Approaches to learning does matter to predict academic achievement

• Autores: Assis Gomes, Cristiano Mauro, Blesa Farias, Heitor, Galinkin Jelihovschi, Enio
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: Revistas - Pontificia Universidad Católica del Perú
• Lenguaje: inglés
• OAI Identifier: oai:revistaspuc:article/25502
• Enlace del recurso: http://revistas.pucp.edu.pe/index.php/psicologia/article/view/25502
• Nivel de acceso: acceso abierto
• Materia: Students’ approaches to learning; Achievement; Intelligence; Incremental validity; CART algorithm; Enfoques de aprendizaje; Realización; Inteligencia; Validez incremental; Algoritmo CART; Approche d’apprentissage; Rendement; Validité incrémentielle; Algorithme CART; Abordagens de aprendizagem; Rendimento; Inteligência; Validade incremental

Meta-analyses show that the correlations of approaches to learning with academic achievement are low. However, only one study has controlled these correlations in the presence of intelligence. Our study investigates the incremental validity of the surface and deep approaches taking cognitive abilities as control and applied the multiple linear regression and CART regression tree method in a diversified sample of high school students from Minas Gerais, Brazil. We found a prediction of 20.37% and 35.20% of the outcome variance, through the linear regression and CART, respectively. Our results show that although cognitive abilities are more important than students’ approaches, the approaches have incremental validity and important roles as predictors of academic achievement.