Arif ALTUN, Gökhan AKÇAPINAR, Petek AŞKAR

ÇEVRİMİÇİ ÖĞRENME ORTAMINDAKİ FARKLI ÖĞRENCİ PROFİLLERİNİN KÜMELEME YÖNTEMİ iLE BELİRLENMESİ

Bu çalışmanın amacı, çevrimiçi öğrenme ortamında benzer davranış örüntüsü sergileyen farklı öğrenci gruplarının kümeleme yöntemi ile belirlenmesidir. Çalışma, Türkiye'de bir devlet üniversitesinde Bilgisayar ve Öğretim Teknolojileri Eğitimi Bölümü'nde Bilgisayar Donanımı dersine kayıtlı 76 üniversite ikinci sınıf öğrencisi ile yürütülmüştür. Öğrenciler 14 hafta süresince yüz yüze derslere ek olarak çevrimiçi öğrenme ortamında, yansıma yazma, tartışma, soru-cevap, kaynak takibi vb. aktiviteler gerçekleştirmişlerdir. Analizlerde kullanılan veriler bu ortamın veri tabanından elde edilmiştir. Veriler iki farklı kümeleme algoritması ile analiz edilmiş ve sonuçları karşılaştırılmıştır. Aynı zamanda elde edilen farklı öğrenci gruplarının akademik performansları incelenerek etkileşim düzeyi ile akademik performans arasındaki ilişki analiz edilmiştir. Araştırma sonuçları öğrencilerin çevrimiçi öğrenme ortamındaki davranış örüntülerine göre üç farklı kümeye ayrılabileceğini göstermiştir. Bu kümelerin isimlendirilmesi konusunda ise iki farklı yaklaşım izlenmiştir. Kümeler, her bir kümede yer alan öğrencilerin aktivite düzeylerine göre (Çok aktif, Aktif, Aktif olmayan) ve öğrencilerin akademik performanslarına göre (Yüksek öğrenenler, Orta öğrenenler, Düşük öğrenenler) tanımlanmıştır.

IDENTIFYING DIFFERENT STUDENT PROFILES IN AN ONLINE LEARNING ENVIRONMENT WITH CLUSTER ANALYSIS

The purpose of this study is to explore how to group students who exhibit similar behavior patterns in an online learning environment via clustering. Relationships between these clusters and students' academic performance in each cluster were examined, as well. The study group consisted of 76 university students studying at the Computer Education and Instructional Technology Department at a state university in Turkey. In addition to the face to face classes during the course of 14 weeks, the students conducted several activities (writing reflections, participating in discussions, following course resources, etc.) in the online learning environment. The results of the cluster analysis indicated that students were ideally divided into three different groups according to the activities carried out in the online learning environment. Regarding the labeling of these clusters, two different methods were used. Firstly, the finalized clusters were named according to the students' activity levels (Non-active, Active, Very active). Then, these clusters were named with regards to students' academic performance (Low learners, Moderate learners, High learners).

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