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Variables that Predict Academic Achievement in the Spanish Compulsory Secondary Educational System: A Longitudinal, Multi-Level Analysis

Published online by Cambridge University Press:  10 April 2014

Elena Martín ,
Rosario Martínez-Arias ,
Alvaro Marchesi  and
Eva M. Pérez
Elena Martín*
Affiliation:
Universidad Autónoma de Madrid (Spain)
Rosario Martínez-Arias
Affiliation:
Universidad Complutense (Spain)
Alvaro Marchesi
Affiliation:
Universidad Complutense (Spain)
Eva M. Pérez
Affiliation:
Universidad Complutense (Spain)
*
Correspondence concerning this article should be addressed to Elena Martín Ortega, Departamento de Psicología Evolutiva y de la Educación, Universidad Autónoma de Madrid, 28749 Madrid. Phone: 34 914975176. FAX: 34 914973268. E-mail: elena.martin@uam.es
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Abstract

This article presents a study whose objective was to identify certain personal and institutional variables that are associated with academic achievement among Spanish, secondary school students, and to analyze their influence on the progress of those students over the course of that stage of their education. In order to do this, a longitudinal, multi-level study was conducted in which a total of 965 students and 27 different schools were evaluated in Language, Math and Social Science at three different times (beginning, middle and end of the period). The results show progress in all the schools and in all areas. As for the personal, student variables, the longitudinal, HLM analyses confirmed the importance of sex and sociocultural background and, distinguishing it from other studies, also the predictive capacity of meta-cognitive abilities and learning strategies on success in school. On the institutional level, the school climate and teachers' expectations of their students were the most relevant of the variables studied. The size of the school, the percentage of students who repeat grades, and the leadership of the administration also explained a portion of the variance in some areas.

En el artículo se presenta un estudio cuyo objetivo es identificar determinadas variables personales y de centro asociadas con el rendimiento académico de estudiantes de secundaria españoles y analizar su influencia en el progreso de los alumnos a lo largo de la esta etapa. Para ello, Se realizó un estudio multinivel longitudinal en el que se evaluó a un total de 965 estudiantes de 27 centros distintos en Lengua, Matemáticas y Ciencias Sociales, en tres momentos (inicio, mitad y final de la etapa). Los resultados mostraron progreso en el conjunto de los centros en todas las áreas. Los análisis HLM longitudinales confirmaron en el nivel personal la importancia del sexo y el nivel sociocultural y, a diferencia de otros estudios, también la capacidad predictiva de las habilidades metacognitivas y las estrategias de aprendizaje. En el nivel de escuela, el clima escolar y las expectativas del profesorado hacia los estudiantes fueron las variables más relevantes. El tamaño del centro, el porcentaje de repetidores y el liderazgo del equipo directivo explicaron también una proporción de la varianza en algunas áreas.

Keywords

academic achievementpredictor variablessecondary educationlongitudinal multi-level analysisschool effectivenessrendimiento escolarvariables predictoraseducación secundariaanálisis mutinivel longitudinaleficacia de la escuela
Type
Articles
Information
The Spanish Journal of Psychology , Volume 11 , Issue 2 , November 2008 , pp. 400 - 413
Copyright
Copyright © Cambridge University Press 2008

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References

American Educational Research Association, American Psychological Association, National Council on Measurement in Education (AERA, APA, NCME) (1999). Standards for educational and psychological testing. Washington, DC: American Educational Research Association, American Psychological Association, National Council on Measurement in Education.Google Scholar
Ayala, C., Martínez Arias, R., & Yuste, C. (2004). CEAM. Cuestionario de estrategias de aprendizaje y motivación. Madrid: EOS.Google Scholar
Beaton, A.E., Martin, M.O., Mullis, I.V.S., Gonzales, E.J., Smith, T.A., & Kelly, D.L. (1996). Science achievement in the middle school years. IEA's Third International Mathematics and Science Study (TIMSS), Chestnut Hill, MA: Boston College, Center for the Study of Testing, Evaluation, and Educational Policy.Google Scholar
Braun, H. (2005). Using student progress to evaluate teachers: A primer on value-added models. Princeton, NJ: Educational Testing Service.Google Scholar
Braun, H., Jenkins, F., & Grigg, W. (2006). Comparing private schools and public schools using hierarchical linear modeling (NCES 2006-461). U.S. Department of Education, National Center for Education Statistics, Institute of Education Sciences. Washington, DC: U.S. Government Printing Office.Google Scholar
Coleman, J. S., Hoffer, T. B., & Kilgore, S. (1982). High school achievement: Public, Catholic, and other private schools compared. New York: Basic Books.Google Scholar
Corten, R., & Dronkers, J. (2006). School achievement of pupils from the lower strata in public, private government-dependent and private government-independent schools: A cross-national test of the Coleman-Hoffer thesis. Educational Research and Evaluation, 12, 179–208.CrossRefGoogle Scholar
De Fraine, B., Van Damme, J., & Onghena, P. (2002) The effect of school and classes upon language achievement. Unpublished manuscript, K.U. Leuven, Secondary and Higher Education Research Center, Leuven, Belgium.Google Scholar
DeSeCO (2005): The definition and selection of key competencies. Executive Summary. OECD.Google Scholar
EURYDICE (2002). Key competencies. A developing concept in general compulsory education. Retrieved April 25, 2006, at: http://www.eurydice.org/Documents/survey5/en/FrameSet.htmGoogle Scholar
European Commision (2004). Competencias clave para un aprendizaje a lo largo de la vida. Un marcode referencia europeo. Working program «Educación y Formación 2010». Grupo de trabajo B. «Competencias clave». E.C. General Direction for Education and Culture. Retrieved May, 1, 2006, at: http://www.educastur.princast.es/info/calidad/indicadores/doc/comision_europea.pdfGoogle Scholar
Freiberg, J.H. (1999). School Climate. Londres: Falmer Press.Google Scholar
Goldstein, H. (2003). Multilevel statistical models. London: Arnold.Google Scholar
Goldstein, J., & Behuniak, P. (2005). Practical assessment, research, and evaluation, Practical Assessment Research & Evaluation, 11, 1–17.Google Scholar
González Nieto, L. (2002). El aprendizaje de la lengua. In Marchesi, A. & Martín, E. (Eds.), Evaluación de la Educación Secundaria. Fotografía de una etapa polémica (pp. 179–196). Madrid: Editorial SM.Google Scholar
Haahr, J.H., Nielsen, T.K., Hansen, M.E., & Jakobsen, S.T. (2005). explaining student performance evidence from the international PISA, TIMSS and PIRLS surveys. Danish Technological Institute. Retrieved February 18, 2006, at http://ec.europa.eu/education/doc/reports/doc/basicskill.pdf.Google Scholar
Hambleton, R.K., & Swaminathan, H. (1987). Item response theory: Principles and applications. Boston, MA: Kluwer.Google Scholar
Hox, J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
INCE (1997). Resultados de matemáticas. Tercer estudio internacional de matemáticas y ciencias (TIMMS). Madrid: MEC-INCEGoogle Scholar
INECSE (2001). Diagnóstico del sistema educativo. La Evaluación de la educación secundaria 1997. Madrid: MEC-INECSEGoogle Scholar
INECSE (2003). Evaluación de la educación secundaria obligatoria 2000. Madrid: MEC-INECSE.Google Scholar
INECSE (2004). Aprender para el mundo del mañana. Resumen de resultados-PISA 2003. Madrid: MEC-INECSE.Google Scholar
INECSE (2005). Resultados en España del Estudio Pisa 2000. Madrid: MEC-INECSE.Google Scholar
Kolen, M.J., & Brennan, R.L. (2004). Test equating, scaling, and linking: Methods and practices. New York: Springer.CrossRefGoogle Scholar
Lissitz, R.W. (Ed.). (2005). Longitudinal and value-added modeling of student performance. Maple Grove, MN: JAM Press.Google Scholar
Lord, F.M. (1980). Applications of item response theory to practical testing problems. New Jersey: Lawrence Erlbaum Associates.Google Scholar
Martín, E. & Moreno, A. (2007). Competencia de aprender a aprender. Madrid: Alianza Editorial.Google Scholar
Marchesi, A. & Martín, E. (Eds.) (2002). Evaluación de la Educación Secundaria. Fotografía de una etapa polémica. Madrid: Editorial SM.Google Scholar
Marchesi, A. y Martínez Arias, R. (2006). Escuelas de éxito en España. Sugerencias e interrogantes a partir del Informe PISA, 2003. Madrid: Fundación SantillanaGoogle Scholar
Marchesi, A., Martínez Arias, R. & Martín, E. (2004). Estudio longitudinal sobre la influencia del nivel sociocultural en el aprendizaje de los alumnos en la Educación Secundaria Obligatoria. Infancia y Aprendizaje, 27(3), 307–323.CrossRefGoogle Scholar
Ministerio de Educación y Ciencia (2007). Las cifras de la educación en España. Estadísticas e Indicadores. Retrieved: July 25, 2007 at: http://www.mec.es/mecd/jsp/plantilla.jsp?id=3131&area=estadisticas&contenido=/estadisticas/educativas/cee/2006A/cee_2006A.htmlGoogle Scholar
Mislevy, R. J., & Bock, D. R. (1990). BILOG. Ítem analysis and test scoring with binary logistic models. Chicago: SSI-Scientific Software International.Google Scholar
Moreno, A. (2002). La evaluación de las habilidades metacognitivas. In Marchesi, A. & Martín, E. (Eds.), Evaluación de la Educación Secundaria. Fotografía de una etapa polémica (pp. 119–136). Madrid: Editorial SM.Google Scholar
Mortimore, P., Sammons, P., Stoll, L., Lewis, D., & Ecob, R. (1988). School matters: The junior years. Wells, UK: Open Books.Google Scholar
Mullis, I.V.S., Martin, M.O., Beaton, A. E., Gonzalez, E. J., Kelly, D. L., & Smith, T.A. (IEA) (1998). Mathematics and science achievement in the final years of secondary school: IEA's Third International Mathematics and Science Report. Chestnut Hill, MA: Boston College, Center for the Study of Testing, Evaluation, and Educational Policy.Google Scholar
OECD (2001). Knowledge and skills for life-first results from PISA 2000. Paris: OECD.Google Scholar
OECD (200). Learning for tomorrow's World. First results from PISA 2003. Paris: OECDGoogle Scholar
Opdenakker, M. C., Van Damme, J., De Fraile, B., Van Landeghem, G., & Onghena, P. (2002). The effect of school and classes mathematics achievement. School Effectiveness and School Improvement, 13, 339–427.CrossRefGoogle Scholar
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage.Google Scholar
Raudenbush, S. W., Bryk, A., Cheong, Y., & Congdon, R. (2004). HLM6: Hierarchical linear and non-linear modeling. Homewood, IL: Scientific Software International.Google Scholar
Raywid, M. (1997). Small schools: A reform that works. Educational leadership, 55, 34–39.Google Scholar
Rivière, V. (2002). El aprendizaje de las matemáticas. In Marchesi, A. & Martín, E. (Eds.), Evaluación de la Educación Secundaria. Fotografía de una etapa polémica (pp. 153–177). Madrid: Editorial SM.Google Scholar
Roca, E. (2002). El aprendizaje de las ciencias sociales. In Marchesi, A. & Martín, E. (Eds.), Evaluación de la Educación Secundaria. Fotografía de una etapa polémica (pp. 197–219). Madrid: Editorial SM.Google Scholar
Sellström, E., & Bremberg, S. (2006). Is there a “school effect” on pupil outcomes? A review of multilevel studies. Journal of Epidemiological Community Health, 60, 149–155.CrossRefGoogle Scholar
Stufflebeam, D. L., & Shinkfield, A. J. (1985). Systematic evaluation: A self-instructional guide to theory and practice. Boston: Kluwer-Nijhoff Publishing [Spanish translation: Evaluación sistemática. Guía teórica y práctica. Barcelona: Paidós- MEC, 1987].CrossRefGoogle Scholar
Teddlie, Ch., Reynolds, D., & Sammons, P. (2000). Context issues within school effectiveness research. In Reynolds, D. & Teddlie, Ch. (Eds.), The international handbook of school effectiveness research (pp. 134–159). London: Falmer Press.Google Scholar
Tiana, (2002) El context sociocultural en la evaluación de los centros educativos. In Marchesi, A. & Martín, E. (Eds.), Evaluación de la Educación Secundaria. Fotografía de una etapa polémica (pp. 61–76). Madrid: Editorial SM.Google Scholar
Van Damme, J., De Fraine, B.; Van Landeghem, G.; Opnedakker, M. (2002). A new study on Educational Effectiveness in Secondary Schools in Flanders: An introduction. School Effectiveness and School Improvement, 13(4), 383–397.CrossRefGoogle Scholar
Wilson, D.T., Wood, R., Kandola-Downs, P., & Gibbons, R. (1991). TESTFACT: Test scoring, item statistics, and item factor analysis. Chicago: SSI-Scientific Software.Google Scholar
Zvoch, K., & Stevens, J. (2006). Successive student cohorts and longitudinal growth models: An investigation of elementary school mathematics performance. Education Policy Analysis Archives, 14(2). Retrieved February 16, 2007 at: http://epaa.asu.edu/epaa/v14n2/.CrossRefGoogle Scholar