The perception of stakeholders regarding the outcomes achieved by schools with high or low levels of effectiveness and high numbers of immigrant students

Abstract

The aim of this study was to characterize those schools in the Autonomous Community of the Basque Country (Spain) with high numbers of immigrant students in accordance with their effectiveness level (high or low). Three effectiveness criteria (scores, residuals, and times) were used to select the schools, resulting in three models: a ceiling or floor effect model, a contextualized cross-sectional model, and a contextualized longitudinal model. Of the 524 initial schools analyzed, 7 were found to have high or low effectiveness levels and higher-than-average immigration levels. Following the selection, the discourses of 40 education stakeholders were analyzed through semi-structured interviews and discussion groups. The results highlight the strong sense of vulnerability felt in some schools, as well as the general conviction that greater support, commitment, and stable, collaborative leadership by the management team and inspectorate would contribute to improving effectiveness.

Do more effective schools succeed in reducing the gender gap in linguistic communicative competence in a bilingual context? (¿Consiguen los centros más eficaces reducir la brecha de género en competencia comunicativa lingüística en un contexto bilingüe?)

“…The variables considered were controlled for in order to calculate the expected score for each school (residual value). The procedure is described in more detail below (Intxausti et al, 2021).…”

“…Las variables consideradas fueron controladas para calcular la puntuación esperada de cada escuela (valor residual). El procedimiento se describe de forma específica a continuación (Intxausti et al, 2021).…”

“…The variables considered were controlled for in order to calculate the expected score for each school (residual value). The procedure is described in more detail below (Intxausti et al, 2021). Before calculating the residual, to avoid possible errors or collinearity effects between the independent variables (Level 1 and Level 2), we performed a new regression analysis eliminating from the model, one by one, those variables not found to be statistically significant, starting with the variable with the highest significance score (p) and continuing on to that with the lowest significance score.…”

“…Las variables consideradas fueron controladas para calcular la puntuación esperada de cada escuela (valor residual). El procedimiento se describe de forma específica a continuación (Intxausti et al, 2021). Antes de calcular el residuo, para evitar posibles errores o efectos de colinealidad entre las variables independientes (N1 y N2), se ha ejecutado nuevamente el análisis de regresión extrayendo del modelo una por una las variables que no han resultado estadísticamente significativas, comenzando por la variable con mayor puntuación hasta llegar a la variable con menor puntuación en significatividad (p).…”