Explaınable Machıne Learnıng For Predıctıng Students’ Academıc Performance From Generatıve AI Usage Patterns in Hıgher Educatıon

Abstract

Generative artificial intelligence is increasingly embedded in higher education learning practices, yet its contribution to academic performance remains contested. This study developed an explainable machine-learning framework for predicting students’ post-semester GPA from Generative AI usage patterns and learning-related characteristics. Using a structured dataset of 50,000 student records, the study modelled academic performance with predictors including pre-semester GPA, weekly GenAI usage hours, primary use case, prompt-engineering skill, tool diversity, traditional study hours, perceived AI dependency, institutional policy, exam anxiety, year of study, and major category. To prevent outcome leakage, non-relevant outcome variables and student identifiers were excluded before training. Ridge Regression, Decision Tree Regression, and Extra Trees Regression were compared using R², RMSE, and MAE. The results showed that Ridge Regression achieved the strongest predictive performance, indicating that academic outcomes were largely explainable through stable relationships among prior achievement, GenAI-related behaviours, and study characteristics. Feature-importance analysis revealed that prior GPA, primary GenAI use case, and prompt-engineering skill were among the most informative predictors. The study contributes to GenAI-aware learning analytics by demonstrating how explainable modelling can support evidence-based academic monitoring.

Keywords

• Generative AI
• academic performance prediction
• explainable machine learning
• learning analytics
• higher education

Yükseköğretimde Öğrencilerin Üretken Yapay Zeka Kullanım Modellerinden Akademik Performanslarını Tahmin Etmek için Açıklanabilir Makine Öğrenimi

Abstract

Üretken yapay zeka, yükseköğretim öğrenme uygulamalarına giderek daha fazla entegre edilmektedir, ancak akademik performansa katkısı tartışmalı olmaya devam etmektedir. Bu çalışma, öğrencilerin dönem sonu not ortalamasını, Üretken Yapay Zeka kullanım modelleri ve öğrenmeyle ilgili özelliklerden tahmin etmek için açıklanabilir bir makine öğrenimi çerçevesi geliştirmiştir. Çalışma, 50.000 öğrenci kaydından oluşan yapılandırılmış bir veri seti kullanarak, dönem öncesi not ortalaması, haftalık Üretken Yapay Zeka kullanım saati, birincil kullanım amacı, komut mühendisliği becerisi, araç çeşitliliği, geleneksel çalışma saati, algılanan yapay zeka bağımlılığı, kurumsal politika, sınav kaygısı, öğrenim yılı ve bölüm kategorisi gibi tahmin değişkenleri ile akademik performansı modellemiştir. Sonuç sızıntısını önlemek amacıyla, ilgili olmayan sonuç değişkenleri ve öğrenci tanımlayıcıları eğitim öncesinde veri setinden çıkarılmıştır. Ridge Regresyon, Karar Ağacı Regresyonu ve Ekstra Ağaçlar Regresyonu, R², RMSE ve MAE kullanılarak karşılaştırılmıştır. Sonuçlar, Ridge Regresyonun en güçlü tahmin performansını elde ettiğini ve akademik sonuçların büyük ölçüde önceki başarı, Üretken Yapay Zeka ile ilgili davranışlar ve çalışma özellikleri arasındaki istikrarlı ilişkiler yoluyla açıklanabildiğini göstermiştir. Özellik önem analizi, önceki not ortalaması, birincil Üretken Yapay Zeka kullanım amacı ve komut mühendisliği becerisinin en bilgilendirici tahmin değişkenleri arasında olduğunu ortaya koymuştur. Bu çalışma, açıklanabilir modellemenin kanıta dayalı akademik izlemeyi nasıl destekleyebileceğini göstererek, Üretken Yapay Zeka farkındalıklı öğrenme analitiğine katkıda bulunmaktadır.

Keywords

• Üretken yapay zeka
• akademik performans tahmini
• açıklanabilir makine öğrenimi
• öğrenme analitiği
• yükseköğretim

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