AI for Student Performance Prediction in E Learning Environments

Abstract

With the widespread use of e-learning platforms, a large amount of data about the academic performance of students is being generated, which can be used to predict future results. The present paper aims to investigate the use of artificial intelligence (AI) and machine learning in predicting the performance of students in e-learning platforms. The study analyzed how often students log in, how much they engage with the system and their grades to identify patterns useful for creating predictions. Thus, the article shows what methods or algorithms of AI and ML can be used to predict the performance of students in online learning, such as decision trees, random forests, support vector machines, and neural networks. In addition, to evaluate the effectiveness of different algorithms, the article compares their accuracy and identifies the most important predictors. Finally, the study discusses the challenges in analyzing and predicting the performance of students in e-learning platforms and offers recommendations for implementation. The authors concluded that AI can be used to improve student retention and help in developing the learning process in e-learning platforms. Thus, the research presented how the AI can be applied for evaluating learners’ performance in e-learning platforms and what challenges it involves.