u00fcrel temelleriyle birle\u015ftirir (Li, 2012). Do\u011fu \u00fclkelerinde (\u00f6r. \u00c7in, Japonya), \u00f6\u011frenme Konf\u00fc\u00e7y\u00fcs etkilerine dayanan erdemli, ya\u015fam boyu s\u00fcren bir kendi kendini m\u00fckemmelle\u015ftirme s\u00fcreci olarak g\u00f6r\u00fclme e\u011filimindedir, oysa Bat\u0131 \u00fclkelerinde (\u00f6r. ABD, Kanada), \u00f6\u011frenme Sokrat\u00e7\u0131 ve Baconcu etkilerine dayanarak \u00e7evremizdeki d\u00fcnyay\u0131 anlama hedefine hizmet eden bir soru\u015fturma \u015fekli olarak g\u00f6r\u00fcl\u00fcr. Ger\u00e7ekten de Bat\u0131 \u00fcniversitelerine kat\u0131lan Konf\u00fc\u00e7y\u00fcs\u00e7\u00fc Asya \u00f6\u011frencileri akademik bir uyum s\u00fcrecinden (Rienties ve Tempelaar, 2013) ge\u00e7iyor ve bu k\u00fclt\u00fcr \u015foku \u00f6\u011frenmeyi engelliyor ve yabanc\u0131la\u015fma duygular\u0131n\u0131 artt\u0131r\u0131yor (Zhou, Jindal \u2013 Snape, Topping ve Todman, 2008). Bu durum k\u00fclt\u00fcrel farkl\u0131l\u0131klar\u0131n ve bireysel farkl\u0131l\u0131klar\u0131n \u00f6\u011fretim yakla\u015f\u0131mlar\u0131n\u0131n etkinli\u011fi \u00fczerindeki potansiyel etkisini daha geni\u015f bir bi\u00e7imde vurgulamaktad\u0131r. KA\u00c7D'ler hem bireysel hem de grup d\u00fczeyinde, \u00f6\u011frenen \u00f6zelliklerinin anla\u015f\u0131lmam\u0131\u015f boyutlar\u0131n\u0131 de\u011ferlendirerek bireysel farkl\u0131l\u0131klar\u0131 ara\u015ft\u0131rmak ve mevcut teorileri geli\u015ftirmek i\u00e7in yeterince \u00e7e\u015fitli \u00f6\u011frenen \u00f6rnekleri sunar. Bireysel farkl\u0131l\u0131klara dair yeni g\u00f6r\u00fc\u015fler hem ki\u015fisel hem de \u00e7evrimi\u00e7i ortamlarda akademik d\u00fczenlemeyi ve uyarlanm\u0131\u015f \u00f6\u011frenme deneyimlerini destekleyen mevcut uygulamalar\u0131 bilgilendirebilir.<\/span><\/p>\n\n

KA\u00c7D'lerde Farkl\u0131 B\u00fcy\u00fck Verilerden Yararlanan G\u00fcncel Ara\u015ft\u0131rma<\/h3>\n

Ara\u015ft\u0131rmac\u0131lar, KA\u00c7D\"lerden gelen heterojen \u00f6\u011frenen verilerinin b\u00fcy\u00fck potansiyelinden yararlanmaya yeni ba\u015fl\u0131yor. Son \u00e7al\u0131\u015fmalar, kurs gezintisi (Guo ve Reinecke, 2014), \u00f6\u011frenen motivasyonunun (K\u0131z\u0131lcec ve Schneider, 2015), s\u00fcreklili\u011fi ve ba\u015far\u0131s\u0131 (DeBoer, Stump, Seaton ve Breslow, 2013; K\u0131z\u0131lcec ve Halawa, 2015; K\u0131z\u0131lcec vd., 2013) ile Amerika Birle\u015fik Devletleri'nde (Hansen ve Reich, 2015) ve d\u00fcnya \u00e7ap\u0131ndaki \u00f6\u011frenenlerin kurs tamamlamalar\u0131nda sosyoekonomik farkl\u0131l\u0131klar\u0131 a\u00e7\u0131s\u0131ndan demografik ve co\u011frafi farkl\u0131l\u0131klar\u0131 ara\u015ft\u0131rm\u0131\u015ft\u0131r (K\u0131z\u0131lcec vd., 2017). \u00d6zellikle her ne kadar \u00f6\u011frenen demografik \u00f6zellikleri ders kazan\u0131mlar\u0131ndaki anlaml\u0131 ay\u0131r\u0131mlar\u0131 hesaba katsa da kestirimci modelleme ba\u011flam\u0131nda davran\u0131\u015fsal kay\u0131t g\u00fcnl\u00fc\u011f\u00fc verileri \u00fczerinde s\u0131n\u0131rl\u0131 iyile\u015ftirmeler sa\u011flar (Brooks, Thompson ve Teasley, 2015a; Brooks, Thompson ve Teasley, 2015b). KA\u00c7D'lerde \u00e7e\u015fitlili\u011fi art\u0131rmak i\u00e7in bir dizi olas\u0131 yakla\u015f\u0131mlar\u0131 vurgulayan alanyaz\u0131ndan iki \u00f6rne\u011fi k\u0131saca a\u00e7\u0131kl\u0131yoruz.<\/span><\/p>\n

\u0130lk \u00f6nce, KA\u00c7D \u00f6\u011frenenlerin kat\u0131l\u0131m\u0131n\u0131 ve \u00f6\u011frenmesini geli\u015ftirmek i\u00e7in \u00e7e\u015fitlili\u011fi kullanmak \u00fczere yola \u00e7\u0131kan Kulkarni, Cambre, Kotturi, Bernstein ve Klemmer (2015) taraf\u0131ndan yap\u0131lan \u00e7al\u0131\u015fmalar\u0131 ele al\u0131yoruz. KA\u00c7D'lerdeki nispeten d\u00fc\u015f\u00fck sosyal etkile\u015fim seviyesini inovasyon ve ara\u015ft\u0131rma i\u00e7in bir f\u0131rsat olarak tan\u0131mlad\u0131lar. Bu yetersizli\u011fi gidermek i\u00e7in, \u00e7evrimi\u00e7i \u00f6\u011frenenleri gruptaki video sohbetleri arac\u0131l\u0131\u011f\u0131yla kurstaki di\u011fer ki\u015filerle ileti\u015fimde tutan bir akran tart\u0131\u015fma sistemi tasarlad\u0131lar. Grup kompozisyonunun performans de\u011ferlendirmesi \u00fczerindeki etkisini incelemek ve akran tart\u0131\u015fma sisteminin tasar\u0131m\u0131n\u0131 iyile\u015ftirmek i\u00e7in bir dizi deney yap\u0131lm\u0131\u015ft\u0131r. \u00dc\u00e7 deneyde \u00f6\u011frenenler, ka\u00e7 \u00fclkenin temsil edildi\u011fine g\u00f6re d\u00fc\u015f\u00fck co\u011frafi \u00e7e\u015fitlili\u011fi y\u00fcksek olan tart\u0131\u015fma gruplar\u0131na atand\u0131lar. Her deneyde b\u00f6l\u00fcm\u00fcn kavramsal olarak tamam\u0131n\u0131 kapsayan a\u00e7\u0131k u\u00e7lu bir soruyu, haftal\u0131k \u201cev \u00f6devi\u201d de\u011ferlendirmelerini ve final s\u0131nav\u0131n\u0131n puan\u0131n\u0131 ilgilendiren performans \u00f6l\u00e7\u00fcmlerinin farkl\u0131 sonu\u00e7lar\u0131 de\u011ferlendirildi. Yazarlar\u0131n hipotezini do\u011frulayan \u00e7ok \u00e7e\u015fitlilikteki akran tart\u0131\u015fmalar\u0131 performansta k\u0131sa vadeli iyile\u015fmeleri getirmi\u015ftir. Cinsiyet \u00e7e\u015fitlili\u011fi, \u00f6nceki \u00e7al\u0131\u015fmalar\u0131n tahmininin aksine, genel veya farkl\u0131 olarak \u00e7e\u015fitlilik durumuna g\u00f6re hi\u00e7bir etki g\u00f6stermemi\u015ftir (Woolley, Chabris, Pentland, Hashmi ve Malone, 2010). Ara\u015ft\u0131rmalar\u0131 e\u011fitsel bir varl\u0131k olarak \u00e7e\u015fitlili\u011fi art\u0131rman\u0131n umut verici bir yol oldu\u011funu g\u00f6stermektedir. Ayr\u0131ca bu yakla\u015f\u0131m\u0131n etkilili\u011fini etkileyebilecek bireysel farkl\u0131l\u0131klar\u0131 cinsiyet etkilerini test ederek ve \u00e7e\u015fitlili\u011fin farkl\u0131 yollarla \u00f6l\u00e7\u00fclebilir hale getirerek de\u011ferlendirmesini incelemeye ba\u015flamam\u0131\u015ft\u0131r.<\/span><\/p>\n

Alanyaz\u0131ndan ikinci bir \u00f6rnek, \u00f6\u011fretim eleman\u0131n\u0131n ders videolar\u0131nda en uygun sunumu ile ilgilidir. \u0130nsan y\u00fcz\u00fc g\u00f6rmek dikkat etmeyi kolayla\u015ft\u0131rabilir ancak dikkat da\u011f\u0131t\u0131c\u0131 da olabilir. G\u00f6r\u00fcnt\u00fc ilkesi, \u00f6\u011fretenin bir videoda g\u00f6sterilmesinin \u00f6\u011frenme \u00e7\u0131kt\u0131lar\u0131n\u0131 etkilemedi\u011fini, \u00e7\u00fcnk\u00fc sosyal ipu\u00e7lar\u0131n\u0131n motivasyonel faydalar\u0131n\u0131n ilave yabanc\u0131 bili\u015fsel i\u015flemle dengelendi\u011fini g\u00f6stermektedir (Mayer, 2001). Bu bulgu, motivasyonun sebat ve ba\u015far\u0131n\u0131n kritik bir \u00f6nc\u00fcl\u00fc oldu\u011fu KA\u00c7D'lerin ba\u011flam\u0131na nas\u0131l \u00e7evrilebilir? K\u0131z\u0131lcec, Bailenson ve Gomez (2015), bir kursta KA\u00c7D \u00f6\u011frenenlerinin %35'inin y\u00fczleri \u00e7ok fazla rahats\u0131z edici bulduklar\u0131 i\u00e7in bir se\u00e7im yap\u0131ld\u0131\u011f\u0131nda, y\u00fcz\u00fc olmayan videolar\u0131 izlemeyi tercih etti\u011fini bulmu\u015ftur. Daha sonra KA\u00c7D'de yap\u0131lan rastgele sonu\u00e7lu bir deneyde, \u00f6\u011fretenin s\u00fcrekli g\u00f6sterilmesi nedeniyle dikkat da\u011f\u0131tan bir video ile \u00f6\u011fretenin g\u00f6sterilmedi\u011fi bir video t\u00fcr\u00fcn\u00fcn kar\u015f\u0131la\u015ft\u0131rmas\u0131 yap\u0131lm\u0131\u015ft\u0131r. Stratejik sunum alg\u0131lanan bili\u015fsel y\u00fck\u00fc ve sosyal varl\u0131\u011f\u0131 ortaya \u00e7\u0131karm\u0131\u015f olsa da s\u00fcreklilik ya da ders notlar\u0131 \u00fczerinde tam bir etkisi olmam\u0131\u015ft\u0131r. Bununla birlikte \u00f6\u011frenme tercihi (yani bireylerin resim ve \u015femalardan m\u0131 yoksa yaz\u0131l\u0131 ve s\u00f6zl\u00fc bilgilerden mi \u00f6\u011frenmeyi tercih edip etmedikleri) dikkate al\u0131nd\u0131\u011f\u0131nda s\u00fcreklilik konusunda \u00f6nemli bir bireysel fark vard\u0131: S\u00f6zel \u00f6\u011frenmeyi tercih eden \u00f6\u011frenenlerin s\u00fcrekli sunumlardan ziyade stratejik olanla kursu b\u0131rakma olas\u0131l\u0131\u011f\u0131 %46 daha fazlayd\u0131. Bu hem uygulamadaki bireysel farkl\u0131l\u0131klar\u0131n nedenini a\u00e7\u0131klamakta hem de mevcut teorilerin geli\u015ftirilmesinin \u00f6nemini g\u00f6stermektedir. E\u011fer sosyal ipu\u00e7lar\u0131 farkl\u0131 ki\u015filer i\u00e7in daha fazla dikkat da\u011f\u0131t\u0131c\u0131 ya da motive edici ise bu g\u00f6r\u00fc\u015f\u00fcn hedeflenen \u00f6\u011fretim tasar\u0131m\u0131 i\u00e7in \u00f6\u011frenen modellerine d\u00e2hil edilmesi yerinde olacakt\u0131r.<\/span><\/p>\n\n

\u00c7EVR\u0130M\u0130\u00c7\u0130 ALAN DENEYLER\u0130N\u0130 KULLANARAK KURAMIN SINANMASI VE E\u011e\u0130T\u0130M UYGULAMALARININ DE\u011eERLEND\u0130R\u0130LMES\u0130<\/h2>\n

Y\u00fcksek\u00f6\u011frenimde kullan\u0131lan geleneksel \u00f6\u011frenme y\u00f6netimi sistemlerine k\u0131yasla KA\u00c7D kurs tasar\u0131mc\u0131lar\u0131na ve neredeyse her yeni \u00f6zellik i\u00e7in s\u0131n\u0131rl\u0131 say\u0131da se\u00e7enek sunar. Bununla birlikte, KA\u00c7D'lerin arkas\u0131ndaki b\u00fcy\u00fck ve \u00e7e\u015fitli \u00f6\u011frenme toplulu\u011fu, deneysel ara\u015ft\u0131rmalar yoluyla \u00f6\u011frenme ve \u00f6\u011fretme hakk\u0131nda daha fazla bilgi edinmek i\u00e7in ola\u011fan\u00fcst\u00fc bir f\u0131rsat sunmaktad\u0131r. KA\u00c7D'lerle yap\u0131lan ilk ara\u015ft\u0131rmalar\u0131n \u00e7o\u011fu, varsay\u0131lan olarak toplanan kurs kay\u0131t g\u00fcnl\u00fc\u011f\u00fc verilerinin (\u00f6r. t\u0131klama ak\u0131\u015flar\u0131) analizine ve nispeten d\u00fc\u015f\u00fck cevap oranlar\u0131na sahip kurs anketlerinden elde edilen \u00f6z-raporlama \u00f6l\u00e7\u00fcmlerine odakland\u0131. KA\u00c7D'lerde \u00f6\u011fretene y\u00f6nelik deneme \u00f6zelliklerinin yak\u0131n zaman \u00f6nce bulunmas\u0131 ara\u015ft\u0131rmac\u0131lar\u0131n basit rastgele sonu\u00e7lu deneyler yapmalar\u0131n\u0131 sa\u011flam\u0131\u015ft\u0131r. Burada, birincisi etkile\u015fim ve \u00f6\u011frenmeyi te\u015fvik etmek i\u00e7in k\u00fc\u00e7\u00fck te\u015fvikler ile ilgilenen, ikincisi ders i\u00e7eri\u011finin ve yap\u0131s\u0131ndaki de\u011fi\u015fikliklerle ilgilenen, \u00fc\u00e7\u00fcnc\u00fcs\u00fc KA\u00c7D'lerin genel olaylar\u0131 incelemek ve ileriye y\u00f6nelik metodolojik d\u00fc\u015f\u00fcnceleri tart\u0131\u015fmak i\u00e7in bir laboratuvar g\u00f6revi g\u00f6rd\u00fc\u011f\u00fc- \u00fc\u00e7 deneysel ara\u015ft\u0131rma ak\u0131\u015f\u0131n\u0131 g\u00f6zden ge\u00e7iriyoruz.<\/span><\/p>\n\n

KA\u00c7D'lerde Yay\u0131nlanm\u0131\u015f Deneysel Ara\u015ft\u0131rmalardan \u00dc\u00e7 Ak\u0131\u015f<\/h3>\n

Bir deneysel ara\u015ft\u0131rma ak\u0131\u015f\u0131 ders sonu\u00e7lar\u0131n\u0131 geli\u015ftirmek i\u00e7in k\u00fc\u00e7\u00fck \u00f6zendirmelere veya hareketliliklere odaklanm\u0131\u015ft\u0131r. Bu t\u00fcr m\u00fcdahaleler \u00f6\u011frenenlerin farkl\u0131 mesajlar\u0131 \u00f6rne\u011fin elektronik posta arac\u0131l\u0131\u011f\u0131yla almalar\u0131 i\u00e7in rastgele atanmalar\u0131 ile y\u00fcr\u00fct\u00fclebilir. Tart\u0131\u015fma forumlar\u0131na kat\u0131l\u0131m\u0131 artt\u0131rmak bir dizi \u00e7al\u0131\u015fma A \/ B testleri kullanm\u0131\u015ft\u0131r. Lamb, Smilack, Ho ve Reich (2015) \u00fc\u00e7 i\u015fleyi\u015fi s\u0131nam\u0131\u015flard\u0131r (kendi kendine test kat\u0131l\u0131m kontrol\u00fc, \u00f6nceki tart\u0131\u015fmalar\u0131n \u00f6zetleriyle tart\u0131\u015fmalar\u0131n ba\u015flat\u0131lmas\u0131 ve yakla\u015fmakta olan tart\u0131\u015fma konular\u0131 hakk\u0131nda tart\u0131\u015fma \u00f6nizleme e-postalar\u0131) ve kat\u0131l\u0131m kontrol\u00fcn\u00fcn varsay\u0131lan kontrol ko\u015fulu \u00fczerinde forum etkinli\u011fini artt\u0131rd\u0131\u011f\u0131n\u0131 ke\u015ffetmi\u015flerdir. K\u0131z\u0131lcec, Schneider, Cohen ve McFarland (2014), iki deneyde forum kat\u0131l\u0131m\u0131 i\u00e7in e-posta kullan\u0131m\u0131na ait \u00f6zendiricili\u011fin \u00e7er\u00e7eveleme etkilerini s\u0131nam\u0131\u015flar ve i\u015fbirlikli bir \u00e7er\u00e7evenin (yani, \"birlikte \u00f6\u011fren\", \"birbirine yard\u0131m et\") bireycili\u011fe ya da n\u00f6tr \u00e7er\u00e7eveye g\u00f6re kat\u0131l\u0131m\u0131 azaltt\u0131\u011f\u0131n\u0131 tespit etmi\u015flerdir. Martinez (2014) \u00e7er\u00e7eveleme etkilerini sosyal bir kar\u015f\u0131la\u015ft\u0131rma paradigmas\u0131 kullanarak s\u0131nam\u0131\u015ft\u0131r (Festinger, 1954). \u00d6\u011frenenler, yukar\u0131 do\u011fru bir sosyal kar\u015f\u0131la\u015ft\u0131rma (ka\u00e7 \u00f6\u011frenenin seni geride b\u0131rakt\u0131\u011f\u0131n\u0131 anlat\u0131r), a\u015fa\u011f\u0131 do\u011fru sosyal bir kar\u015f\u0131la\u015ft\u0131rma (ka\u00e7 ki\u015finin daha k\u00f6t\u00fc performans g\u00f6sterdi\u011fini a\u00e7\u0131klar) veya herhangi bir sosyal kar\u015f\u0131la\u015ft\u0131rmay\u0131 i\u00e7ermeyen bir kontrol mesaj\u0131 i\u00e7eren bir e-posta alm\u0131\u015flard\u0131. A\u015fa\u011f\u0131 y\u00f6nl\u00fc kar\u015f\u0131la\u015ft\u0131rma y\u00fcksek performansl\u0131 \u00f6\u011frenenleri motive ederken, zorlanan \u00f6\u011frenenler yukar\u0131 y\u00f6nl\u00fc kar\u015f\u0131la\u015ft\u0131rmadan yararlanm\u0131\u015ft\u0131r. Son olarak Renz, Hoffmann, Staubitz ve Meinel (2016), pop\u00fcler forum tart\u0131\u015fmalar\u0131n\u0131 ve cevaplanmayan sorular\u0131 sergileyen e-postalar\u0131n forum etkinli\u011fini artt\u0131rd\u0131\u011f\u0131n\u0131 ve g\u00f6r\u00fcnmeyen ders videolar\u0131 hakk\u0131ndaki hat\u0131rlat\u0131c\u0131 e-postalar\u0131n, di\u011fer hat\u0131rlat\u0131c\u0131larla kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda ders etkinli\u011fini (yani ders g\u00f6r\u00fcn\u00fcmlerini) artt\u0131rd\u0131\u011f\u0131n\u0131 ke\u015ffetmi\u015flerdir. Bununla birlikte e-posta m\u00fcdahalelerinin bir dezavantaj\u0131, ara\u015ft\u0131rmac\u0131lar\u0131n genel olarak e-postay\u0131 kimin a\u00e7t\u0131\u011f\u0131n\u0131 ve kimin i\u015fleme maruz kald\u0131\u011f\u0131n\u0131 g\u00f6zlemleyememesidir; bu i\u015flem etkisini tahmin etmede analitik bir zorlu\u011fa yol a\u00e7ar (bk. Lamb vd., 2015). Anket deneyleri, bir anketin i\u00e7ine yerle\u015ftirilen deneme ile bir alternatif sunar. Bir \u00e7al\u0131\u015fmada, \u00f6z y\u00f6netimli \u00f6\u011frenme hakk\u0131nda ipu\u00e7lar\u0131 ya da kurs konular\u0131 hakk\u0131nda bir kontrol mesaj\u0131 almak i\u00e7in rastgele atanan anket kat\u0131l\u0131mc\u0131lar\u0131 vard\u0131 ancak kurs sonu\u00e7lar\u0131nda hi\u00e7bir iyile\u015fme bulamad\u0131lar (K\u0131z\u0131lcec, Perez \u2013 Sanagustin ve Maldonado, 2016). \u0130ste\u011fe ba\u011fl\u0131 anketlerdeki deneylerin olas\u0131 bir dezavantaj\u0131 ankete kat\u0131lmay\u0131 se\u00e7enlerden i\u015fleyi\u015fe farkl\u0131 cevap verebilecek daha kararl\u0131 \u00f6\u011frenenlerin \u00f6rneklem olu\u015fturma e\u011filimidir. Genel olarak, k\u00fc\u00e7\u00fck d\u00fcrtmeler insan davran\u0131\u015flar\u0131 \u00fczerinde \u015fa\u015f\u0131rt\u0131c\u0131 derecede b\u00fcy\u00fck etkilere sahip olsa da (Thaler ve Sunstein, 2009), KA\u00c7D'lerde yap\u0131lan \u00e7o\u011fu deney k\u00fc\u00e7\u00fck veya anlaml\u0131 olmayan sonu\u00e7lar vermi\u015ftir.<\/span><\/p>\n

Ba\u015fka bir deneysel ara\u015ft\u0131rma ak\u0131\u015f\u0131, ders i\u00e7eri\u011fi ve ders yap\u0131s\u0131ndaki teoriye dayal\u0131 de\u011fi\u015fiklikleri incelemi\u015ftir. Renz, Hoffmann, Staubitz ve Meinel (2016), \u00f6\u011frenenlere kurs yap\u0131s\u0131n\u0131 ve navigasyonunu (gezintisini) a\u00e7\u0131klayan etkile\u015fimli bir tur olan \u201cekleme5<\/a><\/sup>\u201d oturumunu sunman\u0131n etkisini de\u011ferlendirmi\u015flerdir ancak kurs kat\u0131l\u0131m\u0131nda bir d\u00fczelmeye ula\u015famam\u0131\u015flard\u0131r. Yar\u0131 deneysel bir yakla\u015f\u0131m\u0131 takiben, Mullaney ve Reich (2015), ayn\u0131 dersin ard\u0131\u015f\u0131k iki \u00f6rne\u011fini, materyalin hepsinin ayn\u0131 anda sunumu kar\u015f\u0131s\u0131nda \u015fa\u015f\u0131rt\u0131c\u0131 bir \u015fekilde farkl\u0131 i\u00e7erik yay\u0131n modelleriyle kar\u015f\u0131la\u015ft\u0131rm\u0131\u015flard\u0131r. Ayr\u0131ca kal\u0131c\u0131l\u0131k ve tamamlama oranlar\u0131 aras\u0131nda da anlaml\u0131 bir fark bulunmam\u0131\u015ft\u0131r. Davis, Chen, van der Zee, Hauff ve Houben (2016), belirlenmi\u015f iki \u00f6\u011frenme stratejisini kolayla\u015ft\u0131rmak i\u00e7in (geri alma prati\u011fi ve \u00e7al\u0131\u015fma planlamas\u0131), \u00f6\u011frencilerden i\u00e7eri\u011fi \u00f6zetlemelerini ve \u00f6nceden planlamalar\u0131n\u0131 isteyen haftal\u0131k yazma konular\u0131n\u0131 denemi\u015flerdir. Yine bir kez daha kurs s\u00fcreklili\u011fi ve tamamlanmas\u0131 konular\u0131nda bir geli\u015fme tespit edilmemi\u015ftir. Kizilcec ve meslekta\u015flar\u0131, (2015) \u00e7oklu ortam \u00f6\u011frenme kuram\u0131n\u0131 temel alarak, \u00f6\u011fretim eleman\u0131n\u0131n video derslerinde y\u00fczlerinin sunumunun y\u0131pratma ve ba\u015far\u0131 oranlar\u0131n\u0131 nas\u0131l etkiledi\u011fini ve daha \u00f6nce tarif edildi\u011fi gibi y\u0131pratma \u00fczerinde heterojen etkiler buldu\u011funu test etmi\u015ftir. Tart\u0131\u015fma forumlar\u0131 kapsam\u0131nda, Tomkin ve Charlevoix (2014), \u00f6\u011freten ileti\u015fiminin \u00e7e\u015fitli kurs \u00e7\u0131kt\u0131lar\u0131 \u00fczerindeki etkisini test etmi\u015ftir. \u00d6\u011fretenlerin forum sorular\u0131na cevap verdi\u011fi ve haftal\u0131k \u00f6zetler g\u00f6nderdi\u011fi y\u00fcksek t\u0131klanma durumlar\u0131, \u00f6\u011freten kat\u0131l\u0131m\u0131 olmayan d\u00fc\u015f\u00fck t\u0131klamal\u0131 durumla kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda memnuniyet, kal\u0131c\u0131l\u0131k veya tamamlanma oranlar\u0131n\u0131 iyile\u015ftirmemi\u015ftir. Coetzee, Fox, Hearst ve Hartmann (2014) tart\u0131\u015fma forumunda sayg\u0131nl\u0131k sisteminin benimsenmesinin etkisini de\u011ferlendirmi\u015fler ve cevap s\u00fcrelerini ve g\u00f6nderim ba\u015f\u0131na d\u00fc\u015fen cevap say\u0131lar\u0131n\u0131n art\u0131\u011f\u0131n\u0131 ancak notlar\u0131 ve devaml\u0131l\u0131\u011f\u0131 etkilemedi\u011fini tespit etmi\u015flerdir. Bir ba\u015fka \u00e7al\u0131\u015fma, farkl\u0131 sistemleri de\u011ferlendirmi\u015f ve rozetin ilerleyi\u015fini ve yakla\u015fmakta olan rozetleri vurgulayan bir forum rozet sisteminin forum etkinli\u011fini artt\u0131rd\u0131\u011f\u0131n\u0131 g\u00f6rm\u00fc\u015ft\u00fcr. (Anderson, Huttenlocher, Kleinberg ve Leskovec, 2014). Bu ara\u015ft\u0131rma ak\u0131\u015f\u0131ndaki \u00e7al\u0131\u015fmalar\u0131n \u00e7o\u011fu; daha g\u00fc\u00e7l\u00fc manip\u00fclasyonlar kullan\u0131lm\u0131\u015f olmas\u0131na ra\u011fmen, \u00f6\u011frenme \u00e7\u0131kt\u0131lar\u0131nda \u00f6nemli bir geli\u015fmeye ula\u015f\u0131lamam\u0131\u015ft\u0131r.<\/span><\/p>\n

\u00dc\u00e7\u00fcnc\u00fc deneysel ara\u015ft\u0131rma ak\u0131\u015f\u0131, genel teorileri ger\u00e7ek d\u00fcnya ba\u011flam\u0131nda test etmek i\u00e7in KA\u00c7D'leri bir laboratuvar ortam\u0131 olarak kullan\u0131r. \u00d6rne\u011fin, \u00e7evrimi\u00e7i s\u0131n\u0131flardaki bilin\u00e7 d\u0131\u015f\u0131 \u00f6n yarg\u0131lar\u0131 test etmek i\u00e7in, Baker, Dee, Evans ve John (2015), 126 KA\u00c7D (tart\u0131\u015fma ba\u015f\u0131na sekiz, toplam 1.008 mesaj) ve rastgele atanan \u00f6\u011frenen adlar\u0131n\u0131 i\u00e7eren tart\u0131\u015fma forumlar\u0131na farkl\u0131 \u0131rk ve cinsiyetleri temsil eden rastgele \u00f6\u011frenci ismi atanm\u0131\u015f mesajlar yerle\u015ftirdiler. Ayr\u0131mc\u0131l\u0131\u011fa ili\u015fkin g\u00fc\u00e7l\u00fc kan\u0131t buldular: \u00d6\u011fretenlerin beyaz erkeklere \u00f6zg\u00fc isim kullananlara, Hint\u00e7e ve \u00c7ince isimler ile beyaz kad\u0131nlara \u00f6zg\u00fc isimler ta\u015f\u0131yan kullan\u0131c\u0131lara g\u00f6re daha fazla cevap yazd\u0131klar\u0131n\u0131 tespit ettiler. Ba\u015far\u0131ya y\u00f6nelik sosyal-psikolojik engellerin test edilmesinde K\u0131z\u0131lcec, Saltarelli, Reich ve Cohen (2017), kursa ait olmama hakk\u0131ndaki endi\u015feleri azaltmak i\u00e7in tasarlanan teoriye dayal\u0131 m\u00fcdahale faaliyetlerinin, \u00f6\u011frenenler aras\u0131ndaki k\u00fcresel ba\u015far\u0131 a\u00e7\u0131\u011f\u0131n\u0131 az geli\u015fmi\u015f \u00fclkelere kar\u015f\u0131 etkin bir \u015fekilde kapatabildi\u011fini ke\u015ffetmi\u015flerdir. Kulkarni vd. (2015) \u00e7e\u015fitlili\u011fin yararlar\u0131 \u00fczerine e\u015f video tart\u0131\u015fmalar\u0131nda co\u011frafi \u00e7e\u015fitlili\u011fin rol\u00fcn\u00fc s\u0131nam\u0131\u015flar ve daha \u00e7e\u015fitli bir grupta olman\u0131n sonraki test performans\u0131n\u0131 iyile\u015ftirdi\u011fi sonucuna varm\u0131\u015flard\u0131r. Akran de\u011ferlendirmesinde do\u011fal bir deneyden yararlanan Rogers ve Feller (2016), \u00f6rnek akran performans\u0131na maruz kalman\u0131n, motivasyonu ve beklenen ba\u015far\u0131y\u0131 zay\u0131flatan sosyal kar\u015f\u0131la\u015ft\u0131rmas\u0131 nedeniyle y\u0131pranmaya neden oldu\u011funu bulmu\u015ftur. Yine, akran de\u011ferlendirme ba\u011flam\u0131nda Kizilcec (2016), akran s\u0131n\u0131fland\u0131rma s\u00fcrecindeki \u015feffafl\u0131k seviyesinin (\u00f6r. notlar\u0131n nas\u0131l ayarland\u0131\u011f\u0131 ve hesapland\u0131\u011f\u0131n\u0131) \u00f6\u011frenenlerin akran s\u0131n\u0131fland\u0131rmada g\u00fcvenini nas\u0131l etkiledi\u011fini test etmi\u015ftir. Sonu\u00e7lar prosed\u00fcr\u00fcn tarafs\u0131z oldu\u011funu vurgulayan bir a\u00e7\u0131klaman\u0131n beklentilerden d\u00fc\u015f\u00fck bir not alan \u00f6\u011frenenler i\u00e7in g\u00fcvene kar\u015f\u0131 esnekli\u011fi art\u0131rabilece\u011fini g\u00f6stermektedir. Bu ara\u015ft\u0131rma ak\u0131\u015f\u0131ndaki \u00e7al\u0131\u015fmalar, KA\u00c7D'ler ba\u011flam\u0131nda farkl\u0131 olgulara odaklanmaktad\u0131r ve sonu\u00e7lar\u0131, zenginle\u015ftirici teori ve pratik i\u00e7in umut vaat etmektedir.<\/span><\/p>\n\n

KA\u00c7D'lerde Randomize Alan Deneyleri i\u00e7in Metodolojik D\u00fc\u015f\u00fcnceler<\/h3>\n

Yay\u0131nlanan ara\u015ft\u0131rmalarla ilgili bu incelemede, bir\u00e7ok deneyin \u00f6nemli sonu\u00e7lar \u00fcretmedi\u011fi g\u00f6ze \u00e7arpmaktad\u0131r. KA\u00c7D'lerin, pratik olarak \u00f6nemsiz farkl\u0131l\u0131klar\u0131 bile istatistiksel olarak anlaml\u0131 k\u0131lacak \u015fekilde nispeten b\u00fcy\u00fck \u00f6rneklem b\u00fcy\u00fckl\u00fckleri sunmas\u0131 g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, bu \u015fa\u015f\u0131rt\u0131c\u0131 olabilir6<\/a><\/sup>. Bununla birlikte, KA\u00c7D verileri, sonu\u00e7 \u00f6l\u00e7\u00fctlerinde (\u00f6r. kal\u0131c\u0131l\u0131k, dereceleri) \u00f6nemli farkl\u0131l\u0131klar g\u00f6stermektedir. \u0130statistiksel g\u00fc\u00e7, verilerde ger\u00e7ek bir etki saptama \u015fans\u0131, \u00f6rneklem b\u00fcy\u00fckl\u00fc\u011f\u00fc ile artarken veriler g\u00fcr\u00fclt\u00fcl\u00fc hale geldik\u00e7e azal\u0131r. Ara\u015ft\u0131rmac\u0131lar a\u00e7\u0131klanamayan varyans seviyesini dikkate almad\u0131klar\u0131nda \u00f6nemli bir bulgu vermeyen ve etkisi olmayan \u00e7al\u0131\u015fmalarla sonu\u00e7lanabilir. Yine de bu varyans; \u00f6rne\u011fin heterojen i\u015flem etkisini test ederek asl\u0131nda daha fazla inceleme yap\u0131lmas\u0131n\u0131 gerektiren bireysel farkl\u0131l\u0131klar\u0131n varl\u0131\u011f\u0131na i\u015faret edebilir. Genel olarak, KA\u00c7D'lerdeki deneyler de\u011ferlendirilirken ve raporlan\u0131rken, istatistiksel \u00f6nemlerine ek olarak i\u015flem etkisinin b\u00fcy\u00fckl\u00fc\u011f\u00fcne odaklan\u0131lmas\u0131 \u00f6nerilir. Ara\u015ft\u0131rmac\u0131lar a\u00e7\u0131k\u00e7a planlanan do\u011frulay\u0131c\u0131 hipotez testlerini anl\u0131k a\u00e7\u0131klay\u0131c\u0131 analizlerden a\u00e7\u0131k bir \u015fekilde ay\u0131rmal\u0131d\u0131r. KA\u00c7D verilerindeki muhtemel sonu\u00e7lar\u0131n ve de\u011fi\u015fken \u00f6nlemlerin devasa oran\u0131 g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, \u00e7oklu test, derinle\u015fme ara\u015ft\u0131rmas\u0131 ve ara\u015ft\u0131rmac\u0131 serbestlik derecelerinin sonucu olarak (Gelman ve Loken,2013) I tipi hata oran\u0131n\u0131n (sahte pozitif) art\u0131r\u0131lmas\u0131 tehlikesi vard\u0131r. Bu zorlu\u011fun \u00fcstesinden gelmek i\u00e7in, s\u0131k s\u0131k hipotez denemesi (\u00f6r. Kruschke, 2013) Bayes\u00e7i alternatiflerinin \u00e7o\u011falt\u0131lmas\u0131, \u00f6n kayd\u0131 ve kullan\u0131m\u0131, ileriye d\u00f6n\u00fck sa\u011flam bilimsel kan\u0131tlar\u0131n olu\u015fturulmas\u0131na yard\u0131mc\u0131 olabilir.<\/span><\/p>\n

Sadece d\u00f6rt y\u0131ll\u0131k bir olgunun i\u00e7inde mevcut olmas\u0131na ra\u011fmen, KA\u00c7D'lerdeki rastgele sonu\u00e7lu deneyler, e\u011fitim ve ilgili disiplinlerde teoriye \u00f6nemli katk\u0131lar sa\u011flamaya haz\u0131rlanmaktad\u0131r. Ancak e\u011fitimin geli\u015ftirilmesi i\u00e7in \u00e7evrimi\u00e7i saha deneyleri vaadi geni\u015f \u00f6l\u00e7\u00fcde ger\u00e7ekle\u015ftirilmemi\u015ftir. Ger\u00e7ek zamanl\u0131 verilerin mevcudiyeti ve karma\u015f\u0131k paralel deneyler yapmak i\u00e7in gereken eri\u015fim seviyesi \u00fczerindeki s\u0131n\u0131rlar, \u00e7o\u011fu ara\u015ft\u0131rmac\u0131n\u0131n devam eden yeni kurslar\u0131n h\u0131z\u0131nda bir seferde yaln\u0131zca bir fikri test etti\u011fi anlam\u0131na gelir. KA\u00c7D'lerde yap\u0131lan deneylerle h\u0131zl\u0131 yinelemeye y\u00f6nelik kritik bir ad\u0131m, \u00f6\u011frenen pop\u00fclasyonuyla deney yaparak ikili \u00f6\u011frenme hedefini ba\u015farmak ve yinelemeli olarak daha iyi bir \u00f6\u011frenme deneyimi sa\u011flamak i\u00e7in uyarlamal\u0131 deney i\u00e7in zemin haz\u0131rlar. Bu disiplin \u00f6\u011fretim ve \u00f6\u011frenimde burs kazanmak i\u00e7in \u00f6zel bir f\u0131rsat sa\u011flayacakt\u0131r. \u00d6rne\u011fin, \u00f6zyineleme kavram\u0131n\u0131 \u00f6\u011fretenin yeni bir yolunu denemek ve test sonu\u00e7lar\u0131n\u0131 \u00f6nceki toplulukla kar\u015f\u0131la\u015ft\u0131rmak yerine, \u00f6zyineleme i\u00e7in birden fazla yakla\u015f\u0131m e\u015fzamanl\u0131 olarak \u00f6\u011fretilebilir ve ilgili etkinlikleri \u00e7abuk\u00e7a belirlenebilir. Bu bir alandaki \u00e7oklu e\u011fitim teorilerinin e\u015fzamanl\u0131 olarak denenmesini ve g\u00fcn\u00fcm\u00fczde ara\u015ft\u0131rmac\u0131 toplulu\u011funun ve \u00f6nemli kaynaklar\u0131n tamam\u0131 i\u00e7in gerekli heterojen etkileri inceleyerek kuram\u0131n ve uygulaman\u0131n geli\u015ftirilmesi s\u00fcrecini sa\u011flayacakt\u0131r. Williams ve meslekta\u015flar\u0131, (2014), KA\u00c7D'lerde, s\u00fcrmekte olan deneylerin sonu\u00e7lar\u0131na g\u00f6re uyarlanan k\u00fc\u00e7\u00fck i\u00e7erik par\u00e7alar\u0131 olan KA\u00c7D'lerde uyarlanabilir deneyler i\u00e7in ilk konsept \u00f6nerdiler. \u0130leriye d\u00f6n\u00fck, deneysel ko\u015fullara dinamik atama, \u00f6rne\u011fin \u00e7ok kollu bir haydut7<\/a><\/sup> algoritmas\u0131 kullanarak (Bather ve Gittins, 1990), \u00f6zellikle PlanOut gibi karma\u015f\u0131k ve paralel tasar\u0131mlar\u0131 destekleyen deneysel sistemle birlikte, kurs tasar\u0131mlar\u0131 \u00fczerinde h\u0131zl\u0131 yineleme yapabilir (Bakshy, Eckles ve Bernstein, 2014). Genel olarak, KA\u00c7D'lerdeki randomize saha deneyleri ara\u015ft\u0131rmac\u0131lara teori ve prati\u011fi h\u0131zl\u0131 bir \u015fekilde zenginle\u015ftirmek i\u00e7in yeni bir f\u0131rsat sunuyor.<\/span><\/p>\n\n

SONU\u00c7<\/h2>\n

Geni\u015f \u00e7apl\u0131 rastgele sonu\u00e7lu deneylerin h\u0131zl\u0131 bir \u015fekilde heterojen \u00f6\u011frenen pop\u00fclasyonuna uygulanabilirli\u011fini, y\u00fcr\u00fct\u00fclen e\u011fitsel ara\u015ft\u0131rma \u015fekillerinin bozulmas\u0131na neden olacak g\u00fcce sahiptir. Ge\u00e7mi\u015fte \u00f6\u011frenme teorileri, ko\u015fullar \u00fczerinde kontrol\u00fcn zor oldu\u011fu (\u00f6r. BESZD ba\u011flam\u0131ndaki y\u00fcksek\u00f6\u011frenim s\u0131n\u0131f \u00e7al\u0131\u015fmalar\u0131) az say\u0131da \u00e7ok se\u00e7ici ortam\u0131n dikkatli bir \u015fekilde incelenmesinden kaynaklan\u0131yor olsa da g\u00fcn\u00fcm\u00fczde d\u00fcnya \u00e7ap\u0131nda tek bir kursta on binlerce \u00f6\u011frenene y\u00fcksek kalitede rastgele sonu\u00e7lu deneyler uygulamak m\u00fcmk\u00fcnd\u00fcr ki bu alanda benzeri g\u00f6r\u00fclmemi\u015f bir f\u0131rsatt\u0131r.<\/span><\/p>\n

Geleneksel y\u00fcksek\u00f6\u011frenim ara\u015ft\u0131rmalar\u0131, deneysel sorgulama i\u00e7in iki ana pratik k\u0131s\u0131tlama ile kar\u015f\u0131 kar\u015f\u0131ya kalm\u0131\u015ft\u0131r. Belki de en \u00f6nemli k\u0131s\u0131tlama, \u00f6\u011fretim sorumlulu\u011fu paradigmas\u0131d\u0131r. Y\u00fcksek\u00f6\u011frenim s\u0131n\u0131flar\u0131nda, dersi veren \u00f6\u011fretim \u00fcyesi \u00f6\u011frenci deneyiminden tamamen sorumlu olma e\u011filimindedir. B\u00f6ylece, fak\u00fclte, deneysel bir yakla\u015f\u0131m yerine e\u015fitlik odakl\u0131 bir yakla\u015f\u0131m benimsemekte ve s\u0131n\u0131ftaki t\u00fcm \u00f6\u011frencilerin destek ve m\u00fcdahalelere e\u015fit eri\u015fime sahip olmalar\u0131n\u0131 sa\u011flamaktad\u0131r. Bu \u015fartlarda yenilik, verilen bir topluluk veya \u00e7al\u0131\u015fma y\u0131l\u0131ndaki \u00f6\u011frenenlerin di\u011fer topluluklarla veya y\u0131llarca yap\u0131lan \u00e7al\u0131\u015fmalarla kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131, daha \u015fa\u015f\u0131rt\u0131c\u0131 de\u011fi\u015fkenler ortaya \u00e7\u0131karan yar\u0131 deneysel y\u00f6ntemlerle olma e\u011filimindedir. KA\u00c7D'lerde, paradigma, belki de \u00f6\u011frenenlerin ba\u015far\u0131s\u0131 i\u00e7in bir sorumluluk \u00fcstlenen kurumsal y\u00f6netim ve tedarik\u00e7i ortaklar\u0131 da d\u00e2hil olmak \u00fczere k\u0131smen daha geni\u015f akt\u00f6r toplulu\u011fu nedeniyle farkl\u0131d\u0131r. Bu akt\u00f6rlerden baz\u0131lar\u0131, \u00f6zellikle h\u0131zl\u0131 prototipleme ve test i\u015fleminin ilke oldu\u011fu risk sermayesi fonlu i\u015fletmelerde risk ve \u00f6d\u00fcl dengeleme etraf\u0131nda k\u00fclt\u00fcr, \u00f6\u011frenenin ba\u015far\u0131s\u0131n\u0131 ilerletmek i\u00e7in deneysel yakla\u015f\u0131mlara y\u00f6nelik daha olumlu tutumlar geli\u015ftirmi\u015ftir.<\/span><\/p>\n

Geleneksel y\u00fcksek\u00f6\u011frenim ara\u015ft\u0131rmalar\u0131nda ikinci bir k\u0131s\u0131tlama, deney yoluyla sa\u011flanan kabul edilebilir risk ve \u00f6d\u00fcl miktar\u0131d\u0131r. Bir tarafta topluma de\u011ferini g\u00f6stermi\u015f y\u00fczlerce y\u0131ll\u0131k y\u00fcksek\u00f6\u011frenim varken ve di\u011fer taraftan belirli bir \u00f6\u011frencinin \u00f6\u011frenimi i\u00e7in de y\u00fcz binlerce dolar tehlike alt\u0131ndayken ara\u015ft\u0131rmac\u0131lar\u0131n y\u00fcksek riskli ara\u015ft\u0131rmalara kat\u0131lmalar\u0131 i\u00e7in etik arg\u00fcman olu\u015fturmalar\u0131 daha zordur. Yine de KA\u00c7D ortamlar\u0131nda, \u00f6\u011frenenlerin \u00e7o\u011fu \u00fccretsiz olarak kay\u0131t yapt\u0131rmaktad\u0131r ve \u00e7ok az\u0131, KA\u00c7D deneyinin sonu\u00e7lar\u0131yla ilgili ge\u00e7im kaynaklar\u0131n\u0131 kaybetme tehlikesi alt\u0131ndad\u0131r8<\/a><\/sup>. Bu fark kurumsal politikaya yans\u0131m\u0131\u015ft\u0131r. Pek \u00e7ok kurum, ABD'deki AEHMY gibi yasal zorunluluklar nedeniyle \u00f6\u011frenci kay\u0131tlar\u0131 ve mahremiyet i\u00e7in g\u00fc\u00e7l\u00fc korumalara sahiptir. Bununla birlikte, KA\u00c7D'lerdeki baz\u0131 k\u0131s\u0131tlamalar\u0131, \u00e7evrimi\u00e7i \u00f6\u011frenenlerle yap\u0131lan deneysel ara\u015ft\u0131rmalarla ilgili politikalardan kald\u0131ran \u00f6\u011frenenler i\u00e7in (veya \u201ckullan\u0131c\u0131lar\u201d) ayn\u0131 y\u00fck\u00fcml\u00fcl\u00fckler mevcut de\u011fildir. Bunun ara\u015ft\u0131rmac\u0131lar i\u00e7in iki \u00f6nemli etkisi ve f\u0131rsat\u0131 var:<\/span><\/p>\n\n

    \n \t
  1. \n

    KA\u00c7D \u00f6\u011frenenlerinin pop\u00fclasyonu farkl\u0131d\u0131r ve bir\u00e7ok y\u00f6nden, geleneksel demografik ara\u015ft\u0131rmalardan (\u00f6\u011frenen demografisi (ya\u015f, \u0131rk, k\u00fclt\u00fcrel ge\u00e7mi\u015f, vb.), \u00d6n bilgi ve ders alma motivasyonlar\u0131) bak\u0131m\u0131ndan \u00e7ok daha \u00e7e\u015fitlidir. Bu detayl\u0131 sunum, \u00e7ok say\u0131da \u00f6\u011frenenle birlikte, bilim insanlar\u0131na, \u00f6\u011frenme kuramlar\u0131n\u0131n pop\u00fclasyonlar aras\u0131nda genelle\u015ftirilebilirli\u011fini s\u0131nanmas\u0131n\u0131 ve belirli \u00f6\u011frenen gruplar\u0131na y\u00f6nelik en uygun kimlik \u00f6\u011frenme teorileri i\u00e7in bir f\u0131rsat sa\u011flar. Bu b\u00f6yle b\u00fcy\u00fck veri k\u00fcmeleri gerektiren sorunlara niceliksel yakla\u015f\u0131mlar sa\u011flayabilir; \u00f6rne\u011fin, Dillahunt, Ng, Fiesta ve Wang\u2019\u0131n (2016) KA\u00c7D'leri sosyal hareketlilik i\u00e7in kullanan d\u00fc\u015f\u00fck gelirli topluluklarla ilgili ara\u015ft\u0131rmalar\u0131, KA\u00c7D\u2019lerde kay\u0131tl\u0131 olan \u00f6\u011frenenlerin say\u0131s\u0131 olmasayd\u0131, nicel olarak \u00e7al\u0131\u015fmak zor olacakt\u0131.<\/span><\/p>\n<\/li>\n \t

  2. \n

    Ara\u015ft\u0131rmac\u0131lar\u0131n \u00f6\u011frenme platformunda do\u011frudan \u00fccretsiz deney yapabilmeleri \u00f6\u011frenen verilerinin hacmini ve varyans\u0131n\u0131 daha fazla bilimsel etki i\u00e7in art\u0131rmalar\u0131n\u0131 sa\u011flar. Bu ara\u015ft\u0131rma, kuram ve uygulaman\u0131n b\u00fct\u00fcnle\u015ftirilmesini te\u015fvik ederek e\u011fitimdeki geri bildirim d\u00f6ng\u00fcs\u00fcn\u00fc bitirme f\u0131rsat\u0131 sunar. KA\u00c7D'lerdeki \u00f6\u011frenen pop\u00fclasyonunun geni\u015fli\u011fi ve derinli\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, deneyimine, di\u011fer \u00f6\u011frenenlerin deneyimlerine ve temeldeki platform verilerine dayanarak \u00f6\u011frenene (yar\u0131) otomatik olarak adapte olan ortamlar olu\u015fturmak i\u00e7in ger\u00e7ek bir olas\u0131l\u0131k vard\u0131r.<\/span><\/p>\n<\/li>\n<\/ol>\n

    Bu b\u00f6l\u00fcmde, daha kapsaml\u0131 ve \u00e7evik9<\/a><\/sup> bir \u00f6\u011frenme biliminin m\u00fcmk\u00fcn k\u0131laca\u011f\u0131na inand\u0131\u011f\u0131m\u0131z KA\u00c7D ara\u015ft\u0131rmalar\u0131n\u0131n iki kayna\u011f\u0131n\u0131 -\u00e7e\u015fitli b\u00fcy\u00fck verilerin mevcudiyeti ve h\u0131zl\u0131 yinelemeyle rastgele alan deneyleri yapma kabiliyetini- ele ald\u0131k. B\u00fcy\u00fck \u00f6l\u00e7\u00fcde kabul g\u00f6rmeleri ve bilgi i\u015flemsel y\u00f6ntemlerinin incelenmesi ile nitelenen \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi alanlar\u0131 bu d\u00fc\u015f\u00fcnceye cevap vermeye ve ilerleyen zamanlarda daha da geni\u015f bir etki yaratmaya haz\u0131rlard\u0131r.<\/span><\/p>\n\n

    KAYNAK\u00c7A<\/h2>\nAmbrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works: Seven research-based principles for smart teaching<\/i>. Jossey-Bass. <\/span><\/span>\n\nAnderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2014). Engaging with massive online courses. Proceedings of the 23rd International Conference on World Wide Web <\/i>(WWW\u201914), 7\u201311 April 2014, Seoul, Republic of Korea (pp. 687\u2013698). New York: ACM. <\/span><\/span>\n\nBaker, R., Dee, T., Evans, B., & John, J. (2015). Bias in online classes: Evidence from a field experiment. Paper presented at the SREE Spring 2015 Conference, Learning Curves: Creating and Sustaining Gains from Early Childhood through Adulthood<\/i>, 5\u20137 March 2015, Washington, DC, USA. <\/span><\/span>\n\nBakshy, E., Eckles, D., & Bernstein, M. S. (2014). Designing and deploying online field experiments. Proceedings of the 23rd International Conference on World Wide Web <\/i>(WWW\u201914), 7\u201311 April 2014, Seoul, Republic of Korea (pp. 283\u2013292). New York: ACM. <\/span><\/span>\n\nBather, J. A., & Gittins, J. C. (1990). Multi-armed bandit allocation indices. Journal of the Royal Statistical Society: Series A, 153<\/i>(2), 257. <\/span><\/span>\n\nBlanchard, E.G. (2012). On the WEIRD nature of ITS\/AIED conferences. In S. A. Cerri et al. (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems <\/i>(ITS 2012), 14\u201318 June 2012, Chania, Greece (pp. 280\u2013285). Lecture Notes in Computer Science. Springer Berlin Heidelberg. <\/span><\/span>\n\nBreslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom research into edX\u2019s first MOOC. Research & Practice in Assessment, 8<\/i>. http:\/\/www.rpajournal.com\/dev\/wp-content\/uploads\/2013\/05\/SF2.pdf. <\/span><\/span>\n\nBrooks, C., Thompson, C., & Teasley, S. (2015a). A time series interaction analysis method for building predictive models of learners using log data. Proceedings of the 5th International Conference on Learning Analytics and Knowledge <\/i>(LAK\u201915), 16\u201320 March 2015, Poughkeepsie, NY, USA (pp. 126\u2013135). New York: ACM. <\/span><\/span>\n\nBrooks, C., Thompson, C., & Teasley, S. (2015b). Who you are or what you do: Comparing the predictive power of demographics vs. activity patterns in massive open online courses (MOOCs). Proceedings of the 2nd ACM Conference on Learning @ Scale <\/i>(L@S 2015), 14\u201318 March 2015, Vancouver, BC, Canada (pp. 245\u2013248). New York: ACM. <\/span><\/span>\n\nClow, D. (2013). MOOCs and the funnel of participation. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge <\/i>(LAK\u201913), 8\u201312 April 2013, Leuven, Belgium. New York: ACM. doi:10.1145\/2460296.2460332 <\/span><\/span>\n\nCochran, M. J., & Heron, P. R. L. (2006). Development and assessment of research-based tutorials on heat engines and the second law of thermodynamics. American Journal of Physics, 74<\/i>(8), 734. <\/span><\/span>\n\nCoetzee, D., Fox, A., Hearst, M. A., & Hartmann, B. (2014). Should your MOOC forum use a reputation system? Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing <\/i>(CSCW\u201914), 15\u201319 February 2014, Baltimore, Maryland, USA (pp. 1176\u20131187). New York: ACM. <\/span><\/span>\n\nDavis, D., Chen, G., van der Zee, T., Hauff, C., & Houben, G.-J. (2016). Retrieval practice and study planning in MOOCs: Exploring classroom-based self-regulated learning strategies at scale. Proceedings of the 11th European Conference on Technology Enhanced Learning <\/i>(EC-TEL 2016), 13\u201316 September 2016, Lyon, France (pp. 57\u201371). Lecture Notes in Computer Science. Springer International Publishing. <\/span><\/span>\n\nDeBoer, J., Stump, G. S., Seaton, D., & Breslow, L. (2013). Diversity in MOOC students\u2019 backgrounds and behaviors in relationship to performance in 6.002x. Proceedings of the 6th Conference of MIT\u2019s Learning International Networks Consortium <\/i>(LINC 2013), 16\u201319 June 2013, Cambridge, Massachusetts, USA. https:\/\/tll.mit.edu\/sites\/default\/files\/library\/LINC%20'13.pdf <\/span><\/span>\n\nDillahunt, T. R., Ng, S., Fiesta, M., & Wang, Z. (2016). Do massive open online course platforms support employability? Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work & Social Computing <\/i>(CSCW\u201916), 27 February\u20132 March 2016, San Francisco, CA, USA (pp. 233\u2013244). New York: ACM. <\/span><\/span>\n\nEmanuel, E.J. (2013). Online education: MOOCs taken by educated few. Nature, 503<\/i>(7476), 342.<\/span><\/span>\n\nFestinger, L. (1954). A theory of social comparison processes. Human Relations: Studies towards the Integration of the Social Sciences, 7<\/i>(2), 117\u2013140. <\/span><\/span>\n\nGa\u0161evi\u0107, D., Dawson, S., Rogers, T., & Ga\u0161evi\u0107, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28<\/i>(January), 68\u201384. <\/span><\/span>\n\nGelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no \u201cfishing expedition\u201d or \u201cp-hacking\u201d and the research hypothesis was posited ahead of time. http:\/\/www.stat.columbia.edu\/~gelman\/research\/unpublished\/p_hacking.pdf. <\/span><\/span>\n\nGuo, P. J., & Reinecke, K. (2014). Demographic differences in how students navigate through MOOCs. Proceedings of the 1st ACM Conference on Learning @ Scale <\/i>(L@S 2014), 4\u20135 March 2014, Atlanta, Georgia, USA (pp. 21\u201330). New York: ACM. <\/span><\/span>\n\nHansen, J. D., & Reich, J. (2015). Democratizing education? Examining access and usage patterns in massive open online courses. Science, 350<\/i>(6265), 1245\u20131248. <\/span><\/span>\n\nHenrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? The Behavioral and Brain Sciences, 33<\/i>(2\u20133), 61\u201383; discussion 83\u2013135. <\/span><\/span>\n\nJonassen, D. H., & Grabowski, B. L. H. (1993). Handbook of individual differences, learning, and instruction. Routledge. <\/span><\/span>\n\nKalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38<\/i>(1), 23\u201331. <\/span><\/span>\n\nKizilcec, R. F. (2016). How much information? Effects of transparency on trust in an algorithmic interface. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems <\/i>(CHI\u201916), 7\u201312 May 2016, San Jose, CA, USA (pp. 2390\u20132395). New York: ACM. <\/span><\/span>\n\nKizilcec, R. F., Bailenson, J. N., & Gomez, C. J. (2015). The instructor\u2019s face in video instruction: Evidence from two large-scale field studies. Journal of Educational Psychology, 107<\/i>(3), 724\u2013739. <\/span><\/span>\n\nKizilcec, R. F., & Halawa, S. (2015). Attrition and achievement gaps in online learning. Proceedings of the 2nd ACM Conference on Learning @ Scale <\/i>(L@S 2015), 14\u201318 March 2015, Vancouver, BC, Canada (pp. 57\u201366). New York: ACM. <\/span><\/span>\n\nKizilcec, R. F., P\u00e9rez-Sanagust\u00edn, M., & Maldonado, J. J. (2016). Recommending self-regulated learning strategies does not improve performance in a MOOC. Proceedings of the 3rd ACM Conference on Learning @ Scale <\/i>(L@S 2016), 25\u201328 April 2016, Edinburgh, Scotland (pp. 101\u2013104). New York: ACM. <\/span><\/span>\n\nKizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge <\/i>(LAK\u201913), 8\u201312 April 2013, Leuven, Belgium. New York: ACM. <\/span><\/span>\n\nKizilcec, R. F., Saltarelli, A. J., Reich, J., & Cohen, G. L. (2017). Closing global achievement gaps in MOOCs. Science, 355<\/i>(6322), 251-252. <\/span><\/span>\n\nKizilcec, R. F., & Schneider, E. (2015). Motivation as a lens to understand online learners: Toward data-driven design with the OLEI scale. ACM Transactions on Computer\u2013Human Interaction, 22<\/i>(2), 1\u201324. <\/span><\/span>\n\nKizilcec, R. F., Schneider, E., Cohen, G. L., & McFarland, D. A. (2014). Encouraging forum participation in online courses with collectivist, individualist and neutral motivational framings. eLearning Papers, 37<\/i>, 13\u201322. <\/span><\/span>\n\nKoedinger, K. R., Booth, J. L., & Klahr, D. (2013). Instructional complexity and the science to constrain it. Science, 342<\/i>(6161), 935\u2013937. <\/span><\/span>\n\nKruschke, J.K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142<\/i>(2), 573\u2013603. <\/span><\/span>\n\nKulkarni, C., Cambre, J., Kotturi, Y., Bernstein, M. S., & Klemmer, S. R. (2015). Talkabout: Making distance matter with small groups in massive classes. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing <\/i>(CSCW\u201915), 14\u201318 March 2015, Vancouver, BC, Canada (pp. 1116\u20131128). New York: ACM.<\/span><\/span>\n\nLamb, A., Smilack, J., Ho, A., & Reich, J. (2015). Addressing common analytic challenges to randomized experiments in MOOCs: Attrition and zero-inflation. Proceedings of the 2nd ACM Conference on Learning @ Scale <\/i>(L@S 2015), 14\u201318 March 2015, Vancouver, BC, Canada (pp. 21\u201330). New York: ACM. <\/span><\/span>\n\nLaney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6<\/i>, 70. <\/span><\/span>\n\nLi, J. (2012). Cultural foundations of learning: East and west. Cambridge University Press. <\/span><\/span>\n\nMakel, M. C., & Plucker, J. A. (2014). Facts are more important than novelty: Replication in the education sciences. Educational Researcher, 43<\/i>(6), 304\u2013316. <\/span><\/span>\n\nMartinez, I. (2014). MOOCs as a massive research laboratory. University of Virginia. <\/span><\/span>\n\nMayer, R.E. (2001). Multimedia learning<\/i>. Cambridge University Press. <\/span><\/span>\n\nMitchell, S. D. (2009). Unsimple truths: Science, complexity, and policy<\/i>. University of Chicago Press. <\/span><\/span>\n\nMullaney, T., & Reich, J. (2015). Staggered versus all-at-once content release in massive open online courses: Evaluating a natural experiment. Proceedings of the 2nd ACM Conference on Learning @ Scale <\/i>(L@S 2015), 14\u201318 March 2015, Vancouver, BC, Canada (pp. 185\u2013194). New York: ACM. <\/span><\/span>\n\nOcumpaugh, J., Baker, R., Gowda, S., Heffernan, N., & Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45<\/i>(3), 487\u2013501. <\/span><\/span>\n\nPappano, L. (2012, November 2). The year of the MOOC. The New York Times<\/i>. http:\/\/www.nytimes.com\/2012\/11\/04\/education\/edlife\/massive-open-online-courses-are-multiplying-at-a-rapid-pace.html <\/span><\/span>\n\nReich, J. (2014, December 8). MOOC completion and retention in the context of student intent. EDUCAUSE Review Online<\/i>. http:\/\/er.educause.edu\/articles\/2014\/12\/mooc-completion-and-retention-in-the-context-of-student-intent <\/span><\/span>\n\nReich, J. (2015). Rebooting MOOC research. Science, 347<\/i>(6217), 34\u201335. <\/span><\/span>\n\nRenz, J., Hoffmann, D., Staubitz, T., & Meinel, C. (2016). Using A\/B testing in MOOC environments. Proceedings of the 6th International Conference on Learning Analytics and Knowledge <\/i>(LAK\u201916), 25\u201329 April 2016, Edinburgh, UK (pp. 304\u2013313). New York: ACM. <\/span><\/span>\n\nRienties, B., & Tempelaar, D. (2013). The role of cultural dimensions of international and Dutch students on academic and social integration and academic performance in the Netherlands. International Journal of Intercultural Relations, 37<\/i>(2), 188\u2013201. <\/span><\/span>\n\nRogers, T., & Feller, A. (2016). Discouraged by peer excellence: Exposure to exemplary peer performance causes quitting. 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Advances in Experimental Social Psychology, 34<\/i>, 379\u2013440. <\/span><\/span>\n\nThaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and happiness. Penguin. <\/span><\/span>\n\nThille, C., Schneider, E., Kizilcec, R. F., Piech, C., Halawa, S. A., & Greene, D. K. (2014). The future of data-enriched assessment. Research & Practice in Assessment, 9<\/i>, 5\u201316.<\/span><\/span>\n\nTomkin, J. H., & Charlevoix, D. (2014). Do professors matter? Using an A\/B test to evaluate the impact of instructor involvement on MOOC student outcomes. Proceedings of the 1st ACM Conference on Learning @ Scale <\/i>(L@S 2014), 4\u20135 March 2014, Atlanta, Georgia, USA (pp. 71\u201378). New York: ACM. <\/span><\/span>\n\nWaldrop, M. M. (2013). Online learning: Campus 2.0. Nature, 495<\/i>(7440), 160\u2013163. <\/span><\/span>\n\nWalton, G. M., & Cohen, G. L. (2007). A question of belonging: Race, social fit, and achievement. 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Theoretical models of culture shock and adaptation in international students in higher education. Studies in Higher Education, 33<\/i>(1), 63\u201375.<\/span><\/span>\n\n
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    1<\/a> https:\/\/blog.coursera.org\/post\/142363925112<\/span><\/span><\/p>\n\n<\/div>\n

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    2<\/a> https:\/\/about.futurelearn.com\/press-releases\/future-learnhas-3million-learners\/<\/span><\/span><\/p>\n\n<\/div>\n

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    3<\/a> http:\/\/blog.edx.org\/edx-yearin-review?track=blog<\/span><\/span><\/p>\n\n<\/div>\n

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    4<\/a> https:\/\/www.udacity.com\/success<\/span><\/span><\/p>\n\n<\/div>\n

    \n

    5<\/a> orj. onboarding<\/span><\/span><\/p>\n\n<\/div>\n

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    6<\/a> Sosyal bilimlerde standart uygulama oldu\u011fu gibi, e\u011fitim ara\u015ft\u0131rmalar\u0131 alan\u0131 da deneysel sonu\u00e7lar\u0131n istatistiksel \u00f6nemini de\u011ferlendirmek i\u00e7in p <0.05 kriterini benimsemi\u015ftir. Bu nedenle, en az\u0131ndan s\u0131f\u0131r hipotezi do\u011fruyken, \u00f6rnek verilerde oldu\u011fu kadar u\u00e7 bir etki elde etme \u015fans\u0131n\u0131n %5'ten daha az olmas\u0131 durumunda, bo\u015f\/s\u0131f\u0131r (\u00f6r. e\u015fit ko\u015fullu ortalamalar) reddedilir..<\/span><\/span><\/p>\n\n<\/div>\n

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    7<\/a> \u00c7evirenin notu: olas\u0131l\u0131k teorisinde kullan\u0131lan ve \u00e7ok kollu haydut olarak \u00e7evirdi\u011fimiz multi\u2013armed bandit ifadesi tek kollu kumar makinelerinden attfen alan yaz\u0131nda kullan\u0131lmaktad\u0131r.<\/span><\/span><\/p>\n\n<\/div>\n

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    8<\/a> KA\u00c7D kimlik bilgilerinin Arizona Devlet \u00dcniversitesi Global Freshman Akademisi ve MIT Mikro-Master programlar\u0131 gibi y\u00fcksek\u00f6\u011fretimde kredi olarak kabul edilmesine y\u00f6nelik son yakla\u015f\u0131mlar, \u00f6\u011frenenlerin ilgi ve y\u00f6nelimlerini de\u011fi\u015ftirmeye ba\u015flam\u0131\u015ft\u0131r.<\/span><\/span><\/p>\n\n<\/div>\n

    \n

    9<\/a> orj. agile<\/span><\/span><\/p>\n\n<\/div>\n","rendered":"

    Rene F. Kizilcec1<\/sup>, Christopher Brooks2<\/sup><\/span><\/span><\/p>\n

    1<\/sup>\u0130leti\u015fim B\u00f6l\u00fcm\u00fc, Stanford \u00dcniversitesi, ABD <\/span><\/span><\/p>\n

    2<\/sup>Bilgi Okulu, Michigan \u00dcniversitesi, ABD<\/span><\/span><\/p>\n

    DOI: 10.18608\/hla17.018<\/span><\/span><\/p>\n

    \u00d6Z<\/h2>\n

    B\u00fcy\u00fck \u00f6l\u00e7ekli e\u011fitim i\u00e7eri\u011finin da\u011f\u0131t\u0131m\u0131nda yeni bir mekanizma olan, kitlesel a\u00e7\u0131k \u00e7evrimi\u00e7i dersler (KA\u00c7D) d\u00fcnya \u00e7ap\u0131nda milyonlarca \u00f6\u011frenenin ilgisini \u00e7ekmektedir. KA\u00c7D’lerin yak\u0131n tarihinin k\u0131sa bir \u00f6zeti olan bu b\u00f6l\u00fcm, ara\u015ft\u0131rma arac\u0131 olarak onlar\u0131n potansiyellerine odaklanmaktad\u0131r. \u00d6\u011frenme analiti\u011fi ve daha geni\u015f anlamda \u00f6\u011frenme bilimi hakk\u0131ndaki ara\u015ft\u0131rmalar\u0131 daha ileri seviyeye ta\u015f\u0131mak i\u00e7in bu ortamlar\u0131n iki \u00f6nemli sa\u011flay\u0131c\u0131s\u0131n\u0131 irdelemekteyiz. Bunlardan ilki, e\u011fitimde \u00e7e\u015fitli b\u00fcy\u00fck verilerin mevcudiyetidir. Heterojen \u00f6\u011frenen \u00f6rnekleri ile yap\u0131lan ara\u015ft\u0131rmalar, daha az elde edilen e\u011fitsel veri k\u00fcmelerinde geleneksel olarak yeterince temsil edilmeyen demografik ve sosyok\u00fclt\u00fcrel gruplardan gelen insanlar\u0131 daha iyi a\u00e7\u0131klayan daha kapsaml\u0131 bir \u00f6\u011frenme bilimini ilerletebilir. \u0130kinci sa\u011flay\u0131c\u0131 ise b\u00fcy\u00fck \u00f6l\u00e7ekli saha deneylerini minimum maliyetle yapabilme yetene\u011fidir. Ara\u015ft\u0131rmac\u0131lar \u00e7oklu teoriye dayal\u0131 m\u00fcdahaleleri h\u0131zl\u0131 bir \u015fekilde de\u011ferlendirebilirler ve bu m\u00fcdahalelerin otantik bir \u00f6\u011frenme ortam\u0131ndaki etkilikleri hakk\u0131nda tesad\u00fcfi \u00e7\u0131kar\u0131mlara varabilirler. Farkl\u0131 t\u00fcrdeki b\u00fcy\u00fck veri ve deneyleme bir arada bireysel farkl\u0131l\u0131klar\u0131n nedenini a\u00e7\u0131klayabilecek \u201ckim i\u00e7in neyin i\u015fe yarad\u0131\u011f\u0131\u201d teorilerine kan\u0131t sa\u011flar ve materyalleri etkili bir \u015fekilde belirlemeye y\u00f6nelik giri\u015fimleri ve \u00e7evrim i\u00e7i \u00f6\u011frenme ortamlar\u0131nda olan yap\u0131lar\u0131 destekler.<\/span><\/span><\/p>\n

    Anahtar Kelimeler<\/span>: Ara\u015ft\u0131rma metodolojisi, \u00e7ok \u00e7e\u015fitli veriler, randomize saha deneyleri, kapsay\u0131c\u0131 bilim, KA\u00c7D<\/span><\/span><\/p>\n

    Kitlesel a\u00e7\u0131k \u00e7evrimi\u00e7i dersler (KA\u00c7D), d\u00fcnya \u00e7ap\u0131nda bir izleyici kitlesine d\u00fc\u015f\u00fck maliyetli e\u011fitim deneyimleri sa\u011flayan teknolojik bir yeniliktir. 2012 y\u0131l\u0131nda, ilk KA\u00c7D’lerden baz\u0131lar\u0131, y\u00fcksek\u00f6\u011frenimdeki aksakl\u0131klar\u0131n giderilmesine ivme kazand\u0131rarak d\u00fcnya \u00e7ap\u0131nda \u00fclkelerden y\u00fcz binlerce insan\u0131n ilgisini \u00e7ekmi\u015ftir(Waldrop, 2013). Sadece birka\u00e7 y\u0131l sonra, d\u00fcnya \u00e7ap\u0131nda y\u00fczlerce kurum, KA\u00c7D’leri Coursera, EdX ve FutureLearn gibi \u00e7evrimi\u00e7i \u00f6\u011frenme platformlar\u0131nda sunmaya ba\u015flad\u0131. Y\u00fcksek\u00f6\u011frenime eri\u015fimin geni\u015fletilmesinin \u00f6tesinde, KA\u00c7D’ler mevcut akademik topluluklardaki burslar\u0131 besleyen ve tarihsel olarak \u00f6\u011frenme bilimlerine daha az d\u00e2hil olan disiplinlere ilgi uyand\u0131ran e\u015fi g\u00f6r\u00fclmemi\u015f miktarda e\u011fitsel veri \u00fcretmi\u015ftir. Bu mevcut disiplinler aras\u0131 topluluklardaki ara\u015ft\u0131rmalar\u0131 g\u00fc\u00e7lendirmi\u015f ve e\u011fitim, bilgisayar bilimi, insan fakt\u00f6rleri ile istatistiklerin kesi\u015fme noktalar\u0131nda tamamen yeni topluluklar\u0131n olu\u015fumuna yol a\u00e7m\u0131\u015ft\u0131r. \u00d6\u011frenme analiti\u011fi alan\u0131nda, yeni nesil ara\u015ft\u0131rmay\u0131 geli\u015ftirebilecek KA\u00c7D’lerin iki yeni \u00f6zelli\u011fini vurguluyoruz: e\u011fitsel verilerin sadece b\u00fcy\u00fck de\u011fil ayn\u0131 zamanda \u00e7e\u015fitli \u00f6\u011frenen d\u00fczeyinde bulunmas\u0131 ve b\u00fcy\u00fck \u00e7evrimi\u00e7i alan deneylerini d\u00fc\u015f\u00fck maliyetle yapma imk\u00e2n\u0131.<\/span><\/p>\n

    KA\u00c7D’lerin yenilik\u00e7i ara\u015ft\u0131rmay\u0131 destekleyen ilk \u00f6zelli\u011fi toplanabilecek verilerin miktar\u0131 ve niteli\u011fidir. KA\u00c7D’ler derin ve geni\u015f kapsaml\u0131 \u00f6\u011frenen verilerini toplar: \u00e7ok say\u0131da \u00f6\u011frenen i\u00e7in her bir \u00f6\u011frencinin \u00f6\u011frenme ortam\u0131ndaki i\u00e7erikle etkile\u015fimlerinden elde edilen elveri\u015fli kay\u0131tlar (Thille vd., 2014). Son zamanlarda bu mevcut verilerin boyutlar\u0131 makine \u00f6\u011frenmesi uygulamalar\u0131na ve daha \u00f6nceden m\u00fcmk\u00fcn olmayan veri madencili\u011fi tekniklerinin kullan\u0131lmas\u0131na imk\u00e2n vermektedir. Bununla birlikte, b\u00fcy\u00fck \u00f6l\u00e7e\u011fin \u00f6tesinde, \u00f6\u011frenen n\u00fcfusu da KA\u00c7D’lerde genel y\u00fcksek\u00f6\u011frenim derslerine g\u00f6re \u00e7ok daha \u00e7e\u015fitlidir. KA\u00c7D’ler, \u00e7o\u011fu deneysel sosyal bilimin dayand\u0131\u011f\u0131 pop\u00fclasyon olan Bat\u0131l\u0131, E\u011fitimli, Sanayile\u015fmi\u015f, Zengin ve Demokratik (BESZD) \u00fclkelerin d\u0131\u015f\u0131ndan da daha fazla \u00f6\u011frenen \u00e7ekmektedir (Henrich, Heine ve Norenzayan, 2010). Daha geni\u015f bir kitleye uygulanan farkl\u0131 veri kapsay\u0131c\u0131 bilimsel teorilerin ve e\u011fitsel uygulamalar\u0131n geli\u015ftirilmesi i\u00e7in son derece \u00f6nemlidir. Ayr\u0131ca, b\u00fcy\u00fck \u00e7e\u015fitlilikteki veriler, mevcut ara\u015ft\u0131rmalarda k\u00fc\u00e7\u00fck veya homojen \u00f6rneklerle ara\u015ft\u0131r\u0131lamayan demografik ve sosyok\u00fclt\u00fcrel gruplar (\u00f6r. bir m\u00fcdahalenin heterojen etkileri) aras\u0131ndaki bireysel farkl\u0131l\u0131klar\u0131 tan\u0131mlayan ara\u015ft\u0131rmay\u0131 m\u00fcmk\u00fcn k\u0131lar.<\/span><\/p>\n

    E\u011fitim ara\u015ft\u0131rmalar\u0131n\u0131n h\u0131z\u0131n\u0131 ve etkisini art\u0131rmay\u0131 vaat eden KA\u00c7D’lerin ikinci \u00f6zelli\u011fi, \u00e7evrimi\u00e7i deneyleri h\u0131zl\u0131, ekonomik ve y\u00fcksek kalitede ger\u00e7ekle\u015ftirme kabiliyetidir (Reich, 2015). Teknoloji sekt\u00f6r\u00fcnde, bu, bireylerin rastgele olarak iki test ko\u015fulundan birine y\u00f6nlendirildi\u011fini iletmek i\u00e7in A \/ B testi olarak adland\u0131r\u0131l\u0131r. \u00c7evrimi\u00e7i deneme, teorileri ve uygulamalar\u0131 test etmek i\u00e7in h\u0131zl\u0131 yinelemeye olanak sa\u011flar, \u00e7\u00fcnk\u00fc \u00e7oklu deneyler paralel olarak \u00e7al\u0131\u015fabilir ve ara\u015ft\u0131rmac\u0131lar testleri ger\u00e7ek zamanl\u0131 ve d\u00fc\u015f\u00fck maliyetle ekleyebilir, silebilir ve de\u011fi\u015ftirebilir. \u00d6rne\u011fin bir ara\u015ft\u0131rmac\u0131 bir dersin ders videolar\u0131n\u0131n farkl\u0131 \u00f6rneklerin kar\u015f\u0131la\u015ft\u0131rabilir (\u00f6r. bir konuya ait giri\u015fin ve kavramlara ait sunumlar\u0131n nas\u0131l oldu\u011funa ili\u015fkin \u00e7e\u015fitlili\u011fi) ve daha sonraki de\u011ferlendirmeler \u00fczerine ger\u00e7ekle\u015ftirilen performans\u0131 g\u00f6zlemleyebilir. Yeterli veri topland\u0131ktan sonra, ara\u015ft\u0131rmac\u0131 en d\u00fc\u015f\u00fck puanlarla ili\u015fkili ders s\u00fcr\u00fcmlerinden vazge\u00e7ebilir teoriye ve mevcut s\u00fcr\u00fcmlerin sonu\u00e7lar\u0131na dayanarak yeni s\u00fcr\u00fcmler ekleyebilir ve yinelemeye devam edebilir. Bu s\u00fcre\u00e7te ara\u015ft\u0131rmac\u0131 belli bir \u00f6rnek \u00e7al\u0131\u015fman\u0131n belirli bir \u00f6\u011frenen grubu i\u00e7in \u00f6rne\u011fin daha az e\u011fitimli \u00f6\u011frenenlere en iyi sonucu verdi\u011fini g\u00f6rebilir. Bu durum yeni teorik bilgiler sa\u011flayabilir ve \u00f6\u011frenmeyi iyile\u015ftirmek i\u00e7in i\u00e7eri\u011fin uyarlamal\u0131 sunumunu gerektirir. Bireysel farkl\u0131l\u0131klar\u0131n ke\u015ffi ve i\u00e7eri\u011fin duyarl\u0131 bir \u015fekilde uyarlanmas\u0131, KA\u00c7D’lerde oldu\u011fu gibi b\u00fcy\u00fck heterojen \u00f6\u011frenen \u00f6rnekleriyle dijital \u00f6\u011frenme ortamlar\u0131nda da m\u00fcmk\u00fcnd\u00fcr.<\/span><\/p>\n

    Bu b\u00f6l\u00fcm\u00fcn amac\u0131, \u00f6\u011frenme analiti\u011fi alan\u0131n\u0131n geli\u015fimi \u0131\u015f\u0131\u011f\u0131nda, KA\u00c7D’lerin bu iki \u00f6zelli\u011fini ortaya koymak ve bu \u00f6zelliklerin \u00f6\u011frenme, \u00f6\u011fretme teorisi ve prati\u011finin nas\u0131l geli\u015ftirilebilece\u011fini tart\u0131\u015fmakt\u0131r. Bu b\u00f6l\u00fcme, KA\u00c7D inisiyatiflerinin ortaya \u00e7\u0131k\u0131\u015f\u0131 ve geli\u015fimi hakk\u0131nda k\u0131sa bir tarihsel bak\u0131\u015fla ba\u015fl\u0131yoruz. Ara\u015ft\u0131rma i\u00e7in b\u00fcy\u00fck verinin avantajlar\u0131n\u0131 ve farkl\u0131 \u00f6\u011frenen \u00f6rneklerini g\u00f6r\u00fc\u015f\u00fcyoruz ve bunlar aras\u0131ndaki ili\u015fkilerden yararlanmak i\u00e7in ortaya \u00e7\u0131kan \u00e7al\u0131\u015fmalar\u0131 g\u00f6zden ge\u00e7iriyoruz. Daha sonra, deney ve h\u0131zl\u0131 yineleme yoluyla ortaya \u00e7\u0131kan f\u0131rsatlar\u0131 ele al\u0131yoruz ve bunlar\u0131n bug\u00fcne kadar KA\u00c7D platformlar\u0131nda nas\u0131l kullan\u0131ld\u0131\u011f\u0131n\u0131 tart\u0131\u015f\u0131yoruz. Mevcut k\u0131s\u0131tlamalar\u0131n ve b\u00fcy\u00fck \u00f6l\u00e7ekli dijital \u00f6\u011frenme ortamlar\u0131n\u0131n sundu\u011fu f\u0131rsatlardan \u00e7ok daha etkin bir \u015fekilde yararlanman\u0131n yollar\u0131n\u0131 konu\u015farak bu b\u00f6l\u00fcm\u00fc sonland\u0131r\u0131yoruz.<\/span><\/p>\n

    KA\u00c7D’LER\u0130N D\u00dcN\u00dc VE BUG\u00dcN\u00dc<\/h2>\n

    KA\u00c7D’lerin geli\u015fimi, uzaktan e\u011fitim (\u00f6r. uyum okullar\u0131, radyo e\u011fitimi), a\u00e7\u0131k eri\u015fim \u00fcniversiteleri ve a\u00e7\u0131k e\u011fitim kaynaklar\u0131 (Simonson, Smaldino, Albright ve Zvacek d\u00e2hil) gibi e\u011fitime eri\u015fimi artt\u0131rma \u00e7abalar\u0131 gelene\u011fi ba\u011flam\u0131nda ger\u00e7ekle\u015fti., 2011). Bununla birlikte 2008 y\u0131l\u0131nda; New York Times’\u0131n “KA\u00c7D y\u0131l\u0131” ilan etti\u011fi 2012 ve George Siemens ve Stephen Downes ilk KA\u00c7D\u2019yi kolayla\u015ft\u0131rd\u0131\u011f\u0131 (Siemens, 2013), y\u0131l\u0131na kadar KA\u00c7D’yi neyin te\u015fkil etti\u011fi kavram\u0131 temelden de\u011fi\u015fikli\u011fe u\u011fram\u0131\u015ft\u0131r. (Pappano, 2012). Bu de\u011fi\u015fim, Siemens’in (2013), kat\u0131 bir kurs yap\u0131s\u0131n\u0131 uygulamadan kolektif bilgi yaratmay\u0131 vurgulayan ba\u011flant\u0131c\u0131 pedagojik modeline dayanan orijinal bKA\u00c7D’lerini daha sonra \u00e7o\u011funlukla de\u011ferlendirmeleri ve kat\u0131 bir ders yap\u0131s\u0131n\u0131 i\u00e7eren ders-tabanl\u0131 \u00f6\u011fretim modeline dayanan gKA\u00c7D’lerden (yani 2012 ve sonras\u0131 KA\u00c7D’lerden) ayr\u0131lmas\u0131n\u0131 sa\u011flam\u0131\u015ft\u0131r. Stanford \u00dcniversitesi Profes\u00f6rleri Sebastian Thrun, Daphne Koller ve KA\u00c7D’leri ders s\u0131n\u0131flar\u0131n\u0131n daha geni\u015f bir izleyici kitlesine ula\u015fmalar\u0131 i\u00e7in dijital y\u00fckseltmeleri olarak yeniden tasarlad\u0131klar\u0131n\u0131 belirten Andrew Ng, bu ideolojik kaymaya yol a\u00e7t\u0131. Bu vizyon, ba\u015fta Coursera, Udacity, EdX ve FutureLearn olmak \u00fczere, \u00e7e\u015fitli kurumsal ve k\u00e2r amac\u0131 g\u00fctmeyen KA\u00c7D sa\u011flayan kurulu\u015flar\u0131n olu\u015fmas\u0131n\u0131 sa\u011flam\u0131\u015ft\u0131r. D\u00fcnya \u00e7ap\u0131ndaki y\u00fcksek\u00f6\u011fretim kurumlar\u0131, her biri on binlerce \u00f6\u011freneni \u00e7eken, artan say\u0131da derse katk\u0131da bulunmak i\u00e7in \u00e7aba harcad\u0131 (Waldrop, 2013).<\/span><\/p>\n

    \u0130lk heyecan ve ivme, KA\u00c7D’lerin evrensel d\u00fc\u015f\u00fck maliyetli y\u00fcksek\u00f6\u011frenim sa\u011flama vaadi yerine getirmekten mahrum kald\u0131\u011f\u0131 ortaya \u00e7\u0131kt\u0131\u011f\u0131nda s\u00f6nmeye ba\u015flad\u0131. \u0130lk \u00e7arp\u0131c\u0131 kan\u0131t, bir kursa ba\u015flayan \u00f6\u011frenenlerin sadece k\u00fc\u00e7\u00fck bir y\u00fczdesinin dersi tamamlamaya gayret etmesiydi (Clow, 2013; Breslow vd., 2013) ve tamamlama herkesin hedefi olmasa da (K\u0131z\u0131lcec ve Schneider, 2015; K\u0131z\u0131lcec, Piech ve Schneider, 2013), bu \u00f6r\u00fcnt\u00fc kritik engellerin a\u015f\u0131lmadan kald\u0131\u011f\u0131n\u0131 g\u00f6stermektedir. \u0130kinci \u00fcz\u00fcc\u00fc fark\u0131ndal\u0131k, tarihsel olarak yoksul kitleler i\u00e7in eri\u015fimi geli\u015ftirme vaadiyle ilgiliydi. Bir\u00e7ok KA\u00c7D \u00f6\u011frenenleri zaten olduk\u00e7a e\u011fitimlidir (Emanuel, 2013). Ayr\u0131ca, Amerika Birle\u015fik Devletleri’ndeki \u00f6\u011frenenler daha zengin b\u00f6lgelerde ya\u015fama e\u011filimindedir ve daha fazla sosyoekonomik kaynaklara sahip bireylerin sertifika kazanma olas\u0131l\u0131klar\u0131 daha y\u00fcksektir (Hansen ve Reich, 2015). Di\u011fer kan\u0131tlar, KA\u00c7D’lerdeki sosyoekonomik ba\u015far\u0131 bo\u015fluklar\u0131n\u0131n d\u00fcnya genelinde e\u011fitim seviyeleri ve ulusal geli\u015fim seviyeleri (Kizilcec, Saltarelli, Reich ve Cohen, 2017) ve ayr\u0131ca kad\u0131nlar\u0131n erkeklere g\u00f6re daha d\u00fc\u015f\u00fck performans sergiledi\u011fini g\u00f6stermektedir (K\u0131z\u0131lcec ve Halawa, 2015). Bu \u00f6r\u00fcnt\u00fcler k\u0131smen yap\u0131sal, k\u00fclt\u00fcrel ve e\u011fitsel engellerden kaynaklanabilir (\u00f6r. \u0130nternet eri\u015fimi, \u00f6nceki bilgiler, dil becerileri, k\u00fclt\u00fcre \u00f6zg\u00fc \u00f6\u011fretim y\u00f6ntemleri). Ek olarak, \u00f6\u011frenenler sosyal gruplar\u0131 nedeniyle (yani sosyal kimlik tehdidi nedeniyle) daha az yetenekli olarak g\u00f6r\u00fclme korkusu ve se\u00e7kin Bat\u0131 kurumlar\u0131ndan KA\u00c7D’lere ait olmalar\u0131ndan emin olmad\u0131klar\u0131 gibi sosyal psikolojik engellerle de kar\u015f\u0131la\u015fabilirler (K\u0131z\u0131lcec vd., 2017). Steele, Spencer ve Joshua, 2002; Walton ve Cohen, 2007). En az\u0131ndan tamamlanma oranlar\u0131 a\u00e7\u0131s\u0131ndan, Kuzey Amerika KA\u00c7D’leri orant\u0131s\u0131z bir \u015fekilde daha ayr\u0131cal\u0131kl\u0131 \u00f6\u011frenenlere fayda sa\u011flad\u0131 ve e\u011fitim hakk\u0131n\u0131 geli\u015ftirmek i\u00e7in tasarlanan bir teknoloji i\u00e7in kritik bir zorluk te\u015fkil etti. Bu teknolojinin tamamlay\u0131c\u0131 g\u00fcc\u00fcn\u00fc vurgulamaktad\u0131r; yani yeni teknolojilerin \u00f6nemi ile ilgili etkin tedbirler al\u0131nmad\u0131k\u00e7a bu durum mevcut e\u015fitsizliklerin yans\u0131mas\u0131na neden olacakt\u0131r. Asl\u0131nda haks\u0131z pop\u00fclasyon i\u00e7in verilen deste\u011fe ili\u015fkin yetersizli\u011fin kan\u0131t\u0131 ortaya \u00e7\u0131kt\u0131k\u00e7a platform sa\u011flay\u0131c\u0131lar\u0131 ba\u015flang\u0131\u00e7ta dikkatlerini se\u00e7kin ABD ortaklar\u0131na yo\u011funla\u015ft\u0131rd\u0131lar ve ard\u0131ndan uluslararas\u0131 \u00fcniversite ortaklar\u0131, STK’lar ve yabanc\u0131 h\u00fck\u00fbmetler takip etti. \u00d6nceki KA\u00c7D platformlar\u0131, masa\u00fcst\u00fc tabanl\u0131 \u00f6\u011frenme deneyimleri sa\u011flamaya odaklan\u0131rken, platform geli\u015ftirme \u00e7abalar\u0131, mobil \u0130nternetin yayg\u0131n oldu\u011fu geli\u015fmekte olan \u00fclkelerde eri\u015fimi art\u0131rman\u0131n bir yolu olarak mobil cihazlara y\u00f6nelik deste\u011fin geni\u015fletilmesine de y\u00f6nelmi\u015ftir.<\/span><\/p>\n

    KA\u00c7D \u00f6\u011frenme etkinlikleri i\u00e7in akreditasyon ve belgelendirme konusu, KA\u00c7D ak\u0131m\u0131 olgunla\u015ft\u0131k\u00e7a s\u00fcrekli de\u011ferlendirilmektedir. \u0130\u00e7erik ba\u015flang\u0131\u00e7ta \u00fccretsiz olarak sunulurken bir sertifikan\u0131n \u00f6nemi (ve bireyin kimli\u011finin ders etkinlikleriyle daha yak\u0131ndan ba\u011flant\u0131l\u0131 olan \u201conaylanm\u0131\u015f bir sertifika\u201d n\u0131n \u00f6nemi) i\u00e7eri\u011fe eri\u015fmek i\u00e7in \u00f6deme yapmak isteyen ya da sonunda bir sertifika almak isteyen \u00f6\u011frenenlerin ilgisini \u00e7ekmi\u015ftir. Sertifika yeterlilik belgesi de zamanla geli\u015fmi\u015ftir. Baz\u0131 kurumlar akademik kurumlardan ba\u011f\u0131ms\u0131z dereceler sunar (\u00f6r. Udacity’nin Nanodegrees uygulamas\u0131); KA\u00c7D’leri, liberal sanat kurslar\u0131n\u0131 \u00e7evrimi\u00e7i olarak tamamlamak i\u00e7in bir ge\u00e7it f\u0131rsat\u0131 olarak kullan\u0131r (\u00f6r. Arizona Devlet \u00dcniversitesi ve EdX Freshman Academy ortakl\u0131\u011f\u0131); bu lisans\u0131 geleneksel bir lisans derecesine ge\u00e7i\u015f yolu olarak kullanma se\u00e7ene\u011fi ile kompakt \u00e7evrimi\u00e7i lisans\u00fcst\u00fc programlar (\u00f6r. MIT Microdegrees) olu\u015fturur ve \u00e7evrimi\u00e7i olarak tam lisans\u00fcst\u00fc programlar sunarlar (\u00f6r. Illinois \u00dcniversitesi\u2019nin \u0130MBA ve Coursera platformundaki Veri Bilimi programlar\u0131). \u00d6zellikle veri bilimi gibi pop\u00fcler konularda, \u00e7e\u015fitli kurumlar\u0131n artan say\u0131da kurs ve k\u0131sa program\u0131 vard\u0131r. \u0130\u015fverenleri ve e\u011fitim kurumlar\u0131n\u0131 daha iyi ba\u011flayan daha verimli pazarlar geli\u015ftik\u00e7e, \u00f6\u011frenenleri derslerine \u00e7ekmek ve e\u011fitim alan\u0131nda \u00fcst\u00fcn i\u015fyeri performans\u0131 ve kariyer f\u0131rsatlar\u0131 g\u00f6stermelerini sa\u011flayacak kurslar sunmak amac\u0131yla kurumlar aras\u0131ndaki rekabetin artaca\u011f\u0131n\u0131 umuyoruz.<\/span><\/p>\n

    E\u011e\u0130T\u0130MDE \u00c7E\u015e\u0130TL\u0130 T\u00dcRDEK\u0130 B\u00dcY\u00dcK VER\u0130LERLE ZENG\u0130NLE\u015eT\u0130RME TEOR\u0130S\u0130 VE UYGULAMASI<\/h2>\n

    \u00d6\u011frenme ve \u00f6\u011fretme kuramlar\u0131 karma\u015f\u0131k bir sistemin b\u00f6l\u00fcmlerini tan\u0131mlar (Mitchell, 2009). Bu nedenle bir \u00f6\u011fretim y\u00f6ntemini veya bir \u00f6\u011frenme stratejisini inceleyen herhangi bir ara\u015ft\u0131rma; \u00f6rne\u011fin kat\u0131l\u0131mc\u0131lar\u0131n \u00f6nceki bilgileri veya konu alan\u0131 gibi kendi ba\u011flam\u0131 ile s\u0131n\u0131rl\u0131d\u0131r. Y\u00fczlerce potansiyel ba\u011flamsal nitelikten hangisinin belirli bir durumda \u00f6nemli oldu\u011funu tahmin etmek zordur ve denemek m\u00fcmk\u00fcn de\u011fildir. Bu nedenle bu karma\u015f\u0131kl\u0131\u011f\u0131 s\u0131n\u0131rlamak ve \u00f6nemli olan de\u011fi\u015fkenleri tan\u0131mlamak i\u00e7in bilimsel teoriye g\u00fcveniriz (Koedinger, Booth ve Klahr, 2013). Bununla birlikte e\u011fitim teorisi asla nihai ya da hepsini kapsay\u0131c\u0131 de\u011fildir. E\u011fitimdeki deneysel ara\u015ft\u0131rmalar ve genel olarak sosyal bilimler, d\u0131\u015f ge\u00e7erlik pahas\u0131na karma\u015f\u0131kl\u0131\u011f\u0131 azaltmak i\u00e7in belirli ba\u011flamlara odaklanma e\u011filimindedir. \u00d6zellikle, sosyal bilimlerde yap\u0131lan deneysel ara\u015ft\u0131rmalar, psikoloji laboratuvar\u0131 \u00e7al\u0131\u015fmalar\u0131na kat\u0131lan ABD’li \u00fcniversite \u00f6\u011frencileri gibi BESZD ba\u011flam\u0131ndaki insanlar\u0131n \u00e7al\u0131\u015fmalar\u0131na dayanmaktad\u0131r (Henrich, Heine ve Norenzayan, 2010). Bu mevcut sonu\u00e7lar\u0131n ve modellerin farkl\u0131 ba\u011flamlara ve kitlelere genellenebilirli\u011fi hakk\u0131nda sorular\u0131 g\u00fcndeme getirmektedir. Bu kayg\u0131lar ayn\u0131 zamanda \u00f6zellikle teknoloji ile g\u00fc\u00e7lendirilmi\u015f e\u011fitim ara\u015ft\u0131rmalar\u0131yla ilgili olarak g\u00fcndeme gelmi\u015ftir(Ocumpaugh, Baker, Gowda, Heffernan ve Heffernan, 2014; Blanchard, 2012). Bu zorlu\u011fun \u00fcstesinden gelmek i\u00e7in ara\u015ft\u0131rmac\u0131lar\u0131n geleneksel olarak elde edilenden daha b\u00fcy\u00fck ve daha \u00e7e\u015fitli olan \u00f6\u011frenen \u00f6rneklerine eri\u015fmeleri gerekir. Bu t\u00fcr \u00e7e\u015fitli \u00f6\u011frenen \u00f6rnekleri, KA\u00c7D’lerde yayg\u0131nd\u0131r.<\/span><\/p>\n

    KA\u00c7D platformlar\u0131nda mevcut olan derslerin tedariki ve \u00e7e\u015fitlili\u011fi, ilk sunumlar\u0131ndan bu yana d\u00fczenli bir \u015fekilde artm\u0131\u015ft\u0131r (Shah, 2015). Bu kurslar, \u00fcniversitelerin, m\u00fczelerin ve ulusal enstit\u00fclerinde d\u00e2hil oldu\u011fu d\u00fcnyadaki kurumlar taraf\u0131ndan olu\u015fturulmu\u015ftur. 2016’n\u0131n ba\u015flar\u0131nda, Coursera d\u00fcnya \u00e7ap\u0131nda 18 milyon \u00f6\u011frenciye ula\u015ft\u0131\u011f\u0131n\u0131 a\u00e7\u0131klam\u0131\u015ft\u0131r1<\/a><\/sup>. \u00c7o\u011fu \u00f6\u011frenen Amerika Birle\u015fik Devletleri, \u00c7in, Hindistan ve Brezilya’da bulunmaktad\u0131r ve kay\u0131tlar\u0131n \u00f6zellikle art\u0131\u015fta oldu\u011fu yerler Meksika, Kolombiya, Brezilya ve Rusya\u2019 d\u0131r. Ortalamada her on \u00f6\u011frenenden d\u00f6rd\u00fcn\u00fc kad\u0131nlar olu\u015fturmaktad\u0131r ancak cinsiyet oran\u0131 Nijerya’da %22’den Filipinler’de %55’e kadar de\u011fi\u015fkenlik g\u00f6stermektedir. Ayn\u0131 \u015fekilde, \u00e7e\u015fitli kurs konular\u0131na ilgi cinsiyete ve b\u00f6lgeye g\u00f6re de\u011fi\u015fiklik g\u00f6stermektedir: \u0130\u015f alan\u0131 ile ilgili kurslar Fransa’da \u00e7ok daha yayg\u0131nken Polonyal\u0131 \u00f6\u011frenenler en az toplumsal cinsiyet dengesi (k\u00fcresel olarak) ile bilgisayar bilimleri konu alan\u0131n\u0131 tercih etmektedirler. Kurstaki \u00f6\u011frenenler iyi e\u011fitimli olma e\u011filimindedir: 2015 y\u0131l\u0131nda yap\u0131lan bir ara\u015ft\u0131rmaya g\u00f6re, yakla\u015f\u0131k %80’i lisans derecesini \u00e7oktan alm\u0131\u015ft\u0131 (Zhenghao vd., 2015). Bu \u00f6r\u00fcnt\u00fc, 2016 y\u0131l\u0131nda 3 milyon \u00f6\u011frenen taraf\u0131ndan kullan\u0131lan, \u0130ngiltere merkezli bir KA\u00c7D platformu olan FutureLearn’\u00fcnkine benziyor: %732<\/a><\/sup> lisans derecesi ve Coursera’n\u0131n aksine %62’si kad\u0131nd\u0131r. Di\u011fer iki b\u00fcy\u00fck KA\u00c7D sa\u011flay\u0131c\u0131s\u0131 olan EdX3<\/a><\/sup> ve Udacity4<\/a><\/sup>, 2016 y\u0131l\u0131na kadar s\u0131ras\u0131yla alt\u0131 milyon ve iki milyon \u00f6\u011frenciye hizmet sunmu\u015ftur. Di\u011fer pek \u00e7ok kurum, KA\u00c7D’leri ya geleneksel \u00f6\u011frenme y\u00f6netim sistemleri (\u00f6r. Canvas Network), kurumsal olarak yerle\u015ftirilmi\u015f a\u00e7\u0131k kaynak platformlar\u0131 (\u00f6r. Open EdX) ya da \u00f6zel ya da \u00f6zel olarak geli\u015ftirilen platformlar arac\u0131l\u0131\u011f\u0131yla sunmaktad\u0131r. Bununla birlikte, Class Central taraf\u0131ndan toplanan verilere g\u00f6re (Shah, 2015), 550 kurum, d\u00fcnya \u00e7ap\u0131nda 35 milyondan fazla insan\u0131n dikkat \u00e7ekici \u015fekilde heterojen bir pop\u00fclasyona ula\u015fan neredeyse t\u00fcm disiplinleri kapsayan 4200 kurs olu\u015fturmu\u015ftur.<\/span><\/p>\n

    Bir KA\u00c7D i\u00e7erisinde toplanan veriler b\u00fcy\u00fck verilerin \u00fc\u00e7 \u00f6zelli\u011finden ikisi olan h\u0131z ve hacim bak\u0131m\u0131ndan y\u00fcksek olsa da (Laney, 2001), \u00e7e\u015fitlili\u011fi sa\u011flamak i\u00e7in \u00f6nlemler al\u0131nmad\u0131\u011f\u0131 s\u00fcrece, \u00e7e\u015fitlilik a\u00e7\u0131s\u0131ndan s\u0131n\u0131rl\u0131 kalabilir. Geleneksel e\u011fitim sistemleri hem ayr\u0131nt\u0131l\u0131 demografik bilgileri (\u00f6r. cinsiyet, etnik k\u00f6ken, sosyoekonomik stat\u00fc proxy’leri) hem de \u00f6nceki bilgi d\u00fczeyini (\u00f6r. \u00f6nceki okul kay\u0131tlar\u0131, lise notlar\u0131, standartla\u015ft\u0131r\u0131lm\u0131\u015f test puanlar\u0131) toplar. Bununla birlikte, bu de\u011fi\u015fkenler giri\u015f engelini azaltmak i\u00e7in KA\u00c7D’lerde otomatik olarak toplanmaz. B\u00f6ylece bir\u00e7ok KA\u00c7D sa\u011flayan kurum bu verileri iste\u011fe ba\u011fl\u0131 anketler yoluyla toplamaya ba\u015flam\u0131\u015ft\u0131r. Kursu tamamlama konusunda daha kararl\u0131 olma e\u011filiminde olan ve kendi tercihleriyle se\u00e7ilen bir grup \u00f6\u011frenen de bu anketleri tamamlama e\u011filimindedir. Reich (2014), kabaca kay\u0131tl\u0131 \u00f6\u011frenenlerin yakla\u015f\u0131k d\u00f6rtte birinin bir ders anketi doldurmas\u0131n\u0131 \u00f6nermektedir. Toplanan anket verilen cevaplar\u0131n toplam hacmi y\u00fcksek olsa da (\u00e7o\u011fu zaman onbinlerce), bu veriler genellikle daha fazla motive olmu\u015f \u00f6\u011frenenlerin \u00e7arp\u0131k bir \u00f6rne\u011fini temsil etti\u011fini hat\u0131rlamak \u00f6nemlidir. \u00d6\u011frenenlerin ge\u00e7mi\u015fine dair dikkat \u00e7ekmeden kapsaml\u0131 bilgi edinmek i\u00e7in mevcut k\u0131s\u0131tlamalar\u0131 a\u015fan veri toplama i\u00e7in geli\u015ftirilmi\u015f mekanizmalara ihtiya\u00e7 vard\u0131r. Bununla birlikte, \u015fu anda mevcut olan anket verileri KA\u00c7D \u00f6\u011frenenlerinin d\u00fcnyan\u0131n d\u00f6rt bir yan\u0131ndan nispeten farkl\u0131 yap\u0131da bir pop\u00fclasyon olu\u015fturdu\u011fu varsay\u0131m\u0131n\u0131 desteklemektedir.<\/span><\/p>\n

    Farkl\u0131 yap\u0131daki bir \u00f6\u011frenen pop\u00fclasyonuna eri\u015fim, e\u011fitim teorisi ve prati\u011fini ilerletmek i\u00e7in iki b\u00fcy\u00fck avantaj sa\u011flar. \u0130lk olarak, farkl\u0131 yap\u0131daki bir \u00f6rnek \u00fczerinde bir \u00f6\u011fretim y\u00f6ntemi veya analitik model de\u011ferlendirilirken, sonu\u00e7lar daha az temsil edilen gruplar i\u00e7in olumsuz \u00e7\u0131kar\u0131mlar\u0131 olan sonu\u00e7 \u00e7\u0131karma olas\u0131l\u0131\u011f\u0131n\u0131 azaltan \u00e7e\u015fitli \u00f6\u011frenen k\u00fcmelerini daha iyi temsil etmektedir. Farkl\u0131 yap\u0131lardaki \u00f6rneklerden elde edilen kan\u0131tlara dayanan teoriyi geni\u015fletmek, ayr\u0131ca farkl\u0131 ge\u00e7mi\u015flerden gelen \u00f6\u011frenenleri destekleyen daha kapsay\u0131c\u0131 ortamlar\u0131n geli\u015ftirilmesini de te\u015fvik eder. Farkl\u0131 yap\u0131lardaki \u00f6\u011frenen \u00f6rneklerinin ikinci b\u00fcy\u00fck avantaj\u0131, bireysel farkl\u0131l\u0131klar\u0131 ortaya \u00e7\u0131karabilmeleridir. \u00c7e\u015fitlilik, kurs materyallerinin ve \u00f6\u011fretim y\u00f6ntemlerinin etkili bir \u015fekilde uyarlanmas\u0131n\u0131 sa\u011flayan i\u00e7g\u00f6r\u00fcn\u00fcn ve kimin i\u00e7in neyin i\u015fe yarad\u0131\u011f\u0131n\u0131n anla\u015f\u0131lmas\u0131 i\u00e7in temel bir bile\u015fendir. As\u0131l \u00f6\u011frenenlerden hi\u00e7birine benzemeyen \u201cortalama \u00f6\u011frenen\u201d e g\u00f6re uyarlaman\u0131n \u00f6tesinde, iyile\u015ftirme i\u00e7in \u00f6nemli bir alan vard\u0131r (Rose, 2016). Asl\u0131nda, \u00f6\u011frenme bilimi insanlar\u0131, \u00f6nceki bilgiler, bili\u015fsel kontrol, zihinsel yetenek ve ki\u015filik d\u00e2hil olmak \u00fczere \u00f6\u011fretim y\u00f6ntemlerinin etkinli\u011fini etkileyen say\u0131s\u0131z de\u011fi\u015fken tan\u0131mlam\u0131\u015ft\u0131r (Jonassen ve Grabowski, 1993). \u00d6rne\u011fin, \u00f6n bilgi iyi belgelenmi\u015f bireysel bir farkt\u0131r (Ambrose, Bridges, DiPietro, Lovett ve Norman, 2010), \u00f6yle ki yeni ba\u015flayanlar i\u00e7in g\u00f6receli olarak etkili olan \u00f6\u011fretim y\u00f6ntemlerinin, alan bilgisini artt\u0131rmay\u0131 \u00f6\u011frenen ki\u015filer i\u00e7in uzmanl\u0131\u011f\u0131n tersine \u00e7evrilmesi olarak bilinen bir olgu i\u00e7in etkisiz, hatta verimsiz olabilmesi gibi (Kalyuga, Ayres, Chandler ve Sweller, 2003). Birlikte ele al\u0131nd\u0131\u011f\u0131nda, \u00e7e\u015fitli b\u00fcy\u00fck veriler ortalamalara g\u00f6re uyarlaman\u0131n \u00f6tesine ge\u00e7en daha kapsaml\u0131 bir bilimi ilerletebilir.<\/span><\/p>\n

    Ara\u015ft\u0131rmac\u0131lar say\u0131s\u0131z bireysel farkl\u0131l\u0131\u011f\u0131 incelemesine ra\u011fmen, e\u011fitim alan\u0131nda yinelenen \u00e7al\u0131\u015fmalar\u0131n\u0131n say\u0131s\u0131 azd\u0131r. Yinelenen \u00e7al\u0131\u015fmalar, 100 b\u00fcy\u00fck dergide yay\u0131nlanan makalelerin yaln\u0131zca %0,13’\u00fcn\u00fc olu\u015fturmaktad\u0131r (Makel ve Plucker, 2014) ve bu \u00e7al\u0131\u015fmalar\u0131n \u00e7o\u011fu BESZD \u00fclkelerindeki nispeten farkl\u0131 t\u00fcrdeki \u00f6\u011frenci pop\u00fclasyonuna dayanmaktad\u0131r. Farkl\u0131 \u00f6\u011frenme ba\u011flamlar\u0131ndaki bireysel \u00e7al\u0131\u015fmalar\u0131n \u00fcst analizini i\u00e7eren \u00e7al\u0131\u015fma \u00f6rnekleri \u00e7ok daha \u00e7e\u015fitli olsa bile \u00e7o\u011funun g\u00f6zlemlenmemi\u015f olmas\u0131 ve buna ba\u011fl\u0131 olarak dikkate al\u0131nmamas\u0131 \u00f6\u011fretim ko\u015fullar\u0131ndaki de\u011fi\u015fiklikleri anla\u015f\u0131lmaz k\u0131lacakt\u0131r (Ga\u0161evi\u0107, Dawson, Rogers ve Ga\u0161evi\u0107, 2016). KA\u00c7D’ler ve \u00e7evrimi\u00e7i \u00f6\u011frenme ortamlar\u0131 daha genel olarak bu acil sorunu ele almaya ba\u015flayabilir. Bu ortamlar, otantik \u00f6\u011frenme ba\u011flam\u0131nda farkl\u0131 \u00f6rneklerle geni\u015f \u00f6l\u00e7ekli \u00e7al\u0131\u015fmalar yapmak i\u00e7in \u00f6zellikle uygundur ve ayn\u0131 dersi tekrar kullanarak veya ayn\u0131 \u00e7al\u0131\u015fmay\u0131 ba\u015fka bir yere yerle\u015ftirerek bir KA\u00c7D’de tam bir \u00e7o\u011faltma \u00e7al\u0131\u015fmas\u0131n\u0131 y\u00fcr\u00fctmek b\u00fcy\u00fck \u00f6l\u00e7\u00fcde daha h\u0131zl\u0131 ve daha ucuzdur. Asl\u0131nda, KA\u00c7D’ler farkl\u0131 \u00f6\u011fretim gruplar\u0131 i\u00e7in hangi \u00f6\u011fretim yakla\u015f\u0131mlar\u0131n\u0131n daha etkili oldu\u011fu konusunda disiplin ara\u015ft\u0131rmalar\u0131 yapmak i\u00e7in \u00f6zellikle uygundur. \u00d6rne\u011fin, fizik e\u011fitiminde termodinami\u011fin ikinci yasas\u0131n\u0131 \u00f6\u011fretmeye y\u00f6nelik farkl\u0131 yakla\u015f\u0131mlar \u00f6nerilmi\u015f ve test edilmi\u015ftir (\u00f6r. Cochran ve Heron, 2006) ancak d\u00fcnyan\u0131n farkl\u0131 b\u00f6lgelerinden gelen \u00f6\u011frenenler i\u00e7in hangi yakla\u015f\u0131m\u0131n daha etkili oldu\u011fu belirsizdir.<\/span><\/p>\n

    KA\u00c7D’lerde \u00e7e\u015fitlili\u011fin kritik bir boyutu co\u011frafya ve dolay\u0131s\u0131yla k\u00fclt\u00fcrd\u00fcr. KA\u00c7D’ler, Bat\u0131 ve Do\u011fu \u00fclkelerinden gelen \u00f6\u011frenenleri, \u00f6\u011frenmenin farkl\u0131 k\u00fclt\u00fcrel temelleriyle birle\u015ftirir (Li, 2012). Do\u011fu \u00fclkelerinde (\u00f6r. \u00c7in, Japonya), \u00f6\u011frenme Konf\u00fc\u00e7y\u00fcs etkilerine dayanan erdemli, ya\u015fam boyu s\u00fcren bir kendi kendini m\u00fckemmelle\u015ftirme s\u00fcreci olarak g\u00f6r\u00fclme e\u011filimindedir, oysa Bat\u0131 \u00fclkelerinde (\u00f6r. ABD, Kanada), \u00f6\u011frenme Sokrat\u00e7\u0131 ve Baconcu etkilerine dayanarak \u00e7evremizdeki d\u00fcnyay\u0131 anlama hedefine hizmet eden bir soru\u015fturma \u015fekli olarak g\u00f6r\u00fcl\u00fcr. Ger\u00e7ekten de Bat\u0131 \u00fcniversitelerine kat\u0131lan Konf\u00fc\u00e7y\u00fcs\u00e7\u00fc Asya \u00f6\u011frencileri akademik bir uyum s\u00fcrecinden (Rienties ve Tempelaar, 2013) ge\u00e7iyor ve bu k\u00fclt\u00fcr \u015foku \u00f6\u011frenmeyi engelliyor ve yabanc\u0131la\u015fma duygular\u0131n\u0131 artt\u0131r\u0131yor (Zhou, Jindal \u2013 Snape, Topping ve Todman, 2008). Bu durum k\u00fclt\u00fcrel farkl\u0131l\u0131klar\u0131n ve bireysel farkl\u0131l\u0131klar\u0131n \u00f6\u011fretim yakla\u015f\u0131mlar\u0131n\u0131n etkinli\u011fi \u00fczerindeki potansiyel etkisini daha geni\u015f bir bi\u00e7imde vurgulamaktad\u0131r. KA\u00c7D’ler hem bireysel hem de grup d\u00fczeyinde, \u00f6\u011frenen \u00f6zelliklerinin anla\u015f\u0131lmam\u0131\u015f boyutlar\u0131n\u0131 de\u011ferlendirerek bireysel farkl\u0131l\u0131klar\u0131 ara\u015ft\u0131rmak ve mevcut teorileri geli\u015ftirmek i\u00e7in yeterince \u00e7e\u015fitli \u00f6\u011frenen \u00f6rnekleri sunar. Bireysel farkl\u0131l\u0131klara dair yeni g\u00f6r\u00fc\u015fler hem ki\u015fisel hem de \u00e7evrimi\u00e7i ortamlarda akademik d\u00fczenlemeyi ve uyarlanm\u0131\u015f \u00f6\u011frenme deneyimlerini destekleyen mevcut uygulamalar\u0131 bilgilendirebilir.<\/span><\/p>\n

    KA\u00c7D’lerde Farkl\u0131 B\u00fcy\u00fck Verilerden Yararlanan G\u00fcncel Ara\u015ft\u0131rma<\/h3>\n

    Ara\u015ft\u0131rmac\u0131lar, KA\u00c7D”lerden gelen heterojen \u00f6\u011frenen verilerinin b\u00fcy\u00fck potansiyelinden yararlanmaya yeni ba\u015fl\u0131yor. Son \u00e7al\u0131\u015fmalar, kurs gezintisi (Guo ve Reinecke, 2014), \u00f6\u011frenen motivasyonunun (K\u0131z\u0131lcec ve Schneider, 2015), s\u00fcreklili\u011fi ve ba\u015far\u0131s\u0131 (DeBoer, Stump, Seaton ve Breslow, 2013; K\u0131z\u0131lcec ve Halawa, 2015; K\u0131z\u0131lcec vd., 2013) ile Amerika Birle\u015fik Devletleri’nde (Hansen ve Reich, 2015) ve d\u00fcnya \u00e7ap\u0131ndaki \u00f6\u011frenenlerin kurs tamamlamalar\u0131nda sosyoekonomik farkl\u0131l\u0131klar\u0131 a\u00e7\u0131s\u0131ndan demografik ve co\u011frafi farkl\u0131l\u0131klar\u0131 ara\u015ft\u0131rm\u0131\u015ft\u0131r (K\u0131z\u0131lcec vd., 2017). \u00d6zellikle her ne kadar \u00f6\u011frenen demografik \u00f6zellikleri ders kazan\u0131mlar\u0131ndaki anlaml\u0131 ay\u0131r\u0131mlar\u0131 hesaba katsa da kestirimci modelleme ba\u011flam\u0131nda davran\u0131\u015fsal kay\u0131t g\u00fcnl\u00fc\u011f\u00fc verileri \u00fczerinde s\u0131n\u0131rl\u0131 iyile\u015ftirmeler sa\u011flar (Brooks, Thompson ve Teasley, 2015a; Brooks, Thompson ve Teasley, 2015b). KA\u00c7D’lerde \u00e7e\u015fitlili\u011fi art\u0131rmak i\u00e7in bir dizi olas\u0131 yakla\u015f\u0131mlar\u0131 vurgulayan alanyaz\u0131ndan iki \u00f6rne\u011fi k\u0131saca a\u00e7\u0131kl\u0131yoruz.<\/span><\/p>\n

    \u0130lk \u00f6nce, KA\u00c7D \u00f6\u011frenenlerin kat\u0131l\u0131m\u0131n\u0131 ve \u00f6\u011frenmesini geli\u015ftirmek i\u00e7in \u00e7e\u015fitlili\u011fi kullanmak \u00fczere yola \u00e7\u0131kan Kulkarni, Cambre, Kotturi, Bernstein ve Klemmer (2015) taraf\u0131ndan yap\u0131lan \u00e7al\u0131\u015fmalar\u0131 ele al\u0131yoruz. KA\u00c7D’lerdeki nispeten d\u00fc\u015f\u00fck sosyal etkile\u015fim seviyesini inovasyon ve ara\u015ft\u0131rma i\u00e7in bir f\u0131rsat olarak tan\u0131mlad\u0131lar. Bu yetersizli\u011fi gidermek i\u00e7in, \u00e7evrimi\u00e7i \u00f6\u011frenenleri gruptaki video sohbetleri arac\u0131l\u0131\u011f\u0131yla kurstaki di\u011fer ki\u015filerle ileti\u015fimde tutan bir akran tart\u0131\u015fma sistemi tasarlad\u0131lar. Grup kompozisyonunun performans de\u011ferlendirmesi \u00fczerindeki etkisini incelemek ve akran tart\u0131\u015fma sisteminin tasar\u0131m\u0131n\u0131 iyile\u015ftirmek i\u00e7in bir dizi deney yap\u0131lm\u0131\u015ft\u0131r. \u00dc\u00e7 deneyde \u00f6\u011frenenler, ka\u00e7 \u00fclkenin temsil edildi\u011fine g\u00f6re d\u00fc\u015f\u00fck co\u011frafi \u00e7e\u015fitlili\u011fi y\u00fcksek olan tart\u0131\u015fma gruplar\u0131na atand\u0131lar. Her deneyde b\u00f6l\u00fcm\u00fcn kavramsal olarak tamam\u0131n\u0131 kapsayan a\u00e7\u0131k u\u00e7lu bir soruyu, haftal\u0131k \u201cev \u00f6devi\u201d de\u011ferlendirmelerini ve final s\u0131nav\u0131n\u0131n puan\u0131n\u0131 ilgilendiren performans \u00f6l\u00e7\u00fcmlerinin farkl\u0131 sonu\u00e7lar\u0131 de\u011ferlendirildi. Yazarlar\u0131n hipotezini do\u011frulayan \u00e7ok \u00e7e\u015fitlilikteki akran tart\u0131\u015fmalar\u0131 performansta k\u0131sa vadeli iyile\u015fmeleri getirmi\u015ftir. Cinsiyet \u00e7e\u015fitlili\u011fi, \u00f6nceki \u00e7al\u0131\u015fmalar\u0131n tahmininin aksine, genel veya farkl\u0131 olarak \u00e7e\u015fitlilik durumuna g\u00f6re hi\u00e7bir etki g\u00f6stermemi\u015ftir (Woolley, Chabris, Pentland, Hashmi ve Malone, 2010). Ara\u015ft\u0131rmalar\u0131 e\u011fitsel bir varl\u0131k olarak \u00e7e\u015fitlili\u011fi art\u0131rman\u0131n umut verici bir yol oldu\u011funu g\u00f6stermektedir. Ayr\u0131ca bu yakla\u015f\u0131m\u0131n etkilili\u011fini etkileyebilecek bireysel farkl\u0131l\u0131klar\u0131 cinsiyet etkilerini test ederek ve \u00e7e\u015fitlili\u011fin farkl\u0131 yollarla \u00f6l\u00e7\u00fclebilir hale getirerek de\u011ferlendirmesini incelemeye ba\u015flamam\u0131\u015ft\u0131r.<\/span><\/p>\n

    Alanyaz\u0131ndan ikinci bir \u00f6rnek, \u00f6\u011fretim eleman\u0131n\u0131n ders videolar\u0131nda en uygun sunumu ile ilgilidir. \u0130nsan y\u00fcz\u00fc g\u00f6rmek dikkat etmeyi kolayla\u015ft\u0131rabilir ancak dikkat da\u011f\u0131t\u0131c\u0131 da olabilir. G\u00f6r\u00fcnt\u00fc ilkesi, \u00f6\u011fretenin bir videoda g\u00f6sterilmesinin \u00f6\u011frenme \u00e7\u0131kt\u0131lar\u0131n\u0131 etkilemedi\u011fini, \u00e7\u00fcnk\u00fc sosyal ipu\u00e7lar\u0131n\u0131n motivasyonel faydalar\u0131n\u0131n ilave yabanc\u0131 bili\u015fsel i\u015flemle dengelendi\u011fini g\u00f6stermektedir (Mayer, 2001). Bu bulgu, motivasyonun sebat ve ba\u015far\u0131n\u0131n kritik bir \u00f6nc\u00fcl\u00fc oldu\u011fu KA\u00c7D’lerin ba\u011flam\u0131na nas\u0131l \u00e7evrilebilir? K\u0131z\u0131lcec, Bailenson ve Gomez (2015), bir kursta KA\u00c7D \u00f6\u011frenenlerinin %35’inin y\u00fczleri \u00e7ok fazla rahats\u0131z edici bulduklar\u0131 i\u00e7in bir se\u00e7im yap\u0131ld\u0131\u011f\u0131nda, y\u00fcz\u00fc olmayan videolar\u0131 izlemeyi tercih etti\u011fini bulmu\u015ftur. Daha sonra KA\u00c7D’de yap\u0131lan rastgele sonu\u00e7lu bir deneyde, \u00f6\u011fretenin s\u00fcrekli g\u00f6sterilmesi nedeniyle dikkat da\u011f\u0131tan bir video ile \u00f6\u011fretenin g\u00f6sterilmedi\u011fi bir video t\u00fcr\u00fcn\u00fcn kar\u015f\u0131la\u015ft\u0131rmas\u0131 yap\u0131lm\u0131\u015ft\u0131r. Stratejik sunum alg\u0131lanan bili\u015fsel y\u00fck\u00fc ve sosyal varl\u0131\u011f\u0131 ortaya \u00e7\u0131karm\u0131\u015f olsa da s\u00fcreklilik ya da ders notlar\u0131 \u00fczerinde tam bir etkisi olmam\u0131\u015ft\u0131r. Bununla birlikte \u00f6\u011frenme tercihi (yani bireylerin resim ve \u015femalardan m\u0131 yoksa yaz\u0131l\u0131 ve s\u00f6zl\u00fc bilgilerden mi \u00f6\u011frenmeyi tercih edip etmedikleri) dikkate al\u0131nd\u0131\u011f\u0131nda s\u00fcreklilik konusunda \u00f6nemli bir bireysel fark vard\u0131: S\u00f6zel \u00f6\u011frenmeyi tercih eden \u00f6\u011frenenlerin s\u00fcrekli sunumlardan ziyade stratejik olanla kursu b\u0131rakma olas\u0131l\u0131\u011f\u0131 %46 daha fazlayd\u0131. Bu hem uygulamadaki bireysel farkl\u0131l\u0131klar\u0131n nedenini a\u00e7\u0131klamakta hem de mevcut teorilerin geli\u015ftirilmesinin \u00f6nemini g\u00f6stermektedir. E\u011fer sosyal ipu\u00e7lar\u0131 farkl\u0131 ki\u015filer i\u00e7in daha fazla dikkat da\u011f\u0131t\u0131c\u0131 ya da motive edici ise bu g\u00f6r\u00fc\u015f\u00fcn hedeflenen \u00f6\u011fretim tasar\u0131m\u0131 i\u00e7in \u00f6\u011frenen modellerine d\u00e2hil edilmesi yerinde olacakt\u0131r.<\/span><\/p>\n

    \u00c7EVR\u0130M\u0130\u00c7\u0130 ALAN DENEYLER\u0130N\u0130 KULLANARAK KURAMIN SINANMASI VE E\u011e\u0130T\u0130M UYGULAMALARININ DE\u011eERLEND\u0130R\u0130LMES\u0130<\/h2>\n

    Y\u00fcksek\u00f6\u011frenimde kullan\u0131lan geleneksel \u00f6\u011frenme y\u00f6netimi sistemlerine k\u0131yasla KA\u00c7D kurs tasar\u0131mc\u0131lar\u0131na ve neredeyse her yeni \u00f6zellik i\u00e7in s\u0131n\u0131rl\u0131 say\u0131da se\u00e7enek sunar. Bununla birlikte, KA\u00c7D’lerin arkas\u0131ndaki b\u00fcy\u00fck ve \u00e7e\u015fitli \u00f6\u011frenme toplulu\u011fu, deneysel ara\u015ft\u0131rmalar yoluyla \u00f6\u011frenme ve \u00f6\u011fretme hakk\u0131nda daha fazla bilgi edinmek i\u00e7in ola\u011fan\u00fcst\u00fc bir f\u0131rsat sunmaktad\u0131r. KA\u00c7D’lerle yap\u0131lan ilk ara\u015ft\u0131rmalar\u0131n \u00e7o\u011fu, varsay\u0131lan olarak toplanan kurs kay\u0131t g\u00fcnl\u00fc\u011f\u00fc verilerinin (\u00f6r. t\u0131klama ak\u0131\u015flar\u0131) analizine ve nispeten d\u00fc\u015f\u00fck cevap oranlar\u0131na sahip kurs anketlerinden elde edilen \u00f6z-raporlama \u00f6l\u00e7\u00fcmlerine odakland\u0131. KA\u00c7D’lerde \u00f6\u011fretene y\u00f6nelik deneme \u00f6zelliklerinin yak\u0131n zaman \u00f6nce bulunmas\u0131 ara\u015ft\u0131rmac\u0131lar\u0131n basit rastgele sonu\u00e7lu deneyler yapmalar\u0131n\u0131 sa\u011flam\u0131\u015ft\u0131r. Burada, birincisi etkile\u015fim ve \u00f6\u011frenmeyi te\u015fvik etmek i\u00e7in k\u00fc\u00e7\u00fck te\u015fvikler ile ilgilenen, ikincisi ders i\u00e7eri\u011finin ve yap\u0131s\u0131ndaki de\u011fi\u015fikliklerle ilgilenen, \u00fc\u00e7\u00fcnc\u00fcs\u00fc KA\u00c7D’lerin genel olaylar\u0131 incelemek ve ileriye y\u00f6nelik metodolojik d\u00fc\u015f\u00fcnceleri tart\u0131\u015fmak i\u00e7in bir laboratuvar g\u00f6revi g\u00f6rd\u00fc\u011f\u00fc- \u00fc\u00e7 deneysel ara\u015ft\u0131rma ak\u0131\u015f\u0131n\u0131 g\u00f6zden ge\u00e7iriyoruz.<\/span><\/p>\n

    KA\u00c7D’lerde Yay\u0131nlanm\u0131\u015f Deneysel Ara\u015ft\u0131rmalardan \u00dc\u00e7 Ak\u0131\u015f<\/h3>\n

    Bir deneysel ara\u015ft\u0131rma ak\u0131\u015f\u0131 ders sonu\u00e7lar\u0131n\u0131 geli\u015ftirmek i\u00e7in k\u00fc\u00e7\u00fck \u00f6zendirmelere veya hareketliliklere odaklanm\u0131\u015ft\u0131r. Bu t\u00fcr m\u00fcdahaleler \u00f6\u011frenenlerin farkl\u0131 mesajlar\u0131 \u00f6rne\u011fin elektronik posta arac\u0131l\u0131\u011f\u0131yla almalar\u0131 i\u00e7in rastgele atanmalar\u0131 ile y\u00fcr\u00fct\u00fclebilir. Tart\u0131\u015fma forumlar\u0131na kat\u0131l\u0131m\u0131 artt\u0131rmak bir dizi \u00e7al\u0131\u015fma A \/ B testleri kullanm\u0131\u015ft\u0131r. Lamb, Smilack, Ho ve Reich (2015) \u00fc\u00e7 i\u015fleyi\u015fi s\u0131nam\u0131\u015flard\u0131r (kendi kendine test kat\u0131l\u0131m kontrol\u00fc, \u00f6nceki tart\u0131\u015fmalar\u0131n \u00f6zetleriyle tart\u0131\u015fmalar\u0131n ba\u015flat\u0131lmas\u0131 ve yakla\u015fmakta olan tart\u0131\u015fma konular\u0131 hakk\u0131nda tart\u0131\u015fma \u00f6nizleme e-postalar\u0131) ve kat\u0131l\u0131m kontrol\u00fcn\u00fcn varsay\u0131lan kontrol ko\u015fulu \u00fczerinde forum etkinli\u011fini artt\u0131rd\u0131\u011f\u0131n\u0131 ke\u015ffetmi\u015flerdir. K\u0131z\u0131lcec, Schneider, Cohen ve McFarland (2014), iki deneyde forum kat\u0131l\u0131m\u0131 i\u00e7in e-posta kullan\u0131m\u0131na ait \u00f6zendiricili\u011fin \u00e7er\u00e7eveleme etkilerini s\u0131nam\u0131\u015flar ve i\u015fbirlikli bir \u00e7er\u00e7evenin (yani, “birlikte \u00f6\u011fren”, “birbirine yard\u0131m et”) bireycili\u011fe ya da n\u00f6tr \u00e7er\u00e7eveye g\u00f6re kat\u0131l\u0131m\u0131 azaltt\u0131\u011f\u0131n\u0131 tespit etmi\u015flerdir. Martinez (2014) \u00e7er\u00e7eveleme etkilerini sosyal bir kar\u015f\u0131la\u015ft\u0131rma paradigmas\u0131 kullanarak s\u0131nam\u0131\u015ft\u0131r (Festinger, 1954). \u00d6\u011frenenler, yukar\u0131 do\u011fru bir sosyal kar\u015f\u0131la\u015ft\u0131rma (ka\u00e7 \u00f6\u011frenenin seni geride b\u0131rakt\u0131\u011f\u0131n\u0131 anlat\u0131r), a\u015fa\u011f\u0131 do\u011fru sosyal bir kar\u015f\u0131la\u015ft\u0131rma (ka\u00e7 ki\u015finin daha k\u00f6t\u00fc performans g\u00f6sterdi\u011fini a\u00e7\u0131klar) veya herhangi bir sosyal kar\u015f\u0131la\u015ft\u0131rmay\u0131 i\u00e7ermeyen bir kontrol mesaj\u0131 i\u00e7eren bir e-posta alm\u0131\u015flard\u0131. A\u015fa\u011f\u0131 y\u00f6nl\u00fc kar\u015f\u0131la\u015ft\u0131rma y\u00fcksek performansl\u0131 \u00f6\u011frenenleri motive ederken, zorlanan \u00f6\u011frenenler yukar\u0131 y\u00f6nl\u00fc kar\u015f\u0131la\u015ft\u0131rmadan yararlanm\u0131\u015ft\u0131r. Son olarak Renz, Hoffmann, Staubitz ve Meinel (2016), pop\u00fcler forum tart\u0131\u015fmalar\u0131n\u0131 ve cevaplanmayan sorular\u0131 sergileyen e-postalar\u0131n forum etkinli\u011fini artt\u0131rd\u0131\u011f\u0131n\u0131 ve g\u00f6r\u00fcnmeyen ders videolar\u0131 hakk\u0131ndaki hat\u0131rlat\u0131c\u0131 e-postalar\u0131n, di\u011fer hat\u0131rlat\u0131c\u0131larla kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda ders etkinli\u011fini (yani ders g\u00f6r\u00fcn\u00fcmlerini) artt\u0131rd\u0131\u011f\u0131n\u0131 ke\u015ffetmi\u015flerdir. Bununla birlikte e-posta m\u00fcdahalelerinin bir dezavantaj\u0131, ara\u015ft\u0131rmac\u0131lar\u0131n genel olarak e-postay\u0131 kimin a\u00e7t\u0131\u011f\u0131n\u0131 ve kimin i\u015fleme maruz kald\u0131\u011f\u0131n\u0131 g\u00f6zlemleyememesidir; bu i\u015flem etkisini tahmin etmede analitik bir zorlu\u011fa yol a\u00e7ar (bk. Lamb vd., 2015). Anket deneyleri, bir anketin i\u00e7ine yerle\u015ftirilen deneme ile bir alternatif sunar. Bir \u00e7al\u0131\u015fmada, \u00f6z y\u00f6netimli \u00f6\u011frenme hakk\u0131nda ipu\u00e7lar\u0131 ya da kurs konular\u0131 hakk\u0131nda bir kontrol mesaj\u0131 almak i\u00e7in rastgele atanan anket kat\u0131l\u0131mc\u0131lar\u0131 vard\u0131 ancak kurs sonu\u00e7lar\u0131nda hi\u00e7bir iyile\u015fme bulamad\u0131lar (K\u0131z\u0131lcec, Perez \u2013 Sanagustin ve Maldonado, 2016). \u0130ste\u011fe ba\u011fl\u0131 anketlerdeki deneylerin olas\u0131 bir dezavantaj\u0131 ankete kat\u0131lmay\u0131 se\u00e7enlerden i\u015fleyi\u015fe farkl\u0131 cevap verebilecek daha kararl\u0131 \u00f6\u011frenenlerin \u00f6rneklem olu\u015fturma e\u011filimidir. Genel olarak, k\u00fc\u00e7\u00fck d\u00fcrtmeler insan davran\u0131\u015flar\u0131 \u00fczerinde \u015fa\u015f\u0131rt\u0131c\u0131 derecede b\u00fcy\u00fck etkilere sahip olsa da (Thaler ve Sunstein, 2009), KA\u00c7D’lerde yap\u0131lan \u00e7o\u011fu deney k\u00fc\u00e7\u00fck veya anlaml\u0131 olmayan sonu\u00e7lar vermi\u015ftir.<\/span><\/p>\n

    Ba\u015fka bir deneysel ara\u015ft\u0131rma ak\u0131\u015f\u0131, ders i\u00e7eri\u011fi ve ders yap\u0131s\u0131ndaki teoriye dayal\u0131 de\u011fi\u015fiklikleri incelemi\u015ftir. Renz, Hoffmann, Staubitz ve Meinel (2016), \u00f6\u011frenenlere kurs yap\u0131s\u0131n\u0131 ve navigasyonunu (gezintisini) a\u00e7\u0131klayan etkile\u015fimli bir tur olan \u201cekleme5<\/a><\/sup>\u201d oturumunu sunman\u0131n etkisini de\u011ferlendirmi\u015flerdir ancak kurs kat\u0131l\u0131m\u0131nda bir d\u00fczelmeye ula\u015famam\u0131\u015flard\u0131r. Yar\u0131 deneysel bir yakla\u015f\u0131m\u0131 takiben, Mullaney ve Reich (2015), ayn\u0131 dersin ard\u0131\u015f\u0131k iki \u00f6rne\u011fini, materyalin hepsinin ayn\u0131 anda sunumu kar\u015f\u0131s\u0131nda \u015fa\u015f\u0131rt\u0131c\u0131 bir \u015fekilde farkl\u0131 i\u00e7erik yay\u0131n modelleriyle kar\u015f\u0131la\u015ft\u0131rm\u0131\u015flard\u0131r. Ayr\u0131ca kal\u0131c\u0131l\u0131k ve tamamlama oranlar\u0131 aras\u0131nda da anlaml\u0131 bir fark bulunmam\u0131\u015ft\u0131r. Davis, Chen, van der Zee, Hauff ve Houben (2016), belirlenmi\u015f iki \u00f6\u011frenme stratejisini kolayla\u015ft\u0131rmak i\u00e7in (geri alma prati\u011fi ve \u00e7al\u0131\u015fma planlamas\u0131), \u00f6\u011frencilerden i\u00e7eri\u011fi \u00f6zetlemelerini ve \u00f6nceden planlamalar\u0131n\u0131 isteyen haftal\u0131k yazma konular\u0131n\u0131 denemi\u015flerdir. Yine bir kez daha kurs s\u00fcreklili\u011fi ve tamamlanmas\u0131 konular\u0131nda bir geli\u015fme tespit edilmemi\u015ftir. Kizilcec ve meslekta\u015flar\u0131, (2015) \u00e7oklu ortam \u00f6\u011frenme kuram\u0131n\u0131 temel alarak, \u00f6\u011fretim eleman\u0131n\u0131n video derslerinde y\u00fczlerinin sunumunun y\u0131pratma ve ba\u015far\u0131 oranlar\u0131n\u0131 nas\u0131l etkiledi\u011fini ve daha \u00f6nce tarif edildi\u011fi gibi y\u0131pratma \u00fczerinde heterojen etkiler buldu\u011funu test etmi\u015ftir. Tart\u0131\u015fma forumlar\u0131 kapsam\u0131nda, Tomkin ve Charlevoix (2014), \u00f6\u011freten ileti\u015fiminin \u00e7e\u015fitli kurs \u00e7\u0131kt\u0131lar\u0131 \u00fczerindeki etkisini test etmi\u015ftir. \u00d6\u011fretenlerin forum sorular\u0131na cevap verdi\u011fi ve haftal\u0131k \u00f6zetler g\u00f6nderdi\u011fi y\u00fcksek t\u0131klanma durumlar\u0131, \u00f6\u011freten kat\u0131l\u0131m\u0131 olmayan d\u00fc\u015f\u00fck t\u0131klamal\u0131 durumla kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda memnuniyet, kal\u0131c\u0131l\u0131k veya tamamlanma oranlar\u0131n\u0131 iyile\u015ftirmemi\u015ftir. Coetzee, Fox, Hearst ve Hartmann (2014) tart\u0131\u015fma forumunda sayg\u0131nl\u0131k sisteminin benimsenmesinin etkisini de\u011ferlendirmi\u015fler ve cevap s\u00fcrelerini ve g\u00f6nderim ba\u015f\u0131na d\u00fc\u015fen cevap say\u0131lar\u0131n\u0131n art\u0131\u011f\u0131n\u0131 ancak notlar\u0131 ve devaml\u0131l\u0131\u011f\u0131 etkilemedi\u011fini tespit etmi\u015flerdir. Bir ba\u015fka \u00e7al\u0131\u015fma, farkl\u0131 sistemleri de\u011ferlendirmi\u015f ve rozetin ilerleyi\u015fini ve yakla\u015fmakta olan rozetleri vurgulayan bir forum rozet sisteminin forum etkinli\u011fini artt\u0131rd\u0131\u011f\u0131n\u0131 g\u00f6rm\u00fc\u015ft\u00fcr. (Anderson, Huttenlocher, Kleinberg ve Leskovec, 2014). Bu ara\u015ft\u0131rma ak\u0131\u015f\u0131ndaki \u00e7al\u0131\u015fmalar\u0131n \u00e7o\u011fu; daha g\u00fc\u00e7l\u00fc manip\u00fclasyonlar kullan\u0131lm\u0131\u015f olmas\u0131na ra\u011fmen, \u00f6\u011frenme \u00e7\u0131kt\u0131lar\u0131nda \u00f6nemli bir geli\u015fmeye ula\u015f\u0131lamam\u0131\u015ft\u0131r.<\/span><\/p>\n

    \u00dc\u00e7\u00fcnc\u00fc deneysel ara\u015ft\u0131rma ak\u0131\u015f\u0131, genel teorileri ger\u00e7ek d\u00fcnya ba\u011flam\u0131nda test etmek i\u00e7in KA\u00c7D’leri bir laboratuvar ortam\u0131 olarak kullan\u0131r. \u00d6rne\u011fin, \u00e7evrimi\u00e7i s\u0131n\u0131flardaki bilin\u00e7 d\u0131\u015f\u0131 \u00f6n yarg\u0131lar\u0131 test etmek i\u00e7in, Baker, Dee, Evans ve John (2015), 126 KA\u00c7D (tart\u0131\u015fma ba\u015f\u0131na sekiz, toplam 1.008 mesaj) ve rastgele atanan \u00f6\u011frenen adlar\u0131n\u0131 i\u00e7eren tart\u0131\u015fma forumlar\u0131na farkl\u0131 \u0131rk ve cinsiyetleri temsil eden rastgele \u00f6\u011frenci ismi atanm\u0131\u015f mesajlar yerle\u015ftirdiler. Ayr\u0131mc\u0131l\u0131\u011fa ili\u015fkin g\u00fc\u00e7l\u00fc kan\u0131t buldular: \u00d6\u011fretenlerin beyaz erkeklere \u00f6zg\u00fc isim kullananlara, Hint\u00e7e ve \u00c7ince isimler ile beyaz kad\u0131nlara \u00f6zg\u00fc isimler ta\u015f\u0131yan kullan\u0131c\u0131lara g\u00f6re daha fazla cevap yazd\u0131klar\u0131n\u0131 tespit ettiler. Ba\u015far\u0131ya y\u00f6nelik sosyal-psikolojik engellerin test edilmesinde K\u0131z\u0131lcec, Saltarelli, Reich ve Cohen (2017), kursa ait olmama hakk\u0131ndaki endi\u015feleri azaltmak i\u00e7in tasarlanan teoriye dayal\u0131 m\u00fcdahale faaliyetlerinin, \u00f6\u011frenenler aras\u0131ndaki k\u00fcresel ba\u015far\u0131 a\u00e7\u0131\u011f\u0131n\u0131 az geli\u015fmi\u015f \u00fclkelere kar\u015f\u0131 etkin bir \u015fekilde kapatabildi\u011fini ke\u015ffetmi\u015flerdir. Kulkarni vd. (2015) \u00e7e\u015fitlili\u011fin yararlar\u0131 \u00fczerine e\u015f video tart\u0131\u015fmalar\u0131nda co\u011frafi \u00e7e\u015fitlili\u011fin rol\u00fcn\u00fc s\u0131nam\u0131\u015flar ve daha \u00e7e\u015fitli bir grupta olman\u0131n sonraki test performans\u0131n\u0131 iyile\u015ftirdi\u011fi sonucuna varm\u0131\u015flard\u0131r. Akran de\u011ferlendirmesinde do\u011fal bir deneyden yararlanan Rogers ve Feller (2016), \u00f6rnek akran performans\u0131na maruz kalman\u0131n, motivasyonu ve beklenen ba\u015far\u0131y\u0131 zay\u0131flatan sosyal kar\u015f\u0131la\u015ft\u0131rmas\u0131 nedeniyle y\u0131pranmaya neden oldu\u011funu bulmu\u015ftur. Yine, akran de\u011ferlendirme ba\u011flam\u0131nda Kizilcec (2016), akran s\u0131n\u0131fland\u0131rma s\u00fcrecindeki \u015feffafl\u0131k seviyesinin (\u00f6r. notlar\u0131n nas\u0131l ayarland\u0131\u011f\u0131 ve hesapland\u0131\u011f\u0131n\u0131) \u00f6\u011frenenlerin akran s\u0131n\u0131fland\u0131rmada g\u00fcvenini nas\u0131l etkiledi\u011fini test etmi\u015ftir. Sonu\u00e7lar prosed\u00fcr\u00fcn tarafs\u0131z oldu\u011funu vurgulayan bir a\u00e7\u0131klaman\u0131n beklentilerden d\u00fc\u015f\u00fck bir not alan \u00f6\u011frenenler i\u00e7in g\u00fcvene kar\u015f\u0131 esnekli\u011fi art\u0131rabilece\u011fini g\u00f6stermektedir. Bu ara\u015ft\u0131rma ak\u0131\u015f\u0131ndaki \u00e7al\u0131\u015fmalar, KA\u00c7D’ler ba\u011flam\u0131nda farkl\u0131 olgulara odaklanmaktad\u0131r ve sonu\u00e7lar\u0131, zenginle\u015ftirici teori ve pratik i\u00e7in umut vaat etmektedir.<\/span><\/p>\n

    KA\u00c7D’lerde Randomize Alan Deneyleri i\u00e7in Metodolojik D\u00fc\u015f\u00fcnceler<\/h3>\n

    Yay\u0131nlanan ara\u015ft\u0131rmalarla ilgili bu incelemede, bir\u00e7ok deneyin \u00f6nemli sonu\u00e7lar \u00fcretmedi\u011fi g\u00f6ze \u00e7arpmaktad\u0131r. KA\u00c7D’lerin, pratik olarak \u00f6nemsiz farkl\u0131l\u0131klar\u0131 bile istatistiksel olarak anlaml\u0131 k\u0131lacak \u015fekilde nispeten b\u00fcy\u00fck \u00f6rneklem b\u00fcy\u00fckl\u00fckleri sunmas\u0131 g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, bu \u015fa\u015f\u0131rt\u0131c\u0131 olabilir6<\/a><\/sup>. Bununla birlikte, KA\u00c7D verileri, sonu\u00e7 \u00f6l\u00e7\u00fctlerinde (\u00f6r. kal\u0131c\u0131l\u0131k, dereceleri) \u00f6nemli farkl\u0131l\u0131klar g\u00f6stermektedir. \u0130statistiksel g\u00fc\u00e7, verilerde ger\u00e7ek bir etki saptama \u015fans\u0131, \u00f6rneklem b\u00fcy\u00fckl\u00fc\u011f\u00fc ile artarken veriler g\u00fcr\u00fclt\u00fcl\u00fc hale geldik\u00e7e azal\u0131r. Ara\u015ft\u0131rmac\u0131lar a\u00e7\u0131klanamayan varyans seviyesini dikkate almad\u0131klar\u0131nda \u00f6nemli bir bulgu vermeyen ve etkisi olmayan \u00e7al\u0131\u015fmalarla sonu\u00e7lanabilir. Yine de bu varyans; \u00f6rne\u011fin heterojen i\u015flem etkisini test ederek asl\u0131nda daha fazla inceleme yap\u0131lmas\u0131n\u0131 gerektiren bireysel farkl\u0131l\u0131klar\u0131n varl\u0131\u011f\u0131na i\u015faret edebilir. Genel olarak, KA\u00c7D’lerdeki deneyler de\u011ferlendirilirken ve raporlan\u0131rken, istatistiksel \u00f6nemlerine ek olarak i\u015flem etkisinin b\u00fcy\u00fckl\u00fc\u011f\u00fcne odaklan\u0131lmas\u0131 \u00f6nerilir. Ara\u015ft\u0131rmac\u0131lar a\u00e7\u0131k\u00e7a planlanan do\u011frulay\u0131c\u0131 hipotez testlerini anl\u0131k a\u00e7\u0131klay\u0131c\u0131 analizlerden a\u00e7\u0131k bir \u015fekilde ay\u0131rmal\u0131d\u0131r. KA\u00c7D verilerindeki muhtemel sonu\u00e7lar\u0131n ve de\u011fi\u015fken \u00f6nlemlerin devasa oran\u0131 g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, \u00e7oklu test, derinle\u015fme ara\u015ft\u0131rmas\u0131 ve ara\u015ft\u0131rmac\u0131 serbestlik derecelerinin sonucu olarak (Gelman ve Loken,2013) I tipi hata oran\u0131n\u0131n (sahte pozitif) art\u0131r\u0131lmas\u0131 tehlikesi vard\u0131r. Bu zorlu\u011fun \u00fcstesinden gelmek i\u00e7in, s\u0131k s\u0131k hipotez denemesi (\u00f6r. Kruschke, 2013) Bayes\u00e7i alternatiflerinin \u00e7o\u011falt\u0131lmas\u0131, \u00f6n kayd\u0131 ve kullan\u0131m\u0131, ileriye d\u00f6n\u00fck sa\u011flam bilimsel kan\u0131tlar\u0131n olu\u015fturulmas\u0131na yard\u0131mc\u0131 olabilir.<\/span><\/p>\n

    Sadece d\u00f6rt y\u0131ll\u0131k bir olgunun i\u00e7inde mevcut olmas\u0131na ra\u011fmen, KA\u00c7D’lerdeki rastgele sonu\u00e7lu deneyler, e\u011fitim ve ilgili disiplinlerde teoriye \u00f6nemli katk\u0131lar sa\u011flamaya haz\u0131rlanmaktad\u0131r. Ancak e\u011fitimin geli\u015ftirilmesi i\u00e7in \u00e7evrimi\u00e7i saha deneyleri vaadi geni\u015f \u00f6l\u00e7\u00fcde ger\u00e7ekle\u015ftirilmemi\u015ftir. Ger\u00e7ek zamanl\u0131 verilerin mevcudiyeti ve karma\u015f\u0131k paralel deneyler yapmak i\u00e7in gereken eri\u015fim seviyesi \u00fczerindeki s\u0131n\u0131rlar, \u00e7o\u011fu ara\u015ft\u0131rmac\u0131n\u0131n devam eden yeni kurslar\u0131n h\u0131z\u0131nda bir seferde yaln\u0131zca bir fikri test etti\u011fi anlam\u0131na gelir. KA\u00c7D’lerde yap\u0131lan deneylerle h\u0131zl\u0131 yinelemeye y\u00f6nelik kritik bir ad\u0131m, \u00f6\u011frenen pop\u00fclasyonuyla deney yaparak ikili \u00f6\u011frenme hedefini ba\u015farmak ve yinelemeli olarak daha iyi bir \u00f6\u011frenme deneyimi sa\u011flamak i\u00e7in uyarlamal\u0131 deney i\u00e7in zemin haz\u0131rlar. Bu disiplin \u00f6\u011fretim ve \u00f6\u011frenimde burs kazanmak i\u00e7in \u00f6zel bir f\u0131rsat sa\u011flayacakt\u0131r. \u00d6rne\u011fin, \u00f6zyineleme kavram\u0131n\u0131 \u00f6\u011fretenin yeni bir yolunu denemek ve test sonu\u00e7lar\u0131n\u0131 \u00f6nceki toplulukla kar\u015f\u0131la\u015ft\u0131rmak yerine, \u00f6zyineleme i\u00e7in birden fazla yakla\u015f\u0131m e\u015fzamanl\u0131 olarak \u00f6\u011fretilebilir ve ilgili etkinlikleri \u00e7abuk\u00e7a belirlenebilir. Bu bir alandaki \u00e7oklu e\u011fitim teorilerinin e\u015fzamanl\u0131 olarak denenmesini ve g\u00fcn\u00fcm\u00fczde ara\u015ft\u0131rmac\u0131 toplulu\u011funun ve \u00f6nemli kaynaklar\u0131n tamam\u0131 i\u00e7in gerekli heterojen etkileri inceleyerek kuram\u0131n ve uygulaman\u0131n geli\u015ftirilmesi s\u00fcrecini sa\u011flayacakt\u0131r. Williams ve meslekta\u015flar\u0131, (2014), KA\u00c7D’lerde, s\u00fcrmekte olan deneylerin sonu\u00e7lar\u0131na g\u00f6re uyarlanan k\u00fc\u00e7\u00fck i\u00e7erik par\u00e7alar\u0131 olan KA\u00c7D’lerde uyarlanabilir deneyler i\u00e7in ilk konsept \u00f6nerdiler. \u0130leriye d\u00f6n\u00fck, deneysel ko\u015fullara dinamik atama, \u00f6rne\u011fin \u00e7ok kollu bir haydut7<\/a><\/sup> algoritmas\u0131 kullanarak (Bather ve Gittins, 1990), \u00f6zellikle PlanOut gibi karma\u015f\u0131k ve paralel tasar\u0131mlar\u0131 destekleyen deneysel sistemle birlikte, kurs tasar\u0131mlar\u0131 \u00fczerinde h\u0131zl\u0131 yineleme yapabilir (Bakshy, Eckles ve Bernstein, 2014). Genel olarak, KA\u00c7D’lerdeki randomize saha deneyleri ara\u015ft\u0131rmac\u0131lara teori ve prati\u011fi h\u0131zl\u0131 bir \u015fekilde zenginle\u015ftirmek i\u00e7in yeni bir f\u0131rsat sunuyor.<\/span><\/p>\n

    SONU\u00c7<\/h2>\n

    Geni\u015f \u00e7apl\u0131 rastgele sonu\u00e7lu deneylerin h\u0131zl\u0131 bir \u015fekilde heterojen \u00f6\u011frenen pop\u00fclasyonuna uygulanabilirli\u011fini, y\u00fcr\u00fct\u00fclen e\u011fitsel ara\u015ft\u0131rma \u015fekillerinin bozulmas\u0131na neden olacak g\u00fcce sahiptir. Ge\u00e7mi\u015fte \u00f6\u011frenme teorileri, ko\u015fullar \u00fczerinde kontrol\u00fcn zor oldu\u011fu (\u00f6r. BESZD ba\u011flam\u0131ndaki y\u00fcksek\u00f6\u011frenim s\u0131n\u0131f \u00e7al\u0131\u015fmalar\u0131) az say\u0131da \u00e7ok se\u00e7ici ortam\u0131n dikkatli bir \u015fekilde incelenmesinden kaynaklan\u0131yor olsa da g\u00fcn\u00fcm\u00fczde d\u00fcnya \u00e7ap\u0131nda tek bir kursta on binlerce \u00f6\u011frenene y\u00fcksek kalitede rastgele sonu\u00e7lu deneyler uygulamak m\u00fcmk\u00fcnd\u00fcr ki bu alanda benzeri g\u00f6r\u00fclmemi\u015f bir f\u0131rsatt\u0131r.<\/span><\/p>\n

    Geleneksel y\u00fcksek\u00f6\u011frenim ara\u015ft\u0131rmalar\u0131, deneysel sorgulama i\u00e7in iki ana pratik k\u0131s\u0131tlama ile kar\u015f\u0131 kar\u015f\u0131ya kalm\u0131\u015ft\u0131r. Belki de en \u00f6nemli k\u0131s\u0131tlama, \u00f6\u011fretim sorumlulu\u011fu paradigmas\u0131d\u0131r. Y\u00fcksek\u00f6\u011frenim s\u0131n\u0131flar\u0131nda, dersi veren \u00f6\u011fretim \u00fcyesi \u00f6\u011frenci deneyiminden tamamen sorumlu olma e\u011filimindedir. B\u00f6ylece, fak\u00fclte, deneysel bir yakla\u015f\u0131m yerine e\u015fitlik odakl\u0131 bir yakla\u015f\u0131m benimsemekte ve s\u0131n\u0131ftaki t\u00fcm \u00f6\u011frencilerin destek ve m\u00fcdahalelere e\u015fit eri\u015fime sahip olmalar\u0131n\u0131 sa\u011flamaktad\u0131r. Bu \u015fartlarda yenilik, verilen bir topluluk veya \u00e7al\u0131\u015fma y\u0131l\u0131ndaki \u00f6\u011frenenlerin di\u011fer topluluklarla veya y\u0131llarca yap\u0131lan \u00e7al\u0131\u015fmalarla kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131, daha \u015fa\u015f\u0131rt\u0131c\u0131 de\u011fi\u015fkenler ortaya \u00e7\u0131karan yar\u0131 deneysel y\u00f6ntemlerle olma e\u011filimindedir. KA\u00c7D’lerde, paradigma, belki de \u00f6\u011frenenlerin ba\u015far\u0131s\u0131 i\u00e7in bir sorumluluk \u00fcstlenen kurumsal y\u00f6netim ve tedarik\u00e7i ortaklar\u0131 da d\u00e2hil olmak \u00fczere k\u0131smen daha geni\u015f akt\u00f6r toplulu\u011fu nedeniyle farkl\u0131d\u0131r. Bu akt\u00f6rlerden baz\u0131lar\u0131, \u00f6zellikle h\u0131zl\u0131 prototipleme ve test i\u015fleminin ilke oldu\u011fu risk sermayesi fonlu i\u015fletmelerde risk ve \u00f6d\u00fcl dengeleme etraf\u0131nda k\u00fclt\u00fcr, \u00f6\u011frenenin ba\u015far\u0131s\u0131n\u0131 ilerletmek i\u00e7in deneysel yakla\u015f\u0131mlara y\u00f6nelik daha olumlu tutumlar geli\u015ftirmi\u015ftir.<\/span><\/p>\n

    Geleneksel y\u00fcksek\u00f6\u011frenim ara\u015ft\u0131rmalar\u0131nda ikinci bir k\u0131s\u0131tlama, deney yoluyla sa\u011flanan kabul edilebilir risk ve \u00f6d\u00fcl miktar\u0131d\u0131r. Bir tarafta topluma de\u011ferini g\u00f6stermi\u015f y\u00fczlerce y\u0131ll\u0131k y\u00fcksek\u00f6\u011frenim varken ve di\u011fer taraftan belirli bir \u00f6\u011frencinin \u00f6\u011frenimi i\u00e7in de y\u00fcz binlerce dolar tehlike alt\u0131ndayken ara\u015ft\u0131rmac\u0131lar\u0131n y\u00fcksek riskli ara\u015ft\u0131rmalara kat\u0131lmalar\u0131 i\u00e7in etik arg\u00fcman olu\u015fturmalar\u0131 daha zordur. Yine de KA\u00c7D ortamlar\u0131nda, \u00f6\u011frenenlerin \u00e7o\u011fu \u00fccretsiz olarak kay\u0131t yapt\u0131rmaktad\u0131r ve \u00e7ok az\u0131, KA\u00c7D deneyinin sonu\u00e7lar\u0131yla ilgili ge\u00e7im kaynaklar\u0131n\u0131 kaybetme tehlikesi alt\u0131ndad\u0131r8<\/a><\/sup>. Bu fark kurumsal politikaya yans\u0131m\u0131\u015ft\u0131r. Pek \u00e7ok kurum, ABD’deki AEHMY gibi yasal zorunluluklar nedeniyle \u00f6\u011frenci kay\u0131tlar\u0131 ve mahremiyet i\u00e7in g\u00fc\u00e7l\u00fc korumalara sahiptir. Bununla birlikte, KA\u00c7D’lerdeki baz\u0131 k\u0131s\u0131tlamalar\u0131, \u00e7evrimi\u00e7i \u00f6\u011frenenlerle yap\u0131lan deneysel ara\u015ft\u0131rmalarla ilgili politikalardan kald\u0131ran \u00f6\u011frenenler i\u00e7in (veya \u201ckullan\u0131c\u0131lar\u201d) ayn\u0131 y\u00fck\u00fcml\u00fcl\u00fckler mevcut de\u011fildir. Bunun ara\u015ft\u0131rmac\u0131lar i\u00e7in iki \u00f6nemli etkisi ve f\u0131rsat\u0131 var:<\/span><\/p>\n

      \n
    1. \n

      KA\u00c7D \u00f6\u011frenenlerinin pop\u00fclasyonu farkl\u0131d\u0131r ve bir\u00e7ok y\u00f6nden, geleneksel demografik ara\u015ft\u0131rmalardan (\u00f6\u011frenen demografisi (ya\u015f, \u0131rk, k\u00fclt\u00fcrel ge\u00e7mi\u015f, vb.), \u00d6n bilgi ve ders alma motivasyonlar\u0131) bak\u0131m\u0131ndan \u00e7ok daha \u00e7e\u015fitlidir. Bu detayl\u0131 sunum, \u00e7ok say\u0131da \u00f6\u011frenenle birlikte, bilim insanlar\u0131na, \u00f6\u011frenme kuramlar\u0131n\u0131n pop\u00fclasyonlar aras\u0131nda genelle\u015ftirilebilirli\u011fini s\u0131nanmas\u0131n\u0131 ve belirli \u00f6\u011frenen gruplar\u0131na y\u00f6nelik en uygun kimlik \u00f6\u011frenme teorileri i\u00e7in bir f\u0131rsat sa\u011flar. Bu b\u00f6yle b\u00fcy\u00fck veri k\u00fcmeleri gerektiren sorunlara niceliksel yakla\u015f\u0131mlar sa\u011flayabilir; \u00f6rne\u011fin, Dillahunt, Ng, Fiesta ve Wang\u2019\u0131n (2016) KA\u00c7D’leri sosyal hareketlilik i\u00e7in kullanan d\u00fc\u015f\u00fck gelirli topluluklarla ilgili ara\u015ft\u0131rmalar\u0131, KA\u00c7D\u2019lerde kay\u0131tl\u0131 olan \u00f6\u011frenenlerin say\u0131s\u0131 olmasayd\u0131, nicel olarak \u00e7al\u0131\u015fmak zor olacakt\u0131.<\/span><\/p>\n<\/li>\n

    2. \n

      Ara\u015ft\u0131rmac\u0131lar\u0131n \u00f6\u011frenme platformunda do\u011frudan \u00fccretsiz deney yapabilmeleri \u00f6\u011frenen verilerinin hacmini ve varyans\u0131n\u0131 daha fazla bilimsel etki i\u00e7in art\u0131rmalar\u0131n\u0131 sa\u011flar. Bu ara\u015ft\u0131rma, kuram ve uygulaman\u0131n b\u00fct\u00fcnle\u015ftirilmesini te\u015fvik ederek e\u011fitimdeki geri bildirim d\u00f6ng\u00fcs\u00fcn\u00fc bitirme f\u0131rsat\u0131 sunar. KA\u00c7D’lerdeki \u00f6\u011frenen pop\u00fclasyonunun geni\u015fli\u011fi ve derinli\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, deneyimine, di\u011fer \u00f6\u011frenenlerin deneyimlerine ve temeldeki platform verilerine dayanarak \u00f6\u011frenene (yar\u0131) otomatik olarak adapte olan ortamlar olu\u015fturmak i\u00e7in ger\u00e7ek bir olas\u0131l\u0131k vard\u0131r.<\/span><\/p>\n<\/li>\n<\/ol>\n

      Bu b\u00f6l\u00fcmde, daha kapsaml\u0131 ve \u00e7evik9<\/a><\/sup> bir \u00f6\u011frenme biliminin m\u00fcmk\u00fcn k\u0131laca\u011f\u0131na inand\u0131\u011f\u0131m\u0131z KA\u00c7D ara\u015ft\u0131rmalar\u0131n\u0131n iki kayna\u011f\u0131n\u0131 -\u00e7e\u015fitli b\u00fcy\u00fck verilerin mevcudiyeti ve h\u0131zl\u0131 yinelemeyle rastgele alan deneyleri yapma kabiliyetini- ele ald\u0131k. B\u00fcy\u00fck \u00f6l\u00e7\u00fcde kabul g\u00f6rmeleri ve bilgi i\u015flemsel y\u00f6ntemlerinin incelenmesi ile nitelenen \u00f6\u011frenme analiti\u011fi ve e\u011fitsel veri madencili\u011fi alanlar\u0131 bu d\u00fc\u015f\u00fcnceye cevap vermeye ve ilerleyen zamanlarda daha da geni\u015f bir etki yaratmaya haz\u0131rlard\u0131r.<\/span><\/p>\n

      KAYNAK\u00c7A<\/h2>\n

      Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How learning works: Seven research-based principles for smart teaching<\/i>. Jossey-Bass. <\/span><\/span><\/p>\n

      Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2014). Engaging with massive online courses. Proceedings of the 23rd International Conference on World Wide Web <\/i>(WWW\u201914), 7\u201311 April 2014, Seoul, Republic of Korea (pp. 687\u2013698). New York: ACM. <\/span><\/span><\/p>\n

      Baker, R., Dee, T., Evans, B., & John, J. (2015). Bias in online classes: Evidence from a field experiment. Paper presented at the SREE Spring 2015 Conference, Learning Curves: Creating and Sustaining Gains from Early Childhood through Adulthood<\/i>, 5\u20137 March 2015, Washington, DC, USA. <\/span><\/span><\/p>\n

      Bakshy, E., Eckles, D., & Bernstein, M. S. (2014). Designing and deploying online field experiments. Proceedings of the 23rd International Conference on World Wide Web <\/i>(WWW\u201914), 7\u201311 April 2014, Seoul, Republic of Korea (pp. 283\u2013292). New York: ACM. <\/span><\/span><\/p>\n

      Bather, J. A., & Gittins, J. C. (1990). Multi-armed bandit allocation indices. Journal of the Royal Statistical Society: Series A, 153<\/i>(2), 257. <\/span><\/span><\/p>\n

      Blanchard, E.G. (2012). On the WEIRD nature of ITS\/AIED conferences. In S. A. Cerri et al. (Eds.), Proceedings of the 11th International Conference on Intelligent Tutoring Systems <\/i>(ITS 2012), 14\u201318 June 2012, Chania, Greece (pp. 280\u2013285). Lecture Notes in Computer Science. Springer Berlin Heidelberg. <\/span><\/span><\/p>\n

      Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom research into edX\u2019s first MOOC. Research & Practice in Assessment, 8<\/i>. http:\/\/www.rpajournal.com\/dev\/wp-content\/uploads\/2013\/05\/SF2.pdf. <\/span><\/span><\/p>\n

      Brooks, C., Thompson, C., & Teasley, S. (2015a). A time series interaction analysis method for building predictive models of learners using log data. Proceedings of the 5th International Conference on Learning Analytics and Knowledge <\/i>(LAK\u201915), 16\u201320 March 2015, Poughkeepsie, NY, USA (pp. 126\u2013135). New York: ACM. <\/span><\/span><\/p>\n

      Brooks, C., Thompson, C., & Teasley, S. (2015b). Who you are or what you do: Comparing the predictive power of demographics vs. activity patterns in massive open online courses (MOOCs). Proceedings of the 2nd ACM Conference on Learning @ Scale <\/i>(L@S 2015), 14\u201318 March 2015, Vancouver, BC, Canada (pp. 245\u2013248). New York: ACM. <\/span><\/span><\/p>\n

      Clow, D. (2013). MOOCs and the funnel of participation. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge <\/i>(LAK\u201913), 8\u201312 April 2013, Leuven, Belgium. New York: ACM. doi:10.1145\/2460296.2460332 <\/span><\/span><\/p>\n

      Cochran, M. J., & Heron, P. R. L. (2006). Development and assessment of research-based tutorials on heat engines and the second law of thermodynamics. American Journal of Physics, 74<\/i>(8), 734. <\/span><\/span><\/p>\n

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      1<\/a> https:\/\/blog.coursera.org\/post\/142363925112<\/span><\/span><\/p>\n<\/div>\n

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      2<\/a> https:\/\/about.futurelearn.com\/press-releases\/future-learnhas-3million-learners\/<\/span><\/span><\/p>\n<\/div>\n

      \n

      3<\/a> http:\/\/blog.edx.org\/edx-yearin-review?track=blog<\/span><\/span><\/p>\n<\/div>\n

      \n

      4<\/a> https:\/\/www.udacity.com\/success<\/span><\/span><\/p>\n<\/div>\n

      \n

      5<\/a> orj. onboarding<\/span><\/span><\/p>\n<\/div>\n

      \n

      6<\/a> Sosyal bilimlerde standart uygulama oldu\u011fu gibi, e\u011fitim ara\u015ft\u0131rmalar\u0131 alan\u0131 da deneysel sonu\u00e7lar\u0131n istatistiksel \u00f6nemini de\u011ferlendirmek i\u00e7in p <0.05 kriterini benimsemi\u015ftir. Bu nedenle, en az\u0131ndan s\u0131f\u0131r hipotezi do\u011fruyken, \u00f6rnek verilerde oldu\u011fu kadar u\u00e7 bir etki elde etme \u015fans\u0131n\u0131n %5’ten daha az olmas\u0131 durumunda, bo\u015f\/s\u0131f\u0131r (\u00f6r. e\u015fit ko\u015fullu ortalamalar) reddedilir..<\/span><\/span><\/p>\n<\/div>\n

      \n

      7<\/a> \u00c7evirenin notu: olas\u0131l\u0131k teorisinde kullan\u0131lan ve \u00e7ok kollu haydut olarak \u00e7evirdi\u011fimiz multi\u2013armed bandit ifadesi tek kollu kumar makinelerinden attfen alan yaz\u0131nda kullan\u0131lmaktad\u0131r.<\/span><\/span><\/p>\n<\/div>\n

      \n

      8<\/a> KA\u00c7D kimlik bilgilerinin Arizona Devlet \u00dcniversitesi Global Freshman Akademisi ve MIT Mikro-Master programlar\u0131 gibi y\u00fcksek\u00f6\u011fretimde kredi olarak kabul edilmesine y\u00f6nelik son yakla\u015f\u0131mlar, \u00f6\u011frenenlerin ilgi ve y\u00f6nelimlerini de\u011fi\u015ftirmeye ba\u015flam\u0131\u015ft\u0131r.<\/span><\/span><\/p>\n<\/div>\n

      \n

      9<\/a> orj. agile<\/span><\/span><\/p>\n<\/div>\n","protected":false},"author":1,"menu_order":2,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[48],"contributor":[],"license":[],"class_list":["post-77","chapter","type-chapter","status-publish","hentry","chapter-type-numberless"],"part":73,"_links":{"self":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/chapters\/77","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\/77\/revisions"}],"part":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/parts\/73"}],"metadata":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/chapters\/77\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/wp\/v2\/media?parent=77"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/pressbooks\/v2\/chapter-type?post=77"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/wp\/v2\/contributor?post=77"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/acikkitap.com.tr\/oaek\/wp-json\/wp\/v2\/license?post=77"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}