Session Information
05 SES 04 A, Teachers Addressing Disadvantage and Bullying
Paper Session
Contribution
The consequences of the COVID-pandemic highlight the importance of a continuous attention to educational inequalities. Worldwide the achievement gap between children in advantaged and disadvantaged positions has expanded even more since the start of 2020 (OECD, 2021). While background characteristics of disadvantaged students and their environment are often given as an explanation for this gap (Clycq, Nouwen, & Vandenbroucke, 2014), the consequences of the past school closures confirm the importance of schools and teachers in making a difference in learning (Hargreaves, 2021). Consequently, focusing on how schools and teachers reach out to all children is even more prominent. In an attempt to integrate school effectiveness research with regard to all students, Hoy and colleagues introduced the concept of academic optimism (Hoy, Tarter, & Woolfolk Hoy, 2006), which offers a fine-grained framework to study the impact of schools and teachers. Building on existing literature, academic optimism emphasizes a triadic set of interactions between efficacy, trust and academic emphasis and has a positive correlation with students’ performance, even after controlling for SES and other demographic variables (Hoy, 2012). A distinction is made between school and teacher academic optimism. School academic optimism (SAO) is a characteristic of the collectively shared school’s culture, represented in the degree of collective efficacy, the extent to which the team trusts students and their parents and the way in which academic achievement is promoted (Hoy et al., 2006).Teacher academic optimism (TAO) is described as an individual teacher’s positive attitude about their ability to teach, to build trusting relationships with students and parents, and to emphasize academic achievement (Woolfolk Hoy, Hoy, & Kurz, 2008). Its malleable nature and its empirically proven influence on the educational performance of all children make academic optimism a promising concept in the pursuit of schools creating equal opportunities.
However, important questions remain as to how school policy makers and educators can foster SAO and TAO, and in this paper, we particularly are interested into grasping the influence of composition elements. Indeed, previous research showed that SAO varies with the socioeconomic (Boonen, Pinxten, Van Damme, & Onghena, 2014) and ethnic (Wu & Lin, 2018) composition of the school. As Wu and Lin (2018) demonstrated that TAO on its turn is influenced by the degree of SAO, it is to be expected that TAO too will vary with these school conditions, through SAO. In addition, we assume TAO to be influenced by similar class compositional characteristics. In previous research, teachers were found to have different expectations with regard to students with lower SES or migration background (Wang, Rubie-Davies, & Meissel, 2018), and tend to perceive their relationships with these students to be of inferior quality (Belfi, Gielen, De Fraine, Verschueren, & Meredith, 2015). Within the reality of deficit thinking (Clycq et al., 2014), an optimistic attitude towards the academic possibilities of all students can be under pressure. To fully grasp the potential that academic optimism offers, we need to further unpack the relation between these composition elements and academic optimism. Promoting maximum learning opportunities for all students requires an holistic approach in which the influence of composition factors is known and taken into account (Van den Branden, Van Avermaet, & Van Houtte, 2011). This research will put teachers (within their schools) to the fore, as they are the first in line to make a difference in students’ learning. It therefore focuses on a combined view on the extent to which the subconcepts of TAO vary with the degree of SAO, considering the influence of school composition, and the extent to which the subconcepts of TAO vary with composition features of the class.
Method
Data is gathered in Antwerp, the largest city in Flanders (Belgium) and exemplary for the highly socioeconomically and ethnically segregated educational landscape. Stratified clustered systematic sampling based on voluntary response led to the participation of 1061 teachers from 37 secondary schools. The adapted (and validated) Survey for Academic Optimism was used (Lelieur, Clycq, & Vanhoof, 2021) to map out SAO and TAO. We also surveyed the compositional features of the class the respondents had in mind answering the TAO items (number of boys, lower SES students, first generation migrated students and second or third generation students). School composition characteristics (the number of students whose mother is low educated, students with school allowance and students with another home language) were collected from administrative data bases collected by the Flemish government. To start, descriptive statistics are calculated to give an overall view on the presence and variation of the variables. Subsequently, as SAO is a feature of the collectively shared school culture, aggregation at the school level is necessary for we are interested in the team's combined judgement of the degree of SAO present at their school. To justify this aggregation, we calculated the intraclass correlation coefficients: ICC1 and ICC2. Using a path model approach this study withholds multiple dependent and independent variables simultaneously. It examines the relationship between SAO, while controlling for the school compositional characteristics, and the subconcepts of TAO, considering the influence of similar class compositional features. Therefore, structural equation modelling is a favoured technique to analyse the possible relationships, as it is designed to evaluate the appropriateness of the proposed conceptual model (Nunkoo & Ramkissoon, 2012). Modification indices were used for refinement of the hypothesised model and we considered the comparative fit index (CFI, cut-off: .90), the root mean square error of approximation (RMSEA, cut-off: .08) and the standardized root mean square residual (SRMR, cut-off: .08) (Hooper, Coughlan, & Mullen, 2008), using the lavaan package (version 0.6-7) in R (Rosseel, 2012) to evaluate model fit. The model was estimated using full information maximum likelihood (FIML) to handle missing data (Schlomer, Bauman, & Card, 2010) and robust maximum likelihood estimation (MLR) to consider the nested structure of the data set (Stapleton, McNeish, & Yang, 2016).
Expected Outcomes
ICC demonstrate SAO to be a property of the school (ICC1=.15, ICC2=.79), which supports aggregation at the school level. After adding two conceptually defensible error covariances, fit indices for our hypothesised model were acceptable (CFI=.914, RMSEA=.079) to good (SRMR=.047). The school composition level variables have strong significant effects on SAO. Percentages of students’ mother’s educational degree (λ= -0.529, p < .001) and students’ school allowance (λ= -0.310, p < .001) have a negative influence, while students’ home language (λ= 0.490, p < .001) has a positive influence on SAO. And SAO in turn has a statistically significant positive effect on teacher efficacy (λ= 0.134, p < .01), teacher trust in students (λ= 0.301, p < .001) and teacher trust in parents (λ= 0.315, p < .001). In addition, teacher efficacy is influenced by the amount of second or third generation students in the studied class (λ= -0.085, p < .05). Teacher trust in students is influenced by the number of boys (λ= -0.137, p < .01), low SES students (λ= -0.123, p < .05), first generation migrated students (λ= 0.141, p < .01) and second or third generation students (λ= -0.102, p < .05). Teacher trust in parents is influenced by the amount of low SES students (λ= -0.262, p < .001) and second or third generation students (λ= -0.159, p < .001). And finally, teacher academic emphasis is influenced by the amount of second or third generation students (λ= -0.079, p < .05). These first results show the importance of understanding the effects of school and class composition on the degree of both SAO and TAO in developing maximum learning opportunities for all students. More results and conclusions will be discussed in the presentation.
References
Belfi, B., Gielen, S., De Fraine, B., Verschueren, K., & Meredith, C. (2015). School-based social capital: The missing link between schools’ socioeconomic composition and collective teacher efficacy. Teaching and Teacher Education, 45, 33–44. https://doi.org/10.1016/j.tate.2014.09.001 Boonen, T., Pinxten, M., Van Damme, J., & Onghena, P. (2014). Should schools be optimistic? An investigation of the association between academic optimism of schools and student achievement in primary education. Educational Research and Evaluation, 20(1), 3–24. https://doi.org/10.1080/13803611.2013.860037 Clycq, N., Nouwen, W. M. A., & Vandenbroucke, A. (2014). Meritocracy, deficit thinking and the invisibility of the system: Discourses on educational success and failure. British Educational Research Journal, 40(5), 796–819. https://doi.org/10.1002/berj.3109 Hargreaves, A. (2021). What the COVID-19 pandemic has taught us about teachers and teaching. Facets, 6, 1835–1863. https://doi.org/10.1139/facets-2021-0084 Hoy, W. (2012). School characteristics that make a difference for the achievement of all students: A 40-year odyssey. Journal of Educational Administration, 50(1), 76–97. https://doi.org/10.1108/09578231211196078 Hoy, W. K., Tarter, C. J., & Woolfolk Hoy, A. (2006). Academic optimism of schools: A second-order confirmatory factor analysis. In Wayne K. Hoy & C. Miskel (Eds.), Contemporary Issues in Educational Policy and School Outcomes (pp. 135–156). Greenwich, CT: Information Age. Lelieur, R., Clycq, N., & Vanhoof, J. (2021). Measuring School and Teacher Academic Optimism in Diverse School Contexts The Validation of the adapted Survey for Academic Optimism (aSAO). Antwerp: unpublished research report. OECD. (2021). The State of Global Education. 18 Months into the Pandemic. The State of Global Education. Paris. https://doi.org/10.4324/9781315862972 Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 36. https://doi.org/10.18637/jss.v048.i02 Van den Branden, K., Van Avermaet, P., & Van Houtte, M. (2011). Equity and Excellence in Education - Towards Maximal Learning Opportunities for All Students. (K. Van den Branden, P. Van Avermaet, & M. Van Houtte, Eds.). New York London: Routledge. Wang, S., Rubie-Davies, C. M., & Meissel, K. (2018). A systematic review of the teacher expectation literature over the past 30 years. Educational Research and Evaluation, 24(3–5), 124–179. https://doi.org/10.1080/13803611.2018.1548798 Woolfolk Hoy, A., Hoy, W. K., & Kurz, N. M. (2008). Teacher’s academic optimism: The development and test of a new construct. Teaching and Teacher Education, 24(4), 821–835. https://doi.org/10.1016/j.tate.2007.08.004 Wu, J. H., & Lin, C. Y. (2018). A multilevel analysis of teacher and school academic optimism in Taiwan elementary schools. Asia Pacific Education Review, 19(1), 53–62. https://doi.org/10.1007/s12564-017-9514-5
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