Predicting Student Performance in a Programming Tutoring System Using AI and Filtering Techniques

In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate approach to employ. Additionally, determining the optimal input parameters for each AI technique remains a pertinent question in this domain. This study employs machine learning (ML) and artificial neural networks (ANN) to predict student grades within a programming tutoring system. The experiment involved university students whose interaction data with the e-learning system were analyzed and used for predictions. By identifying the structural relationships between the properties of the input data, this research aims to determine the most efficient AI method for accurately predicting student performance in e-learning systems. The structure of the input data in these systems is described by variables related to individual student activities, so correlations between variables were a natural starting point for further theoretical considerations. In this manner, by applying a filtering technique based on the minimum redundancy-maximum relevance (mrMR) criterion, it was shown that correlations among predictors and between predictors and the target variable play a significant role in defining the appropriate model for predicting student grades. The results showed that ANN (the Levenberg-Marquardt algorithm with Bayesian regularization) outperformed ML methods, achieving the highest prediction accuracy. The results obtained from this study can be of great importance for learning technologies engineering and AI in general.