Search Results for academic-performance
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.
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
This study aims to build an applied model that explains the impact of strategic planning in activating total quality management to improve academic performance in business schools. To achieve this, the study seeks to explain the basic concepts of strategic planning and total quality management, and to establish the correlational and complementary relationship between them in the educational environment. The study relied on the descriptive analytical approach of previous literature and international standards for academic accreditation. The study concluded that strategic planning represents the basic guiding input, while total quality management provides the executive environment to raise the efficiency of academic performance outcomes (education, scientific research, and accreditation). The study recommends that business schools adopt the proposed model as a comprehensive administrative tool to achieve institutional excellence and keep pace with labor market requirements.
Abstract
This study examines the role of university 0.4—a conceptual framework integrating technology, accessibility, and inclusive policies—in upholding the right to education at Al-Mustaqbal Private University, the research assesses how digital transformation, adaptive learning systems, and institutional policies contribute to equitable access, quality education, and student success. Data was collected via surveys (N=73 faculty), interviews with administrators, and analysis of academic performance metrics. Findings suggest that University 0.4 initiatives, such as virtual classrooms, AI-driven support tools, and financial aid programs, significantly reduce barriers for marginalized groups, including low-income and disabled students. However, challenges like digital literacy gaps and infrastructure limitations persist. The study concludes with recommendations for scaling University 0.4 innovations to align with SDG 4 (Quality Education) and national educational rights frameworks.