Student's Performance Evaluation Using Ensemble Machine Learning Algorithms

Journal Title: Engineering and Technology Journal - Year 2024, Vol 9, Issue 08

Abstract

This study explores the critical domain of predicting students' academic performance in educational institutions. By harnessing the potential of machine learning algorithms, specifically Random Forest, KNN, and XGBoost, and leveraging data collected through technology-enhanced learning applications, the research aims to provide valuable insights into the factors influencing academic outcomes based on the dataset obtained from Kaggle. It is important to note that these models were also hybridized using the stacking ensemble approach. The performances of the algorithm were evaluated using the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2) score. Resultively, the stacked ensemble model displayed remarkable results, with an impressively low RMSE of 0.1768, MSE of 0.0312, MAE of 0.1247, and a high R2-score of 0.9705. This finding showed that the Ensemble model, which combines the strengths of the Random Forest, KNN, and XGBoost algorithms, provides the best overall prediction accuracy, with a high degree of correlation between predicted and actual student performance.

Authors and Affiliations

Oladunjoye John Abiodun , Andrew Ishaku Wreford,

Keywords

Related Articles

PROPOSED IMPLEMENTATION OF LEAN MANUFACTURING TO REDUCE WASTE IN PLYWOOD PRODUCTION

CV. Treewood Abadi Group is a manufacturing company engaged in producing plywood products. In the plywood production process from raw materials to finished products there are still obstacles in the form of activities tha...

Analysis of Fish Supplies at the Poumako Fishing Port, Mimika Regency, Central Papua Province - Indonesia

One of the feeder ports in Fisheries Management Area (FMA) 718 is the Poumako fishing port in Central Papua Province, which encompasses fishing grounds in the sea of Aru, Arafuru, and Eastern Timor. As a supply of fish f...

COMPARATIVE ANALYSIS OF METAMATERIAL SUBSTRATE AND FABRIC SUBSTRATE

Fractal structures have the property ofself-similarity and ease of repetitiveness; they remain less used in planar patterned metamaterial structures. A fractal antenna is design based on the metamaterial for multiband ap...

Estimation of Solar Energy Potential Using Sunshine-Based Model for Busa-Baso Kebele, Dirashe Woreda, Ethiopia

The purpose of this study was focused on estimation of solar photovoltaic power systems in Busa Baso, Dirashe Woreda, Ethiopia. Solar photovoltaics are being promoted to replace fuel-based lighting and off-grid electrica...

Effects of Information and Communication Technology Adoption on University Competitive Advantage via Education Quality a Field Study on Science & Technology and Hadhramout Universities

The current study aimed to determine the effect of information and communication technology adoption on university competitive advantage via education quality in Yemenis Universities. For the purposes of this study, the...

Download PDF file
  • EP ID EP742800
  • DOI 10.47191/etj/v9i08.23
  • Views 40
  • Downloads 0

How To Cite

Oladunjoye John Abiodun, Andrew Ishaku Wreford, (2024). Student's Performance Evaluation Using Ensemble Machine Learning Algorithms. Engineering and Technology Journal, 9(08), -. https://europub.co.uk/articles/-A-742800