Assessing Student Success: The Impact of Machine Learning and XAI-BBO Approach

Yıl 2024, Cilt: 5 Sayı: 1, 40 - 54, 27.06.2024

Cem Özkurt

https://doi.org/10.58769/joinssr.1480695

Öz

In the study conducted to analyze the factors affecting student success in education, various preprocessing steps were applied to the dataset, and transformations aimed at effectively utilizing categorical variables were particularly implemented. These transformations included factors such as students' gender, age range, and parental education level. Subsequently, the Biogeography-Based Optimization (BBO) algorithm was utilized to determine the most important 20 features, which were then incorporated into machine learning models. During the evaluation phase, metrics such as Accuracy, Precision, Recall, and F1 score were employed to obtain results. The highest Accuracy value, 0.7388, was achieved with the Gradient Boosting algorithm. To elucidate the success of this algorithm, interpretable artificial intelligence models such as SHAP and LIME methods were employed. The findings of the study underscored the importance of detailed examination of factors influencing student success, emphasizing the need for further research to formulate education policies more effectively. The results of this study may contribute to the enhancement of data-driven decision-making processes in education and the more effective planning of interventions aimed at improving student success.

Anahtar Kelimeler

Explalinable AI, Biogeography-Based Optimization (BBO) algorithm, Machine Learning models, Gradient Boosting algorithm, SHAP method, LIME method

Öğrenci Başarısını Değerlendirme: Makine Öğrenimi ve XAI-BBO Yaklaşımının Etkisi

Öz

Eğitimde öğrenci başarısını etkileyen faktörlerin analizi amacıyla yapılan çalışmada, veri setine çeşitli ön işleme adımları uygulanmış ve özellikle kategorik değişkenlerin etkin şekilde kullanılmasına yönelik dönüşümler uygulanmıştır. Bu dönüşümler öğrencilerin cinsiyeti, yaş aralığı, ebeveynlerin eğitim düzeyi gibi faktörleri içeriyordu. Daha sonra, en önemli 20 özelliği belirlemek için Biyocoğrafya Tabanlı Optimizasyon (BBO) algoritması kullanıldı ve bunlar daha sonra makine öğrenme modellerine dahil edildi. Değerlendirme aşamasında sonuçların elde edilmesinde Doğruluk, Hassasiyet, Geri Çağırma ve F1 puanı gibi metrikler kullanıldı. En yüksek Doğruluk değeri olan 0,7388'e Gradient Boosting algoritması ile ulaşıldı. Bu algoritmanın başarısının aydınlatılması için SHAP ve LIME yöntemleri gibi yorumlanabilir yapay zeka modelleri kullanıldı. Araştırmanın bulguları, öğrenci başarısını etkileyen faktörlerin ayrıntılı olarak incelenmesinin önemini vurgulayarak, eğitim politikalarının daha etkili bir şekilde formüle edilmesi için daha fazla araştırmaya ihtiyaç duyulduğunu vurguladı. Bu çalışmanın sonuçları eğitimde veriye dayalı karar verme süreçlerinin geliştirilmesine ve öğrenci başarısını artırmaya yönelik müdahalelerin daha etkin planlanmasına katkı sağlayabilir.

Anahtar Kelimeler

Açıklanabilir Yapay Zeka, Biyocoğrafya Tabanlı Optimizasyon (BBO) algoritması, Makine Öğrenimi modelleri, Gradient Boosting algoritması, SHAP yöntemi, LIME yöntemi

Kaynakça

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