09 SES 01 A, Investigating Quality and Equity Using Large-Scale-Assessments
Academic competencies—in particular, the core competencies reading, mathematics, and science—predict a variety of important life outcomes, such as subsequent school grades, participation and academic success in higher education, and success on the labor market (e.g., Kuncel, Hezlett, & Ones, 2001; Richardson, Abraham, & Bond, 2012). Thus, the question whether there are gender differences at different levels of academic competencies is of high practical relevance. The reasons for gender differences in academic achievement are not yet well understood. Several explanations have been suggested, which can roughly be subdivided into the biological and the sociocultural account. The biological account encompasses evolutionary, genetic, hormonal, and brain-related explanations. However, evidence for biological theories is mixed (see, e.g., Ceci et al., 2009; Hyde, 2014, for an overview). Sociocultural theory posits that gender differences are driven by social influences such as societal gender equity (e.g., equity in labor division, women’s access to education; see Ceci et al., 2009; Hyde, 2014). Eccles et al. (1983) and extensions of the expectancy-value model (e.g. Eccles & Wigfield, 2002) aim at systematically integrating assumptions on the relations of different factors driving gender related achievement differences and identify expectations and values as main drivers of disparities in educational achievement of boys and girls.
Even tough gender differences in educational achievement at primary school level have often been interpreted in the light of the Gender Similarity Hypothesis (z.B. Ball, Cribbie & Stelle, 2013; Hyde, 2005) a continuous monitoring of gender related disparities is crucial to ensure that reduced inequalities are not reestablished.
In Slovenia boys showed significantly higher achievement in both mathematics (9 points) and science (12 points) in TIMSS 1995. Since then Slovenia’s primary school education system has faced several substantial structural as well as curricula changes since 1995 (Japelj Pavesic & Svetlik, 2016). This has resulted in a significant and remarkable positive achievement trend in mathematics and science for both girls and boys. Interestingly in mathematics achievement differences in TIMSS disappeared in both domains 2003, but reappeared in mathematics in 2007 and 2011 but not in 2015; whereas in science only reappeared in 2015 (Martin et al. 2016; Mullis et al. 2016). Against this background this paper aims at better understanding factors driving trends in gender differences in mathematics and science achievement in Slovenia since 1995.
To what extent are trends in the development of disparities in achievement between boys and girls associated with chances in the intrinsic motivation or the self-concept of learners?
To what extent are trends in the development of disparities in achievement between boys and girls associated with chances in socio-demographic and learning related family characteristics?
Methodology or Methods/Research Instruments or Sources used: For the analysis multilevel trend regression models were fitted based on student and parent data from the five cycles of the Slovenian participation in TIMSS Grade 4 dataset. The multilevel analyses were conducted with the software SAS/STAT (version 9.4) and took into account the nested data structure as well as, in trend perspective, possible changes in the socio-demographic make-up of the samples across the cycles. In the regression models, the dependent variable was mathematics or science achievement on the overall TIMSS scales. As an analysis strategy in the first model gender differences were explored across the study cycles. In the next step in was investigated to what extent the gender differences remained stable when controlling for student and home factors. Here the following independent variables were used: For self-concept and motivation trend indices across items from the five TIMSS cycles were scaled and the constructs were included as continuous variables in the models, as was done for the home literacy environment, where a trend index was scaled from the parental responses to four aspects: number of books at home, number of children’s books at home, parents’ attitudes and parental activities. Parental education was included as dichotomized variable as well as the the gender of the students.
Conclusions, Expected Outcomes or Findings: Preliminary findings from the multilevel regressions indicate different developments for the two different achievement outcomes: Whereas in mathematics trend towards the reduction of achievement differences can been found, in science a trends towards an increase of differences in the favor of boys becomes apparent, driven by increasing differences in the physics and geography domains. In addition analysis provide evidence for the assumption that developments of disparities in gender related achievement differences in Slovenia are driven by both structural changes as well as differential developments of student attitudes and self-concept.
Ball, L.C., Cribbie, R.A., & Steele, J.R. (2013). Beyond gender differences: Using tests of equivalence to evaluate gender similarities. Psychology of Women Quarterly, 37(2), 147-154. Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women’s underrepresentation in science: Sociocultural and biological considerations. Psychological Bulletin, 135, 218 –261. http://dx.doi.org/10.1037/a0014412 Eccles, J.S. (1994). Understanding women's educational and occupational choices: Applying the Eccles et al. model of achievement-related choices. Psychology of Women Quarterly, 18(4), 585-609. Eccles, J.S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109-132. Hyde, J.S. (2005). The Gender Similarities Hypothesis. American Psychologist, 60(6), 581-592. Hyde, J. S. (2014). Gender similarities and differences. Annual Review of Psychology, 65, 373–398. http://dx.doi.org/10.1146/annurev-psych-010213-115057 Martin, M. O., Mullis, I. V. S., Foy, P., & Hooper, M. (2016). TIMSS 2015 International Results in Science. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/timss2015/international-results/ Mullis, I. V. S., Martin, M. O., Foy, P., & Hooper, M. (2016). TIMSS 2015 International Results in Mathematics. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/timss2015/international-results/ B. Japelj Pavesic & K. Svetlik (2016). Slovenia. In: Mullis, I. V. S., Martin, M. O., Goh, S., & Cotter, K. (Eds.) (2016). TIMSS 2015 Encyclopedia: Education Policy and Curriculum in Mathematics and Science. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/timss2015/encyclopedia/
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