Eğitsel Veri Madenciliği ve Öğrenme Analitikleri: Dünü, Bugünü ve Geleceği

Eğitsel veri madenciliği ve öğrenme analitikleri son zamanlarda e-öğrenme ortamlarının daha etkili hale getirilmesi amacıyla kullanılan iki önemli alan olarak karşımıza çıkmaktadır. Bu araştırmanın amacı, öncelikle her iki çalışma alanı arasındaki farklılıkları ortaya koymak ve diğer taraftan bu kavramlara ilişkin değişimleri tarihsel gelişimleri içerisinde değerlendirmektir. Eğitsel veri madenciliği büyük veri içerisindeki örüntülerin keşfedilmesini ifade etmekte iken, öğrenme analitikleri elde edilen bu örüntülerin e-öğrenme ortamlarının iyileştirilmesi için işe koşulmasıdır. Eğitsel veri madenciliği veri tabanında bilgi keşfi süreçleri ile ortaya koyulmaya başlamışken, öğrenme analitikleri ise özellikle 2011 yılında bu veri tabanlarından elde edilen örüntülerin işe koşulması olarak araştırmalardaki yerini almıştır. Araştırmanın amaçlarından bir tanesi ise bu kavramların gelecekteki yönelimlerine yönelik alan yazına katkı sağlamaktır. Öğrenme analitiklerinin geleceğine yönelik çalışmalar beş temel başlık altında ele alınmıştır. Bu çalışma başlıkları; öğrenme süreçlerinin kişiselleştirilmesi, öğrenme tasarımı, öğrenme yaşantıları tasarımı, öğrenme panelleri tasarımı ve Endüstri 4.0 uygulamaları şeklindedir. Çok yakın bir gelecekte EVM ve Endüstri 4.0 uygulama alanlarından birisi olan “Nesnelerin İnterneti (Internet of Things-IoT)” alanlarında çalışmaların yürütüleceği ve özellikle Öğrenim Yönetim Sistemlerinde (ÖYS) yer alan etkileşim verilerindeki örüntülerin keşfedilmesi ve daha etkili öğrenme ortamlarının tasarlanmasında araştırmacılara önemli bir güç katacağı düşünülmektedir.

Educational Data Mining and Learning Analytics: Past, Present and Future

Educational data mining and learning analytics have recently emerged as twoimportant fields aimed at rendering e-learning environments more effective. Aimof this study seeks first to reveal the differences between these two fields and thento discuss the future of these concepts by evaluating how they changed throughouthistory. Educational data mining refers to uncovering the patterns hidden in thebig data whilst learning analytics is the use of these patterns to optimize e-learningenvironments. One of the purpos,es of the study is to add to the literature on the future trends regarding theseconcepts. The studies on the future of learning analytics are categorized in fivemain headings: personalization of learning processes, learning design, learningexperience design, dashboard design and the Industry 4.0 applications. In the verynear future, it seems that studies will be performed on EDM and the Industry 4.0one of its application areas, “(Internet of Things-IoT)” and EDM has the potentialto substantially help researchers in discovering the patterns in the interaction datain the Learning Management Systems and in designing more effective learningenvironments.

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