A Data-driven Predictive Approach Using Custom and Regression Algorithms for Estimating Student’s Performance

Efficient approaches to estimating and understanding student performance are paramount in the dynamic landscape of higher education. This paper introduces "AData-driven Predictive ApproachUsing Custom and Regression Models for Estimating Student’s Performance," aiming to provide educators with valuable insights into academic outcomes. By integrating specific academic data sources and employing regression analysis, machine learning algorithms, and other data-driven methodologies, educators can gain actionable insights into student performance trends through predictive modeling. The paper delves into the technical complexities of the strategy, outlining methodologies and presenting practical implementations through illustrative examples and case studies. The ethical considerations of data usage and privacy are carefully investigated, emphasizing responsible implementation in the educational context. Pilot implementations in real-world educational settings are explored, shedding light on the strategy’s efficacy and potential impact on educators and students. This study represents a significant advancement in using data-driven strategies to estimate student performance in higher education, envisioning a future where educators are equipped with valuable tools to understand and support each student’s academic journey comprehensively.