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  • 标题:Approaches to learning does matter to predict academic achievement
  • 本地全文:下载
  • 作者:Cristiano Mauro Assis Gomes ; Heitor Blesa Farias ; Enio Galinkin Jelihovschi
  • 期刊名称:Revista de Psicología (PUCP)
  • 印刷版ISSN:0254-9247
  • 出版年度:2022
  • 卷号:40
  • 期号:2
  • 语种:Spanish
  • 出版社:Pontificia Universidad Católica del Perú. Departamento de Psicología
  • 摘要: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.
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