Predicting test scores using random forest regression

This paper explores the use of predictive analytics to accurately forecast student performance based on prior test scores. It begins with an introduction outlining the study’s objectives and relevance, followed by a literature review that examines existing research, identifies key gaps, and positions this study as a contribution to bridging them. The methodology section details the model development process and its practical application, while the results and discussion evaluate model performance and highlight areas for improvement. Positioned within the broader EdTech landscape, this study demonstrates how machine learning can support educators by helping personalize instruction based on predicted student outcomes.