Bir Öğrenci Bilgi Sisteminin Kullanılabilirliğinin Makine Öğrenmesi Teknikleriyle Tahmin Edilmesi

Year 2019, Volume: 2 Issue: 1, 10 - 18, 13.07.2019

Denizhan DEMİRKOL Elif KARTAL Çağla ŞENELER Sevinç GÜLSEÇEN

Abstract

Sistem
kullanılabilirliği, bir sistemin özellikle tasarım ve test aşamalarında
odaklanılması gereken unsurlardan biridir, çünkü sistemin daha iyi hale
getirilmesi için sistem yöneticilerine geri bildirim sağlamaktadır.
Literatürde, sistem kullanılabilirliğinin değerlendirilmesi için Sistem
Kullanılabilirlik Ölçeği (System Usability Scale-SUS) altın standart yöntem
olarak yaygın şekilde kullanılmaktadır. Bunun yanı sıra günümüzde yapay zekânın
alt çalışma alanlarından biri olan makine öğrenmesi de sistem kullanılabilirliğinin
değerlendirilmesi konusunda araştırmacılara yeni ufuklar sağlamaktadır. Bu
çalışmada, bir Öğrenci Bilgi Sisteminin (ÖBS) kullanılabilirliğinin makine
öğrenmesi teknikleriyle tahmin edilmesi hedeflenmiştir. Çalışma yönteminde;
Veri Madenciliği için Çapraz Endüstri Standard Süreç Modeli (CRoss-Industry
Standard Process for Data Mining–CRISP-DM) kullanılmıştır. Analizler;
Türkiye’deki bir vakıf üniversitesine ait bir ÖBS’yi kullanan 324 öğrencinin
SUS’un Türkçe versiyonuna (SUS-TR) verdiği yanıtların bulunduğu “sus1” adlı
veri seti ile “sus1” veri setine öğrencilerin yaş, cinsiyet, öğrenim gördüğü
bölüm eklenerek oluşturulan “sus0” adlı veri setleri üzerinde
gerçekleştirilmiştir. C4.5 Karar Ağacı Algoritması, Naive Bayes Sınıflandırıcı
ve k-En Yakın Komşu Algoritması ile farklı modeller kurularak performans
değerlendirmesi yapılmıştır. %80’e %20’lik Hold-out ayrımıyla gerçekleştirilen
analizlerde en iyi performans, k-En Yakın Komşu Algoritmasıyla “sus0” veri seti
üzerinde elde edilmiştir (k=20 için doğruluk = 0.698, F-ölçütü = 0.796).

Keywords

danışmanlı öğrenme, kullanılabilirlik, makine öğrenmesi, öğrenci bilgi sistemi, sistem kullanılabilirlik ölçeği

Predicting Usability of a Student Information System by Using Machine Learning Techniques

Abstract

System usability is one
of the key elements that should be focused on, especially during the design and
test phases of a system, because it provides feedback to system administrators
in order to improve the system. In the literature, System Usability Scale (SUS)
is widely used as the gold standard method to evaluate system usability. Today,
machine learning, which is one of the subfields of artificial intelligence,
also provide new perspectives on the evaluation of system usability. In this
study, it is aimed to predict usability of a Student Information System (SIS)
by using machine learning techniques. In the study method, the Cross-Industry
Standard Process for Data Mining (CRISP-DM) steps have been followed. Analysis
are performed on two different dataset namely “sus1” and “sus0”. “sus1” dataset
is consisted of demographic characteristics (age, gender, department) of 324
students using a SIS of a foundation university in Turkey, also their responses
to the Turkish version of the SUS. “sus0” includes only responses to the
Turkish version of the SUS. C4.5 Decision Tree Algorithm, Naive Bayes
Classifier and k-Nearest Neighbor Algorithm are used to create models and their
performance are evaluated. The best performance was obtained on the “sus0” data
set with 80% to 20% hold-out method (accuracy = 0.698, F-measure = 0.796 for k
= 20).

Keywords

supervised learning, usability, machine learning, student information systems, system usability scale

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