Academic achievement prediction in higher education through interpretable modeling.

ABSTRACT: Student academic achievement is an important indicator for evaluating the quality of education, especially, the achievement prediction empowers educators in tailoring their instructional approaches, thereby fostering advancements in both student performance and the overall educational quality. However, extracting valuable insights from vast educational data to develop effective strategies for evaluating student performance remains a significant challenge for higher education institutions. Traditional machine learning (ML) algorithms often struggle to clearly delineate the interplay between the factors that influence academic success and the resulting grades. To address these challenges, this paper introduces the XGB-SHAP model, a novel approach for predicting student achievement that combi

SUBMITTER: Wang S 

PROVIDER: S-EPMC11376577 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature