Researching Mathematics Achievement Growth Of European Primary School Students – Results Towards Compositional Effects Of a Longitudinal Study
Author(s):
Conference:
ECER 2017
Format:
Paper

Session Information

09 SES 04 A, Investigating School Composition Effects

Paper Session

Time:
2017-08-23
09:00-10:30
Room:
W3.11
Chair:
Kajsa Yang Hansen

Contribution

Recent research literature on educational achievement in primary schools indicates that many factors can be regarded as a fostering or hindering for achievement growth. For example scholars tend to agree that living in a home that is culturally (cf. Bourdieu, 1986) or socially privileged (Coleman, 1988) can be regarded as fostering factor and students with a privileged cultural or social background are more likely to show high achievement in many domains compared to their peers from less privileged families (Mullis, Martin, Foy, & Arora, 2012). In this context individual characteristics of students have proven to have significant if not substantial effects on student’s achievement growth. Hence, many researchers were able to show that migratory status (Georges & Pallas, 2010), cultural capital (Petty, Harbaugh, & Wang, 2013), socio-economic status (SES; Luyten, Schildkamp, & Folmer, 2009) and aptitude (Sasanguie, Van den Busche, & Reynvoet, 2012) are relevant predictors for achievement growth in primary education.

In light of the history of such-like consistent and stable findings of research on mathematics education, theoretical improvements were established: For example, models conceptualizing knowledge growth and their underlying factors on multiple layers of the educational system have been refined and centrally focus on individual characteristics of primary school students. For example the dynamic model of educational effectiveness (cf. Creemers & Kyriakides, 2008) comprises individual characteristics as well as factors on the teacher, school and context level assuming that all of the determinants have a relevant if not substantial effect on various outcomes of school education such as cognitive, affective and psychomotor skills and abilities. Additionally, literature indicates that there are also compositional effects of classes and schools to take into account when mathematics education is under research (Schofield, 2010). Although there are many cross-sectional investigations of compositional effects in the field, longitudinal studies investigating the effect of individual, family-related background characteristics and compositional variables are scarce, especially in the European context. Therefore, acknowledging the concept of the aforementioned theoretical model, individual and compositional effects are focused in this contribution using the data set of the study ADDITION-study (Creemers et al., 2013) which has been conducted in six European countries.

The present paper focuses on the relevance of individual and compositional characteristics and knowledge growth in mathematics in primary education. In detail, the following research questions are addressed in this paper considering the German subsample of primary school students:

  1. How can the growth in mathematical competencies be explained by central individual background characteristics (gender, migratory status, cultural capital and practice, SES) and previous mathematical competencies?
  2. Taking into account school-related measures of the student body composition, how can the relative relevance of those factors be determined?

Method

The data used to answer the above-mentioned research questions stem from a collaborative European research project entitled A Dynamic Effective Knowledge Base for Quality in Education (ADDITION; cf. Creemers et al., 2013) which is supported by the European Science Foundation (ESF) and has been conducted in six European countries (Belgium, Cyprus, Germany, Greece, Ireland, and Slovenia). Applying a multi-staged clustered sampling approach, the ADDITION-study draws representative samples of fourth grade students in the educational systems mentioned above and tested the mathematical and scientific competencies of the students at two points of measurement (beginning and end of grade four) using instruments from the Trends in International Mathematics and Science Study (TIMSS) and a rotated test booklet design. This offered the unique opportunity to project the longitudinal design of the ADDITION-study onto the metric of TIMSS. This has been conducted calibrating a 3PL IRT model utilizing corresponding item parameters for Germany from the 2011 cycle of the TIMS-study and using the plausible value approach (Von Davier, Gonzalez, & Mislevy, 2009) which is also a default procedure in the TIMS-studies. Thereby, the descriptive results in ADDITION can be interpreted with respect to the five proficiency levels differentiated by TIMSS. The database is also complemented by a student questionnaire which assessed individual background characteristics (gender, migratory status, SES) as well as further information on the students’ live with their families (cultural capital and practice). To answer the above-mentioned research questions, the German subset of the international database (N = 1,117) is used and multivariate methods are applied to investigate the relative relevance of single factors. Descriptive analyses are complemented by multivariate regression analyses accounting for the nested structure of the data (multilevel regression analysis; cf. Raudenbush & Bryk, 2002). The analytic strategy behind the research questions in a first step requires the determination to what extent variation in mathematics can be allocated to the individual or to the class level. Subsequent analyses are carried out taking into account a) individual characteristics of the fourth grade students and b) school characteristics (social or achievement-related student body composition). All multilevel analyses in this paper were applied using the statistical software package of Mplus 7 (Muthén & Muthén, 2012).

Expected Outcomes

In total, analyses of individual predictors were able to show that these are closely linked to mathematical competencies. Especially aptitude (γ08 = 0.6; p < .05) and the migratory background (γ02 = -10.2; p < .05) prove to be the strongest predictors on the individual level in the mathematics domain. Taking into account individual characteristics and compositional variables of schools, the central finding of this paper is that among relevant individual characteristics, the average school-specific mathematics achievement (aptitude on the school level) is one of the strongest predictors among considered variables (γ010 = 0.2; p < .05). Additionally, a finding is that classes do not show significantly higher scores because at their schools, there are low proportions of students with low cultural capital, low proportions of students with a migratory background or with a favorable socio-economic background. Although on the individual level, there is a strong but declining link between social background and educational achievement (Martin, Mullis, Foy, & Stanco, 2012; Mullis et al., 2012), the results in this paper lead the authors to believe that on the school level, social intake characteristics (as measured in ADDITION) do not affect the mathematics achievement of fourth grade students, which, in turn, is an indication that regional disparities (emerging through different areas with different population characteristics) are negligible, at least in the German educational system. Future research should address the fact that the average mathematics achievement level predicts mathematics achievement of classes. Hereto, for example, measures of instructional quality (cf. Creemers & Kyriakides, 2008) should be taken into account in order to investigate if this variables are mediating this effect. Furthermore, qualitative studies could contribute to further explore the mechanics of the school-specific achievement level and mathematics learning in primary education in Europe.

References

Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education (pp. 241-260). New York: Greenwood Press. Coleman, J.S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, 95-120. Creemers, B.P.M., & Kyriakides, L. (2008). The Dynamics of Educational Effectiveness. A Contribution to Policy, Practice and Theory in Contemporary Schools. Abingdon: Routledge. Creemers, B.P.M., Kyriakides, L., Panayiotou, A., Bos, W., Holtappels, H.G., Pfeifer, M., . . . Tempridou, A. (2013). Establishing a knowledge base for quality in education: Testing a dynamic theory for education. Handbook on designing evidence-based strategies and actions to promote quality in education. Münster: Waxmann. Georges, A., & Pallas, A.M. (2010). New look at a persistent problem: Inequality, mathematics achievement, and teaching. The Journal of Educational Research, 103(4), 274-290. Luyten, H., Schildkamp, K., & Folmer, E. (2009). Cognitive development in Dutch primary education, the impact of individual background and classroom composition. Educational Research and Evaluation, 15(3), 265-283. Martin, M.O., Mullis, I.V.S., Foy, P., & Stanco, G.M. (2012). TIMSS 2011 International Results in Science. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College. Mullis, I.V.S., Martin, M.O., Foy, P., & Arora, A. (2012). TIMSS 2011 International Results in Mathematics. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College. Muthén, L.K., & Muthén, B.O. (2012). Mplus 7. Los Angeles, CA: Muthén & Muthén. Petty, T., Harbaugh, A.P., & Wang, C. (2013). Relationships between student, teacher, and school characteristics and mathematics achievement. School Science and Mathematics, 113(7), 333-344. Raudenbush, S.W., & Bryk, A.S. (2002). Hierarchical Linear Models. Application and Data Analysis Methods (2. ed.). Thousand Oaks, CA: Sage. Sasanguie, D., Van den Busche, E., & Reynvoet, B. (2012). Predictors for mathematics achievement? Evidence from a longitudinal study. Mind, Brain and Education, 6(3), 119-128. Schofield, J.W. (2010). International evidence on ability grouping with curriculum differentiation and the achievement gap in secondary schools. Teachers College Record, 112(5), 1492-1528. Von Davier, M., Gonzalez, E., & Mislevy, R.J. (2009). What are plausible values and why are they useful? In M. Von Davier & D. Hastedt (Eds.), IERI Monograph Series: Issues and Methodologies in Large-Scale Assessments (Vol. II) (pp. 9-36). Princeton, NJ: IEA ETS Research Institute (IERI).

Author Information

Mario Vennemann (presenting / submitting)
Paderborn University
Institute for Educational Research
Paderborn
Paderborn University, Germany
TU Dortmund University

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