Unveiling the Best-fit Model: A Comparative Analysis of Classification Methods in Predicting Student Success

Abstract

To reduce failure and personalize instruction, educators work to predict student achievement. For this objective, this study compared several categorization techniques. The study investigated techniques employing datasets from Portuguese schools, even though various circumstances make it difficult to gather full data and achieve high accuracy. Upon evaluating the various algorithms, including Random Forest and Decision Trees, the study determined that Random Forest was the most successful model, attaining a 94.55% accuracy rate. This demonstrates how machine learning—more especially, Random Forest—could forecast student achievement. The study opens the door for applying these techniques to early interventions and personalized learning. But more work needs to be done, such as creating publicly accessible educational datasets and investigating different strategies like regression algorithms to manage the nuances of grading systems more effectively.