Student Performance Prediction

Abstract

Abstract: Assessing student’s learning performance is a fundamental aspect of evaluating educational systems, playing a pivotal role in addressing challenges within the learning process and measuring learning outcomes. The emergence of educational data mining (EDM) as a research field has harnessed the power of data and knowledge to enhance education systems. EDM involves the development of techniques to analyze data collected from educational environments, offering a more comprehensive understanding of students and facilitating improvements in educational outcomes. The integration of machine learning (ML) technology has witnessed substantial growth, enabling researchers and educators to leverage data mining insights to predict and simulate educational processes, including success rates, dropouts, and more. This research paper explores the analysis of students' performance through data mining methods. It employs classification technique to discern the early-stage impact on GPA. In the classification methodology, various machine learning models are experimented with to predict student performance in the early stages, incorporating diverse features such as course grades and admission test scores. The paper employs different assessment metrics to evaluate model performance. The findings underscore the potential of educational systems to proactively address the risk of student failures during their initial stages of education