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Journals / International Journal of 3D Printing Technologies and Digital Industry / 2026 / Cilt: 10 Sayı: 2

INTELLIGENT EDUCATIONAL ANALYTICS: LEVERAGING MACHINE LEARNING FOR COMPREHENSIVE ACADEMIC PERFORMANCE PREDICTION

Botan Onat(AKDENİZ ÜNİVERSİTESİ)0000-0001-7063-4066
Alper Bilge(AKDENIZ UNIVERSITY)0000-0003-3467-9915
Ayça Akın(Antalya Belek University)0000-0002-6107-3487
Pages
425–439
Publication Date
2026-08-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results may be used as criteria for schools seeking to employ data-driven strategies for improving student achievement and academic performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The current research provides a thorough exploration into different methods of machine learning used to predict educational performance using a variety of data sources.
📊
Key Findings & Data:This research studies methods for predicting academic performance and displays the difference in performance of each model, including performance measures, demographics, and behavior survey data collected from middle school-aged children.
💡
Conclusion & Impact:The results may be used as criteria for schools seeking to employ data-driven strategies for improving student achievement and academic performance.
🔬Design:Survey & Empirical Study
📍Scope:Comprehensive Sample
⚙️Variables:Intelligent Educational Analytics · Machine Learning · Academic Performance Prediction

Abstract

The current research provides a thorough exploration into different methods of machine learning used to predict educational performance using a variety of data sources. This research studies methods for predicting academic performance and displays the difference in performance of each model, including performance measures, demographics, and behavior survey data collected from middle school-aged children. In this research, 5-fold cross-validation was useful for demonstrating increases in the accuracy of predictions with the use of multiple types of data without significantly affecting the level of computing needed for making predictions. The research showed that using multiple data types significantly increases the predictive power of the model. Additionally, among all methods evaluated, multiple linear regression had the best balance between accuracy and computational requirement for predicting student performance. The factors determined to be the most predictive of student academic performance included student behavior, the educational level of parents, socio-economic status, and historically earned grades. The study of educational data mining presented in this paper provides a valuable understanding of how different data integration configurations affect how well algorithms compare in performance. The results may be used as criteria for schools seeking to employ data-driven strategies for improving student achievement and academic performance.

Özet

The current research provides a thorough exploration into different methods of machine learning used to predict educational performance using a variety of data sources. This research studies methods for predicting academic performance and displays the difference in performance of each model, including performance measures, demographics, and behavior survey data collected from middle school-aged children. In this research, 5-fold cross-validation was useful for demonstrating increases in the accuracy of predictions with the use of multiple types of data without significantly affecting the level of computing needed for making predictions. The research showed that using multiple data types significantly increases the predictive power of the model. Additionally, among all methods evaluated, multiple linear regression had the best balance between accuracy and computational requirement for predicting student performance. The factors determined to be the most predictive of student academic performance included student behavior, the educational level of parents, socio-economic status, and historically earned grades. The study of educational data mining presented in this paper provides a valuable understanding of how different data integration configurations affect how well algorithms compare in performance. The results may be used as criteria for schools seeking to employ data-driven strategies for improving student achievement and academic performance.

🔖

Cite This Article

Onat, B., Bilge, A., Akın, A. (2026). INTELLIGENT EDUCATIONAL ANALYTICS: LEVERAGING MACHINE LEARNING FOR COMPREHENSIVE ACADEMIC PERFORMANCE PREDICTION. International Journal of 3D Printing Technologies and Digital Industry, Cilt: 10 Sayı: 2, 425–439. https://doi.org/10.46519/ij3dptdi.1947910

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