Examining students’ achievement in mathematics: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data for Greece

Karakolidis, Anastasios; Pitsia, Vasiliki; Emvalotis, Anastassios

doi:10.1016/j.ijer.2016.05.013

Cited by 56 publications

(62 citation statements)

References 39 publications

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“…Es bien sabido que el nivel socioeconómico (NSE) incide de forma directa en el logro escolar (Woitschach, Fernández-Alonso, Martínez-Arias & Muñiz, 2017). Vale la pena tener presente que el hecho de que estudiantes con diferente NSE, no tienen las mismas posibilidades de éxito en las evaluaciones, revela la inequidad social del sistema educativo (Gamboa & Waltenberg, 2015;Karakolidis, Pitsia & Emvalotis, 2016). Situación que es particularmente más notoria en los países de Latinoamérica (Breton & Canavire-Bacarreza, 2017;Deutsch, 2012, Woitshach et al, 2017, en donde existe una amplia segregación que incide de manera directa en los resultados académicos de los estudiantes (Krüger, 2017).…”

Section: Variables Asociadas Al Logro Educativo En Pisaunclassified

“…Situación que es particularmente más notoria en los países de Latinoamérica (Breton & Canavire-Bacarreza, 2017;Deutsch, 2012, Woitshach et al, 2017, en donde existe una amplia segregación que incide de manera directa en los resultados académicos de los estudiantes (Krüger, 2017). Al respecto, Rodríguez (2005) señaló que las variables de NSE explican más varianza principalmente en los países en los que existe mayor desigualdad social, y, por el contrario, en países con brechas más angostas de desigualdad, son las variables psicológicas las que toman mayor fuerza (Arikan, 2014;Karakolidis et al, 2016;Ortiz, 2013;Özdemir, 2016). Sin embargo, un nivel socioeconómico alto también puede tener un efecto negativo, Schindler (2007) encontró que, en los estudiantes daneses, un mayor NSE se asociaba a menores puntuaciones en Lectura.…”

Section: Variables Asociadas Al Logro Educativo En Pisaunclassified

“…Además del nivel socioeconómico, existen otras características de índole personal que impactan los resultados en PISA. Entre las más citadas se encuentran el género (Deutsch, 2013;Gamazo, Martínez-Abad, Olmos-Miguelañez & Rodríguez Conde, 2018;Karakolidis et al, 2016;Kasapoglu, 2014;Özdemir, 2016;Stoet & Geary, 2014), el estatus migratorio (Gamazo et al, 2018;Karakolidis et al, 2016), la asistencia a educación preescolar (Karakolidis et al, 2016, Ortiz, 2013, la condición de repetidor (Choi & Calero, 2013;Ehmke, Drechsel & Carstensen, 2008;Gamazo et al, 2018;Giménez & Castro, 2017) el número de veces que ha cambiado de escuela y el historial académico previo (Gamazo et al, 2018).…”

Section: Variables Asociadas Al Logro Educativo En Pisaunclassified

“…Sin embargo, su revisión ayuda a comprender el efecto que la escuela tiene en las puntuaciones de los estudiantes. Resulta pertinente iniciar mencionando los resultados del estudio de Karakolidis et al (2016) quienes mostraron que cerca del 40% de la varianza del logro, es atribuida a las diferencias entre las escuelas y 60% a la diferencia dentro de las escuelas. Es decir, lo que sucede dentro de la escuela tiene un gran peso explicativo en el logro de los estudiantes.…”

Section: Variables De Eficacia Escolar Asociadas a Las Puntuaciones Eunclassified

“…Este rubro hace referencia a las acciones de organización y gestión que se llevan a cabo dentro de la escuela o el aula. Por ejemplo, en cuanto a la gestión del tiempo, Anil (2011) encontró una correlación lineal positiva entre el tiempo dedicado al aprendizaje y el logro en ciencias; asimismo, Karakolidis et al (2016) mostraron que esta variable también se asocia al logro en matemáticas. Otro aspecto importante arrojado por la investigación es la implicación de los padres de familia y el uso de los resultados de las evaluaciones como insumo para el establecimiento de estrategias de mejora (Camminatiello et al, 2012).…”

Section: Variables De Eficacia Escolar Asociadas a Las Puntuaciones Eunclassified

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Estudio multinivel sobre las variables explicativas de los resultados de México en PISA 2015

López

Gamazo

2020

EPAA

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This study aims to analyze which student- and school-level context factors have a significant relationship with student achievement in the three competences assessed by PISA 2015 (Reading, Mathematics and Science) in Mexico. Through the use of multilevel modelling, we analyzed data from over 6 700 students from 189 schools, concluding that the factors with a higher impact on achievement are the average socioeconomic level of the school, having repeated a grade, and the grade the student is in at the time of the assessment. Likewise, through a logistic regression, the process variables related to school effectiveness were assessed, finding that only the following school-level ones had a significant impact: Teacher participation, Teacher support in science class, Teacher-directed science instruction.

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Section: Variables Asociadas Al Logro Educativo En Pisaunclassified

Section: Variables De Eficacia Escolar Asociadas a Las Puntuaciones Eunclassified

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Estudio multinivel sobre las variables explicativas de los resultados de México en PISA 2015

López

Gamazo

2020

EPAA

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Academic self‐attributions for success and failure in mathematics and school refusal

Gonzálvez

Sanmartín

Vicent

et al. 2018

Psychology in the Schools

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The aim of this research is twofold: to analyze the mean differences scores in mathematic self-attributions based on school refusal and to verify its predictive capability on high scores in school refusal. The Sydney Attribution Scale and the School Refusal Assessment Scale-Revised were administered to 1078 Spanish students (50.8% boys) aged between 8 and 11 years (M = 9.63; SD = 1.12). School refusers based on negative affect or anxiety attributed their failures more to the lack of capacity and effort, whereas students searching for tangible reinforcements outside of the school were more likely to attribute their successes to capacity. In turn, academic attributions acted as both positive and negative predictors of high-school refusal.Results are discussed considering more adaptive attributional styles. K E Y W O R D Sacademic self-attributions, mathematics, school refusalThe interest in determining the results of academic performance on a massive scale through international assessments began during the mid-20th century. The International Association for the Evaluation of Educational Achievement initiated these reports and mathematics was the first domain to be assessed. Today, the Program for International Student Assessment is one of the principal world scales used to evaluate the quality, equity, and efficiency of the educational systems in the areas of mathematics, sciences, and reading. More and more countries are participating in this study.In its first edition, held in 2000, 43 countries participated, while in the last edition of 2015, 72 countries from the five continents participated, including the 34 belonging to the Organization for Economic Co-operation and Development (OECD). In the latest reports produced by this organization, it has been noted that the academic performance of Spanish children has improved slightly in sciences and language, although it is below the OECD average for mathematics (OECD, 2012(OECD, , 2015. made to educational professionals: (1) to introduce a favorable working atmosphere in their day-to-day practice; (2) to foster the attribution of achievements to internal, unstable, and controllable factors such as effort, as opposed to external factors such as fate and luck; and (3) and to improve the students' level of aspiration by promoting attributions to the effort exerted and by maintaining appropriate expectations based on their level. ORCID Carolina Gonzálvez

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A Bivariate Multilevel Analysis of Portuguese Students

Faria

Salgado

2020

Computational Science and Its Applications – ICCSA 2020

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In this work, we illustrate how to perform a bivariate multilevel analysis in the complex setting of large-scale assessment surveys. The purpose of this study was to identify a relationship between students’ mathematics and science test scores and the characteristics of students and schools themselves. Data on about 7325 Portuguese students and 246 Portuguese schools who participated in PISA-2015 were used to accomplish our objectives. The results obtained by this approach are in line with the existing research: the index of the socioeconomic status of the student, being a male student and the average index of the socioeconomic status of the student in school, positively influence the students’ performance in mathematics and science. On the other hand, the grade repetition had a negative influence on the performance of the Portuguese student in mathematics and science.

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Examining students’ achievement in mathematics: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data for Greece

Cited by 56 publications

References 39 publications

Estudio multinivel sobre las variables explicativas de los resultados de México en PISA 2015

Estudio multinivel sobre las variables explicativas de los resultados de México en PISA 2015

Academic self‐attributions for success and failure in mathematics and school refusal

A Bivariate Multilevel Analysis of Portuguese Students

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