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<p>This study explores the complex interplay between cognitive factors and STEM achievement, examining how various cognitive constructs influence students’ performance in STEM education. Neuro-Cognitive STEM Enhancement (NCSE) refers to interventions that strengthen key cognitive functions—such as attention, memory and problem-solving—to improve STEM achievement. Specifically, it investigates the role of mathematical cognitive abilities (MCA) and errors and misconceptions (EMR) as key predictors of success in STEM disciplines. Using a hybrid approach of Artificial Neural Networks (ANN) and Partial Least Squares Structural Equation Modeling (PLS-SEM), the study identifies significant relationships between cognitive constructs and STEM performance, revealing the ways in which cognitive abilities and errors influence learning outcomes. The results suggest that MCA positively influences STEM proficiency, while EMR impedes cognitive processing and problem-solving abilities. These findings have important implications for education, particularly in designing instructional strategies that target cognitive development and address common misconceptions in STEM education. This study contributes to the growing body of research on the cognitive underpinnings of STEM achievement and provides valuable insights into how educators can foster better learning environments to enhance student success in STEM fields.</p>

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Understanding the interplay of cognitive factors and STEM achievement: implications for education

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • Anass Bayaga

摘要

This study explores the complex interplay between cognitive factors and STEM achievement, examining how various cognitive constructs influence students’ performance in STEM education. Neuro-Cognitive STEM Enhancement (NCSE) refers to interventions that strengthen key cognitive functions—such as attention, memory and problem-solving—to improve STEM achievement. Specifically, it investigates the role of mathematical cognitive abilities (MCA) and errors and misconceptions (EMR) as key predictors of success in STEM disciplines. Using a hybrid approach of Artificial Neural Networks (ANN) and Partial Least Squares Structural Equation Modeling (PLS-SEM), the study identifies significant relationships between cognitive constructs and STEM performance, revealing the ways in which cognitive abilities and errors influence learning outcomes. The results suggest that MCA positively influences STEM proficiency, while EMR impedes cognitive processing and problem-solving abilities. These findings have important implications for education, particularly in designing instructional strategies that target cognitive development and address common misconceptions in STEM education. This study contributes to the growing body of research on the cognitive underpinnings of STEM achievement and provides valuable insights into how educators can foster better learning environments to enhance student success in STEM fields.