Social Origin And The Development Of Academic Performance In Swiss Primary Schools
Author(s):
Michael Beck (presenting / submitting) Jan Hochweber
Conference:
ECER 2016
Format:
Paper

Session Information

09 SES 02 C, Relating Home and School Learning Environments to Educational Achievement

Paper Session

Time:
2016-08-23
15:15-16:45
Room:
NM-F107
Chair:
David Miller

Contribution

Educational evaluation and standardised testing are widely discussed issues since at least the first PISA study. Typical topics include the research and comparison of effectiveness of educational systems, individual and group based predictors of achievement, development of academic performance over time but also the question of educational equality on the individual, class or societal level. While the influence of social origin variables as socio-economic status (SES) and minority status (in Europe mostly immigrant background) is well documented, the strength of those influences differs strongly with regard to countries, educational systems and grade level.

 Up to this day, educational policy in Switzerland is federally organised and within the responsibility of each canton. However, in the last years efforts have been made to standardise mandatory schooling with respect to length of schooling and curriculum. The new curriculum “Lehrplan 21” represents a milestone in the history of the Swiss educational system, focussing on competencies students should attain rather than curriculum contents. Standardised testing tools, providing feedback to facilitate students learning processes and providing teachers with a curriculum oriented objective measurement for students achievement as "Stellwerk" and "Klassencockpit" are becoming more important, and so does the need of developing those tests further with regard to the new curriculum. For that reason, the cantons of Zurich and St.Gallen are currently developing a new learning and testing tool called "LernLupe", which aims at students from grade level 3 to 6 in primary schools, allowing to assess their competence development in German and mathematics over four years. Results from LernLupe are supposed to help students and teachers to improve and individualize students’ learning strategies, but also to give teachers feedback which allows them to adapt their teaching and instruction methods to the requirements of their classes.

 The influence of SES on academic achievement is well researched and undisputed (e.g. Sirin 2005). As studies show, Switzerland is a country where the connection between social origin and academic performance is rather strong (e.g. OECD 2013). With respect to development over time, studies show, that the achievement gap in reading skills as well as mathematical skills between students from a more favourable and those with a less favourable social origin tends to widen over time in primary education (for an overview, see Neumann et al. 2014), although some research indicates, that at least in mathematics the achievement gap is rather stable over the first years of schooling, between the age of 7 and 11, and tends to widen between the age of 11 and 15 (Leibhan & Takongmo 2015, Caro et al. 2009).

This paper addresses the following research questions:

-       How do reading and arithmetic skills as measured by "LernLupe" improve between grade levels 3 and 6 in the respective cantons of Switzerland?

-       How much are those skills influenced by social origin in terms SES and immigrant background?

-       Does the influence (the gradient) of social origin change over time between grade level 3 and grade level 6?

Method

We are presenting results from the scaling of a new developed standardized performance test for Swiss primary schools, that was developed to test reading skills in German as well as arithmetic skills in accordance to the new Swiss standardized curriculum "Lehrplan 21". 938 classes (16,417 students) from class levels 3 to 6 in two Swiss-German cantons were randomly sampled (stratified by regional context). Every student took the test in reading as well as arithmetics, participation was mandatory. As a first step we model the development of performance over time using a multigroup-IRT-Model (Bock & Zimkowsky 1997) with the grade levels as a grouping variable. As a second step we examine the relation of SES (categorised into "high", "medium" and "low") and speaking a language different to the language of instruction (categorised in “only language of curriculum”, “language of curriculum and another language” and “other language than language of curriculum”) with academic performance over time using multiple hierarchical latent regressions (Wilson & De Boeck 2004). We introduce interactions of social origin variables with class level to research if and how the connection of said variables with performance is changing over time (i.e. class level).

Expected Outcomes

First results show an increase in academic performance over class levels between 0.4 and 0.7 pooled standard deviations. With increasing class level, the increase in performance is getting gradually smaller, as shown in other studies (Bloom et al. 2008, Hill et al. 2007). The connection between socio-economic status and reading performance is changing over time, as the students from a high socio-economic status are showing an increasingly higher performance with every grade level, compared to those from medium and low socio-economic status. There is a connection between speaking the language of the curriculum and reading performance: the difference between those speaking the language of the curriculum at home and those who don't, also increases over time.

References

Bloom, H.S., Hill, C.J, Black, A.R. & Lipsey, M.W.(2008). Performance trajectories and performance gaps as achievement effect-size benchmarks for educational interventions. MDRC working papers on research methodology. New York/Oakland: MDRC Bock, R. D., & Zimkowski, M. F. (1997). Multiple group IRT. In W. J. van der Linden, & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 433-448). New York: Springer. Caro, D. H., McDonald, T., & Willms, J. D. (2009). Socio-economic status and academic achievement trajectories from childhood to adolescence. Canadian Journal of Education, 32(3), pp. 558–590. Leibhan, L. & Takongmo, C.-O.M. (2015). Academic achievement trajectories and risk factors during early childhood. Scientific Series. Montreal: Cirano. Neumann, M., Becker, M.,& Maaz, K. (2014). Soziale Ungleichheiten in der Kompetenzentwicklung in der Grundschule und der Sekundarstufe I. Zeitschrift für Erziehungswissenschaft, Special Edition 24-2014 (Herkunft und Bildungserfolg von der frühen Kindheit bis ins Erwachsenenalter: Forschungsstand und Interventionsmöglichkeiten aus interdisziplinärer Perspektive), 167-203. OECD (2013). PISA 2012 Results in Focus. What 15-year-olds know and what they can do with what they know. Paris: OECD. Hill, C.J, Bloom, H.S., Black, A.R., Lipsey, M.W. (2007). Empirical benchmarks for interpreting effect sizes in Research. Child Development Perspectives 2(3), pp. 172-177. Wilson, M., & De Boeck, P. (2004). Descriptive and explanatory item response models. In: P. De Boeck, & M. Wilson (Eds.), Explanatory item response models (pp. 43-74). New York: Springer.

Author Information

Michael Beck (presenting / submitting)
University of Teacher Education St.Gallen, Switzerland
University of Teacher Education St.Gallen, Switzerland

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