Çevrimiçi Öğrenme Ortamında Akademik Başarıyı Artırmak İçin Karar Destek Sistemi

Year 2019, Volume: 15 Issue: 3, 8 - 22, 30.12.2019

Hakan Kör , Hasan Erbay Melih Engin

Abstract

Karar desktek sistemleri(KDS) kurum ve
organizasyonlar için geliştirilen, karar vericilerin daha sağlıklı ve gerekçeli
kararlar almasını sağlayan sistemlerdir. Bu sistemler, çevirimiçi eğitim
ortamlarında, özellikle daha yüksek başarı elde etmek için öğrenci ve eğitim
yöneticilerinin kullanımına sunulmaktadır. Çevrimiçi öğrenme ortamlarında,
öğrenciler farklı türlerde ders materyalleri ve etkileşim araçları
kullanmaktadır. Fakat, çoğu zaman öğrenciler akademik performanslarını olumlu
yönde etkileyecek ders içerikleri ve etkinliklerin seçiminde zorlanırlar.

Bu çalışmada, öğrencilere ve eğitim
yöneticilerine etkinlik öneri karar destek modeli oluşturulmuştur. Model,
öğrencilerin geçmiş verilerini işleyerek en iyi etkinlik seçimine yardımcı
olur. Karar destek sisteminde, veri madenciliği yöntemi kullanılmıştır. veri
ambarı için muhtemel özellikler ve veriler, moodle öğrenme yönetim sistemi
(ÖYS) aracılığıyla elde edilmektedir. Daha sonra, modelin performansını
artırmaya katkıda bulunan nitelikler, veri madenciliği işlemini uygulamak için
filitrelendi. Veri madenciliği sürecinde geleceğe yönelik tahmin işlemleri için
birçok karar ağacı algoritması kullanılmıştır. Ancak, C5 algorimasının diğer
karar ağacı algoritmalarından daha iyi performans sergilediği görülmüştür.

Veri madenciliği işleyişine ek olarak,
örneklemin demografik yapısı, haftalık başarı oranları ve ders kullanım
sayıları gibi çeşitli istatistiksel bilgilerde performansı artırmak için modele
eklenmiştir. Model için web tabanlı bir uygulama tasarlanmış ve uygulama
bölümünde yer verilmiştir.

Keywords

: Çevrimiçi Öğrenme, Veri Madenciliği

Activity Suggestion Decision Support System Design In Online Learning Environment

Abstract

Decision support systems is created for
organizations to enable decision makers to have healthier and more reasonable
actions. These systems are made available to students and administrators in
online education environments, especially for higher success. In online learning
environments, students utilize different types of course materials and
interaction tools, which provides reaching a higher success rate in a
considerable amount. However, students often difficult to choose course content
and activities that will positively affect their academic performance. In this
study, the decision support system model is constituted for students and
lecturer in terms of online learning environments. The model helps students
choose the best activity by processing their previous data. Data mining methods
have been used in decision making process. Possible features and data for the
data warehouse are obtained through moodle learning management system. Then,
the attributes that contributed to improving the performance of the model were
filtered to implement the data mining process. In the data mining process of
the research, many decision tree algorithms have been used for success
predictions. However, it has been seen that C5 algorithm performs better than
other decision tree algorithms. In addition to the data mining process,
demographic structure of the sample, weekly success rates and number of course
document usage were added to the model to improve performance in various
statistical information. A web based application has been designed for the
model and is added in the application section.

Keywords

Online Learning, Educational Data Mining, Classification Methods, Decision Tree Algorithms, C5 Decision Tree Algorithm

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