ERG SES C 02, PechaKucha Poster Session
We have recently seen a rising interest in the study of the factors associated to academic success, as it is proven by the growing number of large-scale studies that propose models for the identification of the variables that influence the students’ academic performance (Murillo & Román, 2011).
Academic performance is a multifaceted phenomenon that involves a lot of variables which are related to one another. Identifying these variables entails a complex task, mainly due to two reasons: their diversity and the relationships among them (Tejedor, 2003).
In the past few years, the use of standardised tests to assess the students’ performance has become a common practice in the education systems, and we can find this kind of tests at an international level, such as the PISA report, from the OECD, or the TIMSS and PIRLS tests, from the IEA, and, in many European countries, we can also find national and regional tests of this nature.
However, in order to properly analyse the results of these tests and to identify high- and low-performing schools, it is essential to identify and isolate the contextual factors to foster a more rigorous and equitable analysis based on the schools’ added value once the effect of the contextual variables has been controlled (Joaristi, Lizasoain & Azpillaga, 2014).
Due to their complexity, the study of these variables is conducted through a greatly diverse group of models and statistical procedures, aiming to adjust the methodology to the particular features of each study and its subject matter. The main models used to explore the impact of these variables on student performance are multiple regression, structural equation modelling, hierarchical linear models (multilevel), discriminant analysis or econometric techniques. Due to the nature of most large-scale assessments, in which the students are nested within a higher level (schools), multilevel models are suggested in order to avoid mistakes such as the overestimation of the statistical significance of the coefficients (Calero & Escárdibul, 2013).
At an international level there are many studies on this subject, such as the one conducted by Coleman et al. (2006), who focused on the relationship between student performance and school resources, Huffman, Pankake & Munoz’s study (2006), in which they use a three-level multilevel model (students, schools and districts), or the work of Bryk, Sebring, Allensworth, Luppescu & Easton (2010), who carried out a longitudinal study and developed their own value-added model to evaluate Chicago schools.
In Spain, this type of research has arisen in the past few years. A couple of remarkable projects on the subject are those conducted by Joaristi, Lizasoain & Azpillaga (2014), who intended to characterise the good practices implemented in high value-added schoolsthrough the use of contextualised transversal models, Marchesi and Martin’s study (2002), who evaluated the factors that determined the differences among Secondary Education schools, or the one conducted by Santiago, Lukas, Joaristi, Lizasoain y Moyano (2008), who carried out a longitudinal study to explore the effects of certain contextual factors in student performance.
Bryk, A. S., Sebring, P. B., Allensworth, E., Luppescu, S. & Easton, J. Q. (2010). Organizing Schools for Improvement: Lessons from Chicago. Chicago: University of Chicago Press. Calero, J. & Escardíbul, J.O. (2013). El rendimiento del alumnado de origen inmigrante en PISA-2012. PISA 2012. Informe Español. Volumen II. Análisis secundario. Madrid. Ministerio de Educación, Cultura y Deporte– Instituto Nacional de Evaluación Educativa. Coleman, J.S., Campbell, E.Q., Hobson, C.J., McPartland, J., Mood, A., Weinfield, F.D. & York, R.L. (1966). Equality of Educational Opportunity. Washington DC: Government Printing Office. Huffman, J. B., Pankake, A. & Munoz, A. (2006). The Tri-Level Model in Action: Site, District, and State Plans for School Accountability in Increasing Student Success. Journal of School Leadership, 16(5), 569-582. Joaristi, L., Lizasoain, L. & 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. Marchesi, A. & Martín, E. (2002). Evaluación de la educación secundaria: fotografía de una etapa polémica. Madrid: Fundación Santa María Murillo, F. J. & Román, M. (2011). ¿La escuela o la cuna? Evidencias sobre su aportación al rendimiento de los estudiantes de América Latina. Estudio multinivel sobre la estimación de los efectos escolares. Revista de Curriculum y Formación de Profesorado, 15(3), 27-53.. Santiago, K., Lukas, J. F., Joaristi, L., Lizasoain, L. & Moyano, N. (2008). A Longitudinal Study of Academic Achievement in Spanish: The Effect of Linguistic Models. Language, Culture and Curriculum, 21(1), 48-58. Tejedor, F.J. (2003). Poder explicativo de algunos determinantes del rendimiento en los estudios universitarios. Revista Española de Pedagogía, 224, 5-32.
00. Central Events (Keynotes, EERA-Panel, EERJ Round Table, Invited Sessions)
Network 1. Continuing Professional Development: Learning for Individuals, Leaders, and Organisations
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