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
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