Intelligent Ensemble Learning Framework for Prediction of Students Academic Performance Using Extreme Gradient Boosting and Random Forest Algorithms (Published)
A key component of educational data mining (EDM) and learning analytics is the prediction of students’ academic achievement. Institutions can increase overall learning results, identify at-risk students, and carry out focused interventions by utilizing machine learning approaches. The Intelligent Ensemble Learning Framework presented in this paper combines Extreme Gradient Boosting (XGBoost) and Random Forest (RF) to increase prediction accuracy. XGBoost a powerful boosting strategy noted for its effectiveness in managing huge datasets and minimizing overfitting, combines multiple decision trees to reduce variation and improve model stability. The study uses information gathered from Federal Polytechnic Ukana, including attendance, demographics, and academic records of 400 students, among other pertinent characteristics. 16 important features were found based on eigenvalues and explained variance following data preprocessing, which included normalization and feature selection using Principal Component Analysis (PCA). The dataset was divided into subsets for testing (20%) and training (80%), and a bagging technique was used to create the ensemble model. Experimental results demonstrate that the ensemble model outperforms individual RF and XGBoost models in predicting students’ cumulative grade point average (CGPA). The performance evaluation, based on standard regression metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R-Squared Score (R2), Explained Variance Score (EVS), and Median Absolute Error (MedAE), indicates superior predictive accuracy. The ensemble model achieved an R2 score of 0.9900, outperforming RF (0.9888) and XGBoost (0.9800). Visualizations using scatter plots, grouped bar charts, and heat maps further validate the effectiveness of the proposed approach. This research contributes to the growing body of work in machine learning applications in education, demonstrating the potential of ensemble regression models in academic performance prediction. The findings underscore the importance of advanced predictive models in educational institutions, facilitating proactive decision-making and student support strategies to enhance academic success.
Keywords: : Academic Performance, Ensemble learning, Framework, Intelligence, Prediction, extreme gradient boosting and random forest
The Role of the Greek Term Techné in the Area of Today’s Government of Information Technology and Technological Progress (Published)
This paper will show the significance of the Greek term techné, in today’s world of technique and technology. In this direction, it is necessary to understand the meaning of the mentioned term as a fundamental term that gives meaning to today’s term technique. In addition, the term techné, shows the meaning of the terms originally associated with it, namely knowledge, technique and intelligence. The main goal of the research here is to show that the concept of, techné, in addition to being one of the fundamental concepts of philosophy, is a concept whose meaning we must understand if we want to successfully approach understanding the phenomenon of today, i.e., the rule of information technology and technological progress. All of the above means returning to the source of the term techné, and Martin Heidegger’s idea of the technique, which is the question that preoccupies everyone on the planet today.
Keywords: Intelligence, Knowledge, phenomenology, technique, techné