Evolución de los predictores contextuales del nivel competencial de las y los estudiantes españolesUn estudio comparativo entre PISA 2015 y 2018
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Universidad de Salamanca
info
ISSN: 0212-4068, 1989-9106
Any de publicació: 2024
Volum: 42
Número: 2
Tipus: Article
Altres publicacions en: Revista de investigación educativa, RIE
Resum
The Programme for International Student Assessment (PISA) has been assessing student competence levels for over 20 years, while also influencing the implementation of educational policies and practices based on its results at an international level. Although PISA’s configuration does not allow for longitudinal studies, this paper proposes the design of a trend study that enables the assessment of the evolution of the sociodemographic and educational context factors that best predict student competence levels. Through a multilevel regression analysis (hierarchical linear models) with the Spanish sample from PISA 2015 and 2018 waves, consisting of 65684 students and 1873 schools, the changes in variables predicting student performance in reading, math and science can be observed. The most noteworthy findings are the reduction of the impact of migration status for first generation immigrant students, the narrowing of the gender gap in STEM subjects (and its widening in reading), or the decrease of the contextual effect of the average socioeconomic level of students in a school. The paper concludes with the need to perform deeper analyses, both at statistical and educational policy levels, to produce more detailed results that shed light on which measures are more useful in order to reduce the impact of socioeconomic, demographic and educational context factors on Spanish students’ performance.
Referències bibliogràfiques
- Bisquerra, R. (2004). Metodología de la investigación educativa. La Muralla.
- Carabaña, J. (2015). La inutilidad de PISA para las escuelas. Los Libros de la Catarata, D.L.
- Creemers, B. P. M. y Kyriakides, L. (2008). The Dynamics of Educational Effectiveness: A contribution to policy, practice and theory in contemporary schools. Routledge. https://doi.org/10.4324/9780203939185
- Cohen, L., Manion, L., y Morrison, K. (2017). Surveys, longitudinal, cross-sectional and trend studies. In Research methods in education (pp. 334-360). Routledge. https://doi.org/10.4324/9781315456539
- de Miguel, M. (1985). Estrategias metodológicas en los estudios longitudinales. Revista de Investigación Educativa, 3(6), 252-270. Pearson.
- Ding, H. y Homer, M. (2020). Interpreting mathematics performance in PISA: Taking account of reading performance. International Journal of Educational Research, 102, 101566. https://doi.org/10.1016/j.ijer.2020.101566
- Doncel Abad, D. y Cabrera Álvarez, P. (2020). Comunidades Autónomas bilingües, identidades y desempeño educativo según PISA 2015. Revista de educación, 387, 163-188. https://doi.org/10.4438/1988-592X-RE-2020-387-443
- Dumay, X. y Dupriez, V. (2014). Educational quasi-markets, school effectiveness and social inequalities. Journal of Education Policy, 29(4), 510-531. https://doi.org/10.1080/02680939.2013.850536
- Gamazo, A. y Martínez-Abad, F. (2020). An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.575167
- Gamazo, A., Martínez-Abad, F., Olmos-Migueláñez, S., y Rodríguez-Conde, M. J. (2018). Evaluación de factores relacionados con la eficacia escolar en PISA 2015. Un análisis multinivel. Revista de educación, 379, 56-84.
- Gaviria Soto, J. L. y Castro Morera, M. (2005). Modelos jerárquicos lineales. La Muralla.
- Gómez-Fernández, N. y Mediavilla, M. (2021). Exploring the relationship between Information and Communication Technologies (ICT) and academic performance: A multilevel analysis for Spain. Socio-Economic Planning Sciences, 77, 101009. https://doi.org/10.1016/j.seps.2021.101009
- Han, S. W. (2018). School-based teacher hiring and achievement inequality: A comparative perspective. International Journal of Educational Development, 61, 82-91. https://doi.org/10.1016/j.ijedudev.2017.12.004
- Hayes, A. F. (2006). A Primer on Multilevel Modeling. Human Communication Research, 32(4), 385-410. https://doi.org/10.1111/j.1468-2958.2006.00281.x
- Hu, X., Gong, Y., Lai, C., y Leung, F. K. S. (2018). The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis. Computers & Education, 125, 1-13. https://doi.org/10.1016/j.compedu.2018.05.021
- Joaristi, L., Lizasoain Hernández, L., y Azpillaga, V. (2014). Detección y caracterización de los centros escolares de alta eficacia de la Comunidad Autónoma del País Vasco mediante Modelos Transversales Contextualizados y Modelos Jerárquicos Lineales. Estudios sobre educación, 27, 37-61.
- Jornet Meliá, J. M. (2016). Análisis metodológico del proyecto PISA como evaluación internacional. RELIEVE. Revista Electrónica De Investigación y Evaluación Educativa, 22(1), 1-26. https://doi.org/10.7203/relieve22.1.8293
- Kameshwara, K. K., Sandoval-Hernandez, A., Shields, R., y Dhanda, K. R. (2020). A false promise? Decentralization in education systems across the globe. International Journal of Educational Research, 104, 101669. https://doi.org/10.1016/j.ijer.2020.101669
- Kyriakides, L., Creemers, B. P. M., y Antoniou, P. (2009). Teacher behaviour and student outcomes: Suggestions for research on teacher training and professional development. Teaching and Teacher Education, 25(1), 12-23. https://doi.org/10.1016/j.tate.2008.06.001
- Lafontaine, D., Baye, A., Vieluf, S., y Monseur, C. (2015). Equity in opportunity-to-learn and achievement in reading: A secondary analysis of PISA 2009 data. Studies in Educational Evaluation, 47, 1-11. https://doi.org/10.1016/j.stueduc.2015.05.001
- Laukaityte, I. y Rolfsman, E. (2020). Low, medium, and high-performing schools in the Nordic countries. Student performance at PISA Mathematics 2003-2012. Education Inquiry, 11(3), 276-295. https://doi.org/10.1080/20004508.2020.1721256
- Lee, V. E. (2000). Using Hierarchical Linear Modeling to Study Social Contexts: The Case of School Effects. Educational Psychologist, 35(2), 125-141. https://doi.org/10.1207/S15326985EP3502_6
- Lenkeit, J. (2012). How effective are educational systems? A value-added approach to study trends in PIRLS. Journal of Educational Research Online, 4(2),143-173. https://hdl.handle.net/11245/1.405026
- Li, H. (2016). How is formative assessment related to students’ reading achievement? Findings from PISA 2009. Assessment in Education: Principles, Policy & Practice, 23(4), 473-494. https://doi.org/10.1080/0969594X.2016.1139543
- Martínez-Abad, F. (2019). Identification of Factors Associated With School Effectiveness With Data Mining Techniques: Testing a New Approach. Frontiers in Psychology, 10, 2583. https://doi.org/10.3389/fpsyg.2019.02583
- Martínez-Abad, F., Gamazo, A., y Rodriguez-Conde, M.-J. (2020). Educational Data Mining: Identification of factors associated with school effectiveness in PISA assessment. Studies in Educational Evaluation, 66, 100875. https://doi.org/10.1016/j.stueduc.2020.100875
- Martínez-Abad, F., Lizasoain, L., Castro, M., y Joaristi, L. M. (2017). Selección de escuelas de alta y baja eficacia en Baja California (México). REDIE: Revista Electrónica de Investigación Educativa, 19(2), 38-53. https://doi.org/10.24320/redie.2017.19.2.960
- Meunier, M. (2011). Immigration and student achievement: Evidence from Switzerland. Economics of Education Review, 30(1), 16-38.
- Molina Portillo, E., Contreras García, J. M., Molina Muñoz, D., y Sánchez Pelegrín, J. A. (2022). Estudio por género del impacto de factores contextuales en el rendimiento matemático del alumnado español en PISA 2018. Revista Complutense de Educación, 33(4), 645-656. https://doi.org/10.5209/rced.76428
- OECD. (2006). El programa PISA de la OCDE: Qué es y para qué sirve. http://iice.institutos.filo.uba.ar/el-programa-pisa-de-la-ocde-qu%C3%A9-es-y-para-qu%C3%A9-sirve
- OECD. (2019). PISA 2018 Technical Report (p. 468).
- Pomianowicz, K. (2021). Educational achievement disparities between second-generation and non-immigrant students: Do school characteristics account for tracking effects? European Educational Research Journal, 22(3), 297-324. https://doi.org/10.1177/14749041211039929
- Raudenbush, S. W. y Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE.
- Rutkowski, D., Rutkowski, L., Wild, J., y Burroughs, N. (2018). Poverty and educational achievement in the US: A less-biased estimate using PISA 2012 data. Journal of Children and Poverty, 24(1), 47-67. https://doi.org/10.1080/10796126.2017.1401898
- Scheerens, J., Luyten, H., van den Berg, S. M., y Glas, C. A. W. (2015). Exploration of direct and indirect associations of system-level policy-amenable variables with reading literacy performance. Educational Research and Evaluation, 21(1), 15-39. https://doi.org/10.1080/13803611.2015.1008520
- Sirin, S. (2005). Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. Review of Educational Research, 75. https://doi.org/10.3102/00346543075003417
- Snijders, T. A. B. y Bosker, R. J. (2011). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. SAGE.
- Sullivan, A. y Calderwood, L. (2017). Surveys: longitudinal, cross-sectional, and trend studies. The BERA/SAGE Handbook of Educational Research: Two Volume Set (Vol.2, pp.395-415). SAGE Publications Ltd. https://doi.org/10.4135/9781473983953
- Willms, J. D. (2010). School Composition and Contextual Effects on Student Outcomes. Teachers College Record, 112(4), 1008-1037.
- Wiseman, A. W. (2013). Policy responses to PISA in comparative perspective. En H. D. Meyer y A. Benavot (Eds.), PISA, power, and policy: The emergence of global educational governance (pp. 303-322). Symposium Books.
- Wu, H., Shen, J., Zhang, Y., y Zheng, Y. (2020). Examining the effect of principal leadership on student science achievement. International Journal of Science Education, 42(6), 1017-1039. https://doi.org/10.1080/09500693.2020.1747664
- Wu, M. (2005). The role of plausible values in large-scale surveys. Studies in Educational Evaluation, 31(2), 114-128. https://doi.org/10.1016/j.stueduc.2005.05.005
- Yetişir, M. y Bati, K. (2021). The Effect of School and Student-Related Factors on PISA 2015 Science Performances in Turkey. International Journal of Psychology and Educational Studies, 8, 170-186. https://doi.org/10.52380/ijpes.2021.8.2.433