Abstract
In this chapter secondary analyses of international data sets are presented. The analyses are based on data from TIMSS 2011 (grade 4 and grade 8) and PISA 2012, for the 22 countries that participated in both studies. In the analyses on TIMSS data three explanatory variables are taken into account: mathematics OTL, science OTL and number of books at home. In PISA only information on mathematics OTL is available (no information on science OTL was collected). All data were aggregated at the school level. All in all the secondary analyses of these international data sets show a modest effect of OTL for mathematics. An unexpected finding was that math OTL appears to be more strongly related to science achievement, than science OTL. The PISA 2012 results showed relatively high OTL effects, within and between countries. The standardized regression coefficients range from 0.119 in Romania to 0.813 in Qatar. The average effect across the 22 countries in PISA was 0.369. The PISA OTL effects stand out as being much stronger than the mathematics results found in the TIMSS studies. The first hypothetical explanation for this difference that comes to mind is the fact that TIMSS OTL measures were based on teacher responses, and the PISA OTL measures on student responses. The findings leave many questions that would be interesting to take up in future research.
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References
Caroll, J. B. (1963). A model of school learning. Teachers College Record, 64, 722–733.
Caroll, J. B. (1989). The Caroll model, a 25-year retrospective and prospective view. Educational Researcher, 18, 26–31.
OECD. (2014a). PISA technical report. Paris: OECD.
OECD. (2014b). PISA 2012 results: What student know and can do, student performance in mathematics, reading and science (Vol. 1). Paris: OECD.
Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15(3), 351–357.
Schmidt, W. H., Burroughs, N. A., Zoido, P., & Houang, R. H. (2015). The role of schooling in perpetuating educational inequality: an international perspective. Educational Researcher, 20(10), 1–16.
Wu, M. & Adams, R. J. (2002). Plausible values—Why they are important. Paper presented at the International Objective Measurement Workshop, New Orleans, 6–7 April.
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Luyten, H. (2017). Predictive Power of OTL Measures in TIMSS and PISA. In: Scheerens, J. (eds) Opportunity to Learn, Curriculum Alignment and Test Preparation . SpringerBriefs in Education. Springer, Cham. https://doi.org/10.1007/978-3-319-43110-9_5
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DOI: https://doi.org/10.1007/978-3-319-43110-9_5
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