Version 1
: Received: 14 June 2024 / Approved: 14 June 2024 / Online: 20 June 2024 (02:56:24 CEST)
How to cite:
Alzahrani, M. R. Predicting Student Performance Using Ensemble Models and Learning Analytics Techniques. Preprints2024, 2024061100. https://doi.org/10.20944/preprints202406.1100.v1
Alzahrani, M. R. Predicting Student Performance Using Ensemble Models and Learning Analytics Techniques. Preprints 2024, 2024061100. https://doi.org/10.20944/preprints202406.1100.v1
Alzahrani, M. R. Predicting Student Performance Using Ensemble Models and Learning Analytics Techniques. Preprints2024, 2024061100. https://doi.org/10.20944/preprints202406.1100.v1
APA Style
Alzahrani, M. R. (2024). Predicting Student Performance Using Ensemble Models and Learning Analytics Techniques. Preprints. https://doi.org/10.20944/preprints202406.1100.v1
Chicago/Turabian Style
Alzahrani, M. R. 2024 "Predicting Student Performance Using Ensemble Models and Learning Analytics Techniques" Preprints. https://doi.org/10.20944/preprints202406.1100.v1
Abstract
This paper explores the utilization of ensemble models and learning analytics techniques to predict student academic performance. With the advent of educational big data, institutions are increasingly leveraging advanced analytics to gain insights into student learning patterns and optimize educational outcomes. Ensemble models, which combine the predictive power of multiple algorithms, offer a robust approach to enhance prediction accuracy. The performance of the ensemble models was analyzed and compared using the Open University Learning Analytics Dataset, which consists of sources such as demographic information, historical performance data, and engagement metrics for 23,344 students. The evaluation of various ensemble models across different classification scenarios revealed that the proposed stacking model consistently emerges as the best-performing model, excelling in both multi-class and binary classification tasks.
Keywords
ensemble models; student performance; machine learning; stacking; bagging; random forest
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
