Predicting Student Success and Tailoring Learning Experiences: An Exploration of LSTMs and Causal Analysis

Abstract

Abstract: This paper explores the potential of machine learning to predict student success and personalize the learning experience. The research focuses on using Long Short-Term Memory (LSTM) networks and causal analysis to achieve these objectives. A comprehensive student dataset from Kaggle was employed in this study, and various machine-learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors, were systematically compared and evaluated. Logistic Regression emerged as the most effective model for predicting student success based on specific data characteristics. Beyond prediction, the paper delves into the application of causal analysis to identify factors influencing student performance. Understanding these factors enables the development of a system that recommends personalized learning interventions tailored to individual student needs. The potential benefits of this approach for students, educators, and society are significant, providing a pathway to more effective and personalized education. The paper also addresses the importance of responsible data practices and ethical considerations in the implementation of such technologies.