Enhancing data pipelines for forecasting student performance: integrating feature selection with cross-validation.

ABSTRACT: Educators seek to harness knowledge from educational corpora to improve student performance outcomes. Although prior studies have compared the efficacy of data mining methods (DMMs) in pipelines for forecasting student success, less work has focused on identifying a set of relevant features prior to model development and quantifying the stability of feature selection techniques. Pinpointing a subset of pertinent features can (1) reduce the number of variables that need to be managed by stakeholders, (2) make "black-box" algorithms more interpretable, and (3) provide greater guidance for faculty to implement targeted interventions. To that end, we introduce a methodology integrating feature selection with cross-validation and rank each feature on subsets of the training corpus. This modifie

SUBMITTER: Bertolini R 

PROVIDER: S-EPMC8591701 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature