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