09 SES 08 A, Exploring Trends and Equity with PIRLS/TIMSS
The TIMSS 2015 results revealed an improved mathematics score for 8th graders in Sweden. However, the achievement gaps with respect to socioeconomic and ethnic background also have increased dramatically (Mullis, Martin, & Loveless, 2016). It is thus interesting and important to examine the factors that might be lying behind the observed trends in educational equity and quality.
A possible precursor of this period of fluctuation in achievement and the noted achievement gap is the influence of wide-ranging structural school reforms implemented since the late 1980s. These reforms, notably the free school choice policy, decentralization and deregulation, have transformed the school system creating a marketplace for providers and may have triggered changes in the characteristics in school compositions, in turn have an influence on student outcomes (Thrupp, 1995; Thrupp & Lupton, 2006). Another possible explanation is that changing opportunities to learn (OTL) have had differential effects on equity and efficiency. Many countries have revised their curricula, and other reforms, such as choice of schools have changed the social and ethnic composition of the schools. Changes in the learning and teaching environment may, furthermore, constrain or strengthen the OTL (e.g., Authors, 2016a; 2016b). In the study we focus on changes in OTL of mathematics content and SES between 1995 and 2015 in Sweden. We try to establish a causal link between the changing SES, OTL and the changing achievement gaps between unobserved subgroups of students.
This theoretical framework opportunity to learn (OTL) distinguishes between the prescribed curriculum, the taught curriculum, the assessed curriculum and the achieved curriculum. The prescribed curriculum is usually specified at the system level. The taught curriculum is at the classroom level. The assessed curriculum refers to the tasks included in the achievement tests, and the achieved curriculum refers to what individuals have learnt, as reflected in the assessment (e.g. Schmidt, Zoido & Cogan, 2014). The theoretical model also distinguishes between antecedents and contexts at three levels: the educational system level, the school/classroom level, and the student level. In the proposed study, we focus on the antecedents at the school/classroom context is determined by allocation of resources and translation of the prescribed curriculum into teaching and the antecedents at the student level are individual characteristics such as characteristics of the home, which influence the achieved curriculum. This multilevel model thus specifies direct and indirect effects on the achieved curriculum of the other factors in the model. According to the model, there are direct effects of OTL and student characteristics; while the factors at the school/classroom and educational system levels influence the achieved curriculum indirectly.
As mentioned before, the free choice of schools accompanied with other reforms in Sweden has changed the landscape of Swedish schools. As a consequence, the school mix in terms of student intake body’s characteristics and school resources differed largely across schools. We have good reason to assume that different groups of students and schools may have rather differentiated factor structure in the measurement and structural relations. The unobserved heterogeneity in the sample population need to be taken care of otherwise the estimates of effects will be biased.
Against this background, the paper seeks to answer the following questions:
1. How do SES and OTL impact mathematics achievement in TIMSS 1995 to 2015?
2. Does the factor structure of SES an OTL differ over some unobserved subgroups of students and schools in each TIMSS studies from 1995 to 2015 in Sweden?
3. Does the relationship among SES OTL and math achievement differ across the subgroups of students and schools?
Authors. (2016a) Authors. (2016b). Henry, K. L., & Muthén, B. (2010). Multilevel latent class analysis: An application of adolescent smoking typologies with individual and contextual predictors. Structural Equation Modeling: A Multidisciplinary Journal, 17(2), 193–215 Lubke, G. H., & Muthén, B. (2005). Investigating Population Heterogeneity With Factor Mixture Models. Psychological Methods, 10(1), 21-39. doi:http://dx.doi.org/10.1037/1082-989X.10.1.21 Mullis, V.S., Martin, M. O. & Loveless, T. (2016). 20 Years of TIMSS International Trends in Mathematics and Science Achievement, Curriculum, and Instruction. TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College and International Association for the Evaluation of Educational Achievement (IEA). Muthén, L. K., & Muthén, B. O. (1998-2015). Mplus user’s guide (6th ed.). Los Angeles, CA: Muthén & Muthén. Schmidt, W. H., Burroughs, N. A., Zoido, P., & Houang, R. T. (2015). The Role of Schooling in Perpetuating Educational Inequality: An International Perspective. Educational Researcher, 44(7), 371-386. doi:10.3102/0013189x15603982 Thrupp, M. (1995). The School Mix Effect: the history of an enduring problem in educational research, policy and practice. British Journal of Sociology of Education, 16(2), 183-203. doi:10.1080/0142569950160204 Thrupp, M., & Lupton, R. (2006). Taking school contexts more seriously: The social justice challenge. British journal of educational studies, 54(3), 308-328.
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