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
Performance Comparison of Xgboost and Random Forest for The Prediction of Students Academic Performance (Published)
In educational data mining and learning analytics, predicting student academic performance is essential because it provides stakeholders with useful information to improve educational outcomes. In order to predict students’ academic results, this study assesses and contrasts the effectiveness of two popular machine learning algorithms: Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Data preparation methods, such as principal component analysis (PCA) and feature normalization, were used to enhance a real-world dataset of 400 records gathered from six departments at Federal Polytechnic Ukana. Based on their Eigen values and explained variance, sixteen crucial input features were chosen for examination. Eighty percent (80%) of the dataset was used for training, and the remaining twenty percent (20%) was used for testing. To evaluate the performance of the models, evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R-Squared Score (R²), Explained Variance Score (EVS), and Median Absolute Error (MedAE) were used. The findings show that both models have strong predictive powers, with RF marginally outperforming XGBoost in important parameters. The results highlight the potential of data-driven tactics to enhance student outcomes and offer evidence-based suggestions for choosing machine learning models in educational predictive analytics.
Keywords: : Academic Performance, Performance, Prediction, Random Forest, Students, extreme gradient boosting
An Intelligent Analytic Framework for Predicting Students Academic Performance Using Multiple Linear Regression and Random Forest (Published)
In the contemporary educational landscape, data-driven decision-making has become pivotal for enhancing student success. This article explores an intelligent analytic framework leveraging Multiple Linear Regression (MLR) and Random Forest (RF) algorithms to predict student performance, providing a comparative analysis of their predictive capabilities. MLR, a statistical technique, models the relationship between students’ grades and various factors such as attendance and socio-economic background, offering transparency and interpretability of the impact of each predictor. RF, an ensemble learning method, excels in handling large datasets and capturing non-linear interactions among variables, offering higher accuracy in prediction. The study was conducted using 664 datasets from eight departments of Federal Polytechnic Ukana, following rigorous data preprocessing and normalization. The performance of both models was evaluated based on metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), R-squared Score (R²), and Explained Variance Score (EVS). The results revealed that RF outperformed MLR significantly, with lower error rates and higher predictive accuracy. Scatter plots and bar charts further illustrated the robust performance of RF over MLR. This research underscores the potential of integrating advanced machine learning techniques in educational settings to provide deeper insights into student performance, enabling timely and targeted interventions. The findings advocate for the adoption of RF for more accurate predictions and improved educational outcomes. Future research should explore hybrid models and expand the dataset to validate the applicability of these findings across diverse educational contexts.
Keywords: : Academic Performance, Random Forest, machine learning and ensemble learning, multiple linear regression
Academic Performance of Universities and Polytechnics Students: The Impact of Social Media (Published)
Do social media indeed have an effect on the academic performance of students? And is the social media being fully utilized for the right purpose? These questions are some of the issues that this research tried to answer. This research is on the academic performance of university and polytechnic students and the impact that social media has on the students’ academic performance. Six institutions were used for the study; three polytechnics and three universities were selected. Students were randomly selected from the various institutions and the total population was 200 students. The study found out that students used more of facebook and whatsapp as social media for their various interactions and activities on social media. Facebook accounted for 60% of the population of the study that used it while the remaining 40% was for whatsapp. Even though some students used other media they predominantly used these two more frequently. The study found out that there is an impact that social can make on the academic performance of students if their habits can be changed in the positive direction.
Keywords: : Academic Performance, Social media, Students