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

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...

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Bibliographic Details
Main Authors: Roberto Bertolini, Stephen J. Finch, Ross H. Nehm
Format: Article
Language:English
Published: SpringerOpen 2021-08-01
Series:International Journal of Educational Technology in Higher Education
Subjects:
Data pipeline
Feature selection
Cross-validation
Data mining
Introductory biology
Online Access:https://doi.org/10.1186/s41239-021-00279-6

Internet

https://doi.org/10.1186/s41239-021-00279-6

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