MEVLÜT TÜRE, Zekeriya AKTÜRK, İMRAN KURT ÖMÜRLÜ, Nezih DAĞDEVİREN

The effect of health status, nutrition, and some other factors on low school performance using induction technique

Amaç: Bu çalışmada Lojistik Regresyon (LR) ve Chi-squared Automatic Interaction Detection (CHAID) yöntemleri kullanılarak bazı faktörlerin (beslenme, sağlık göstergeleri, riskli davranışlar, kişilik özellikleri, aile göstergeleri, vb.) okul başarısı üzerindeki etkileri araştırıldı. Çalışma Planı: Çalışma örneklemi, 2003 yılında Edirne’de okuyan 12150 öğrenciden oluşan çalışma evreninden, tabakalı örneklemeyle rasgele seçilen 873 ortaokul ve lise öğrencisinden oluşturuldu. Bulgular: Duyarlılık, doğruluk ve özgüllük oranları CHAID için sırasıyla %61.19, %67.70 ve %74.25; LR için sırasıyla %50.00, %64.29 ve %75.69 bulundu. Babanın eğitim düzeyi CHAID yönteminde en önemli faktör olarak bulundu. Aynı yöntemle, sigara kullanımı, ev ödevi için ayrılan süre ve beslenme faktörleri, başarısızlığı tahmin eden diğer önemli faktörler olarak saptandı. Sonuç: Sınıflandırma ağacı algoritması, okul başarısının kontrolü için risk analizi ve hedef belirlemede kullanılabilir bir yöntemdir. Bu çalışmanın sonuçları, ortaokul ve lise öğrencilerinin eğitimiyle ilgili kişilere bir kılavuz olarak katkıda bulunabilir.

Anahtar Kelimeler:

Başarı, Sağlık durumu, Ki-kare dağılımı, Ergen davranışı, Diyet, Aile özellikleri, Öğretim

Tümevarım tekniği kullanılarak sağlık durumu, beslenme ve bazı diğer faktörlerin okul başarısızlığına etkisinin araştırılması

Objectives: We investigated the effect of some hypothetical factors (nutrition, health indicators, risk behaviors, personal characteristics, and family indicators) on academic achievement using Logistic Regression (LR) and the Chi-squared Automatic Interaction Detection (CHAID) method. Study Design: Participants were 873 secondary school or high school students selected randomly from a total of 12,150 students after stratification according to school populations in Edirne, in 2003. Results: The sensitivity, positive predictivity, and specificity rates were 61.19%, 67.70%, and 74.25% for CHAID, and 50.00%, 64.29%, and 75.69% for LR, respectively. Father’s educational level was the most important factor in the CHAID method. Smoking status, time reserved for homework, and nutrition were the other important factors predicting low school performance according to the CHAID method. Conclusion: The classification tree algorithm can be used in risk analysis and target segmentation for academic achievement management. Our results may contribute to developing guidelines for those involved in secondary school and high school education.

Keywords:

Achievement, Health Status, Chi-Square Distribution, Adolescent Behavior, Diet, Family Characteristics, Teaching,

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