Yükseköğretimde Okul Terki ve Mezuniyet: CHAID Analizi

Öz

Bu araştırmada, yükseköğretim öğrencilerinin okul terki ve mezuniyet durumları üzerinde anlamlı etkisi olan sosyoekonomik değişkenlerin tespit edilmesi ve önem sırasının belirlenmesi amaçlanmıştır. Araştırmada ilişkisel tarama modeli kullanılmıştır. Çalışmada “Students Dropout and Academic Success Dataset” veri seti kullanılmıştır. Polytechnic Institute of Portalegre tarafından oluşturulan veri seti, 4424 kayıt içermektedir. Verilerin analiz edilmesinde CHAID karar ağacı algoritması kullanılmıştır. Bu sayede bağımlı değişkende en fazla farklılaşmayı gösteren bağımsız değişkenler hiyerarşik olarak tespit edilmiştir. Araştırmada öğrencilerin %49.93’ünün okulu tamamlama durumlarının “mezun”, %32.12’sinin “terk”, %17.94’ünün “devam eden” olduğu görülmektedir. Elde edilen bulgular öğrencilerin mezuniyet oranlarının istenilen düzeyde olmadığını göstermektedir. Öğrencilerin okul bitirme durumlarını en iyi açıklayan değişkenin “üniversite harç borcu” olduğu bulunmuştur. Harç borcu olan öğrencilerin %86.55'i okulu terk etmiş, harç borcu olmayan öğrencilerin %55.95'i mezun olmuştur. “Üniversite harç borcu” değişkeninin oluşturduğu kümeyi en iyi açıklayan değişken “burs sahibi” olarak bulunmuştur. Üniversite harç borcu olan ve burslu olmayan öğrencilerin %89.00’u okulu terk ederken, harç borcu olmayan ve burslu öğrencilerin %78.44'ü mezun olmuştur. Araştırmadan elde edilen sonuçlardan yola çıkılarak, okul terki sorunuyla başa çıkmak ve okul terkiyle ilgili daha fazla araştırmayı yönlendirmek için çeşitli önerilerde bulunulmuştur.

Anahtar Kelimeler

• Yükseköğretim
• okul terki
• mezuniyet
• karar ağaçları
• CHAID analizi

Dropout and Graduation in Higher Education: CHAID Analysis

Öz

This study aims to investigate the socioeconomic variables and their order of importance that have a significant effect on the dropout and graduation of higher education students. Relational survey model was used in the study. In the study, the "Students Dropout and Academic Success Dataset," was utilized. The dataset, created by the Polytechnic Institute of Portalegre, consists of 4424 records. CHAID decision tree algorithm was used to analyze the data. With this method, the independent variables that demonstrate the maximum variation in the dependent variable have been identified hierarchically. It is found that, 49.93% of the students are “graduate”, 32.12% are “dropout”, and 17.948% are “enrolled”. Obtained findings show that the graduation rates of the students are not at the desired level. “Tuition fees up to date” was found as the best variable that explains the students’ school completion. 86.55% of students with not up to date tuition fees were found as dropout and 55.95% of students with up-to-date tuition fees were found as graduate. “Scholarship holder” was found as the variable that best explains the clusters formed by variable “tuition fees up to date”. 89.00% of the students that don’t have their tuition fees up to date and don’t hold a scholarship dropout the school, while 78.44% of students that have their tuition fees up to date and holding a scholarship are graduate. Building on the results obtained from the study, several suggestions were proposed for coping with dropout problem and further guiding research on dropout.

Anahtar Kelimeler

• Higher education
• dropout
• graduation
• decision trees
• CHAID analysis

Kaynakça

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