Predicting academic achievement: The role of Motivation and Learning Strategies

Autores
Stover, Juliana Beatriz; Freiberg Hoffmann, Agustín; de la Iglesia, Guadalupe; Fernandez Liporace, Maria Mercedes
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The aim of this study consists in testing a predictive model of academic achievement including motivation and learning strategies as predictors. Motivation is defined as the energy and the direction of behaviors; it is categorized in three types of motivation –intrinsic, extrinsic and amotivation (Deci & Ryan, 1985). Learning strategies are deliberate operations oriented towards information processing in academic activities (Valle, Barca, González & Núñez, 1999). Several studies analysed the relationship between motivation and learning strategies in high school and college environments. Students with higher academic achievement were intrinsically motivated and used a wider variety of learning strategies more frequently. A non-experimental predictive design was developed. The sample was composed by 459 students (55.2% high-schoolers; 44.8% college students). Data were gathered by means of sociodemographic and academic surveys, and also by the local versions of the Academic Motivation Scale –EMA, Echelle de Motivation en Éducation (Stover, de la Iglesia, Rial Boubeta & Fernández Liporace, 2012; Vallerand, Blais, Briere & Pelletier, 1989) and the Learning and Study Strategies Inventory –LASSI (Stover, Uriel & Fernández Liporace, 2012; Weinstein, Schulte & Palmer, 1987). Several path analyses were carried out to test a hypothetical model to predict academic achievement (Kline, 1998). Results indicated that self-determined motivation explained academic achievement through the use of learning strategies. The final model obtained an excellent fit (χ2=16.523, df= 6, p=0.011; GFI=0.987; AGFI=0.955; SRMR=0.0320; NFI=0.913; IFI=0.943; CFI=0.940). Results are discussed considering Self Determination Theory and previous research.
Fil: Stover, Juliana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Freiberg Hoffmann, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: de la Iglesia, Guadalupe. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fernandez Liporace, Maria Mercedes. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
ACADEMIC ACHIEVEMENT
LEARNING STRATEGIES
MOTIVATION
STUDENTS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/34453
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Predicting academic achievement: The role of Motivation and Learning StrategiesStover, Juliana BeatrizFreiberg Hoffmann, Agustínde la Iglesia, GuadalupeFernandez Liporace, Maria MercedesACADEMIC ACHIEVEMENTLEARNING STRATEGIESMOTIVATIONSTUDENTShttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5The aim of this study consists in testing a predictive model of academic achievement including motivation and learning strategies as predictors. Motivation is defined as the energy and the direction of behaviors; it is categorized in three types of motivation –intrinsic, extrinsic and amotivation (Deci & Ryan, 1985). Learning strategies are deliberate operations oriented towards information processing in academic activities (Valle, Barca, González & Núñez, 1999). Several studies analysed the relationship between motivation and learning strategies in high school and college environments. Students with higher academic achievement were intrinsically motivated and used a wider variety of learning strategies more frequently. A non-experimental predictive design was developed. The sample was composed by 459 students (55.2% high-schoolers; 44.8% college students). Data were gathered by means of sociodemographic and academic surveys, and also by the local versions of the Academic Motivation Scale –EMA, Echelle de Motivation en Éducation (Stover, de la Iglesia, Rial Boubeta & Fernández Liporace, 2012; Vallerand, Blais, Briere & Pelletier, 1989) and the Learning and Study Strategies Inventory –LASSI (Stover, Uriel & Fernández Liporace, 2012; Weinstein, Schulte & Palmer, 1987). Several path analyses were carried out to test a hypothetical model to predict academic achievement (Kline, 1998). Results indicated that self-determined motivation explained academic achievement through the use of learning strategies. The final model obtained an excellent fit (χ2=16.523, df= 6, p=0.011; GFI=0.987; AGFI=0.955; SRMR=0.0320; NFI=0.913; IFI=0.943; CFI=0.940). Results are discussed considering Self Determination Theory and previous research.Fil: Stover, Juliana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Freiberg Hoffmann, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: de la Iglesia, Guadalupe. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fernandez Liporace, Maria Mercedes. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaScientific Methodical Center "Scientia Educologica"2014-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/34453Stover, Juliana Beatriz; Freiberg Hoffmann, Agustín; de la Iglesia, Guadalupe; Fernandez Liporace, Maria Mercedes; Predicting academic achievement: The role of Motivation and Learning Strategies; Scientific Methodical Center "Scientia Educologica"; Problems of Psychology in the 21st Century; 8; 1; 2-2014; 71-842029-8587CONICET DigitalCONICETenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:45:57Zoai:ri.conicet.gov.ar:11336/34453instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:45:57.838CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predicting academic achievement: The role of Motivation and Learning Strategies
title Predicting academic achievement: The role of Motivation and Learning Strategies
spellingShingle Predicting academic achievement: The role of Motivation and Learning Strategies
Stover, Juliana Beatriz
ACADEMIC ACHIEVEMENT
LEARNING STRATEGIES
MOTIVATION
STUDENTS
title_short Predicting academic achievement: The role of Motivation and Learning Strategies
title_full Predicting academic achievement: The role of Motivation and Learning Strategies
title_fullStr Predicting academic achievement: The role of Motivation and Learning Strategies
title_full_unstemmed Predicting academic achievement: The role of Motivation and Learning Strategies
title_sort Predicting academic achievement: The role of Motivation and Learning Strategies
dc.creator.none.fl_str_mv Stover, Juliana Beatriz
Freiberg Hoffmann, Agustín
de la Iglesia, Guadalupe
Fernandez Liporace, Maria Mercedes
author Stover, Juliana Beatriz
author_facet Stover, Juliana Beatriz
Freiberg Hoffmann, Agustín
de la Iglesia, Guadalupe
Fernandez Liporace, Maria Mercedes
author_role author
author2 Freiberg Hoffmann, Agustín
de la Iglesia, Guadalupe
Fernandez Liporace, Maria Mercedes
author2_role author
author
author
dc.subject.none.fl_str_mv ACADEMIC ACHIEVEMENT
LEARNING STRATEGIES
MOTIVATION
STUDENTS
topic ACADEMIC ACHIEVEMENT
LEARNING STRATEGIES
MOTIVATION
STUDENTS
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv The aim of this study consists in testing a predictive model of academic achievement including motivation and learning strategies as predictors. Motivation is defined as the energy and the direction of behaviors; it is categorized in three types of motivation –intrinsic, extrinsic and amotivation (Deci & Ryan, 1985). Learning strategies are deliberate operations oriented towards information processing in academic activities (Valle, Barca, González & Núñez, 1999). Several studies analysed the relationship between motivation and learning strategies in high school and college environments. Students with higher academic achievement were intrinsically motivated and used a wider variety of learning strategies more frequently. A non-experimental predictive design was developed. The sample was composed by 459 students (55.2% high-schoolers; 44.8% college students). Data were gathered by means of sociodemographic and academic surveys, and also by the local versions of the Academic Motivation Scale –EMA, Echelle de Motivation en Éducation (Stover, de la Iglesia, Rial Boubeta & Fernández Liporace, 2012; Vallerand, Blais, Briere & Pelletier, 1989) and the Learning and Study Strategies Inventory –LASSI (Stover, Uriel & Fernández Liporace, 2012; Weinstein, Schulte & Palmer, 1987). Several path analyses were carried out to test a hypothetical model to predict academic achievement (Kline, 1998). Results indicated that self-determined motivation explained academic achievement through the use of learning strategies. The final model obtained an excellent fit (χ2=16.523, df= 6, p=0.011; GFI=0.987; AGFI=0.955; SRMR=0.0320; NFI=0.913; IFI=0.943; CFI=0.940). Results are discussed considering Self Determination Theory and previous research.
Fil: Stover, Juliana Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: Freiberg Hoffmann, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina
Fil: de la Iglesia, Guadalupe. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Fernandez Liporace, Maria Mercedes. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description The aim of this study consists in testing a predictive model of academic achievement including motivation and learning strategies as predictors. Motivation is defined as the energy and the direction of behaviors; it is categorized in three types of motivation –intrinsic, extrinsic and amotivation (Deci & Ryan, 1985). Learning strategies are deliberate operations oriented towards information processing in academic activities (Valle, Barca, González & Núñez, 1999). Several studies analysed the relationship between motivation and learning strategies in high school and college environments. Students with higher academic achievement were intrinsically motivated and used a wider variety of learning strategies more frequently. A non-experimental predictive design was developed. The sample was composed by 459 students (55.2% high-schoolers; 44.8% college students). Data were gathered by means of sociodemographic and academic surveys, and also by the local versions of the Academic Motivation Scale –EMA, Echelle de Motivation en Éducation (Stover, de la Iglesia, Rial Boubeta & Fernández Liporace, 2012; Vallerand, Blais, Briere & Pelletier, 1989) and the Learning and Study Strategies Inventory –LASSI (Stover, Uriel & Fernández Liporace, 2012; Weinstein, Schulte & Palmer, 1987). Several path analyses were carried out to test a hypothetical model to predict academic achievement (Kline, 1998). Results indicated that self-determined motivation explained academic achievement through the use of learning strategies. The final model obtained an excellent fit (χ2=16.523, df= 6, p=0.011; GFI=0.987; AGFI=0.955; SRMR=0.0320; NFI=0.913; IFI=0.943; CFI=0.940). Results are discussed considering Self Determination Theory and previous research.
publishDate 2014
dc.date.none.fl_str_mv 2014-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/34453
Stover, Juliana Beatriz; Freiberg Hoffmann, Agustín; de la Iglesia, Guadalupe; Fernandez Liporace, Maria Mercedes; Predicting academic achievement: The role of Motivation and Learning Strategies; Scientific Methodical Center "Scientia Educologica"; Problems of Psychology in the 21st Century; 8; 1; 2-2014; 71-84
2029-8587
CONICET Digital
CONICET
url http://hdl.handle.net/11336/34453
identifier_str_mv Stover, Juliana Beatriz; Freiberg Hoffmann, Agustín; de la Iglesia, Guadalupe; Fernandez Liporace, Maria Mercedes; Predicting academic achievement: The role of Motivation and Learning Strategies; Scientific Methodical Center "Scientia Educologica"; Problems of Psychology in the 21st Century; 8; 1; 2-2014; 71-84
2029-8587
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Scientific Methodical Center "Scientia Educologica"
publisher.none.fl_str_mv Scientific Methodical Center "Scientia Educologica"
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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