Search Results for digital-learning
Abstract
This study presents a machine learning-based framework for predicting and analyzing student academic performance using behavioral, psychological, demographic, and digital learning features. The dataset consists of 14,003 student records collected from higher education institutions in 2024 and obtained from Kaggle. It includes 16 variables covering study behaviors, engagement patterns, learning resources, psychological factors, and demographic characteristics. The target variable (response) was Final Grade, which was categorized into four performance levels: Low / Fail / Poor performance, below average performance, Good / Average performance, and Excellent / High performance, representing overall academic performance. Three machine learning algorithms XGBoost (Extreme Gradient Boosting), CatBoost (Categorical Boosting), and Support Vector Machine (SVM) were applied to classify student performance levels. The models were evaluated using confusion matrices, accuracy, F1-score, MCC, R², RMSE, MAE, and feature importance analysis. Results show that XGBoost achieved the highest performance (99.96% accuracy), followed by CatBoost (99.82%) and SVM (98.96%). Analysis of the importance analysis revealed that Exam Score is the most influential predictor across all models. The study highlights the effectiveness of advanced machine learning techniques in predicting academic success and supporting data-driven educational decision-making.
Abstract
The integration of smart technologies into higher education is rapidly reshaping traditional teaching and learning environments. This study investigates differences in academic performance between students utilizing AI technologies and those who do not, spanning three educational levels: undergraduate, graduate, and postgraduate. Using a simulated dataset of 200 participants, the analysis employed independent sample t-tests, chi-square tests, regression models, correlation analyses, and visualizations such as scatter plots. The findings consistently demonstrate that students who adopt AI significantly outperform their peers, with notable statistical improvements across all groups. Additionally, factors like educational level and age were found to influence outcomes, with younger students and AI users showing greater consistency and higher achievement. Beyond presenting numerical data, the research contextualizes these results within modern digital learning theories, addresses challenges in technological integration, and offers practical recommendations for effectively incorporating AI tools into higher education frameworks.