Opportunity to learn secondary maths: A curriculum approach with TIMSS 2011 data
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This document summarizes a presentation about using TIMSS 2011 data to analyze the relationship between opportunity to learn (OTL) factors, socioeconomic status (SES), and mathematics achievement. The presentation discusses using multilevel models to account for student, classroom, school, and country-level variables. It finds that SES variables significantly reduce variance at the classroom and country levels, while OTL variables also reduce variance but to a lesser degree. OTL is a significant predictor of achievement at the classroom level but not the country level, suggesting classroom/teacher factors play a bigger role than national curriculum.
Opportunity to learn secondary maths: A curriculum approach with TIMSS 2011 data
- 1. OPPORTUNITY TO LEARN SECONDARY MATHS: A CURRICULUM APPROACH WITH TIMSS 2011 DATA Dr. Christian Bokhove Southampton Education School BSLRM day conference November 7th 2015
- 2. Rationale • enGasia project, studying geometry education in international perspective • Differences in curriculum • Existing international comparisons like TIMSS and PISA • Recently published paper ‘Opportunity to Learn’
- 3. IEA & OECD “The International Association for the Evaluation of Educational Achievement (IEA) is an independent, international cooperative of national research institutions and governmental research agencies. It conducts large- scale comparative studies of educational achievement and other aspects of education.” “The mission of the Organisation for Economic Co-operation and Development (OECD) is to promote policies that will improve the economic and social well-being of people around the world.”
- 4. Opportunity to Learn • Relationship Socioeconomic status and achievement (e.g. Sirin, 2005; Chudgar & Luschei, 2009) • One factor: role of curriculum, exposure to curriculum content. • Opportunity to learn (OTL; Carroll, 1963), content coverage
- 5. Schmidt, Burroughs, Zoido & Houang (2015) • Role of schooling in perpetuating educational inequality • PISA 2012 data • Opportunity to Learn • “instructional content as a mediator for socioeconomic inequality” • Student level indicators? • Key question what is involved in OTL? Surely the teacher/classroom level is important?
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- 7. Dynamic model • Educational outcomes are influenced by variables at the student level, the classroom level, the school level and national/regional level. Dynamic model (Creemers & Kyriakides, 2008). • ‘Management of time’ at teacher/classroom level one of the most significant factors of effectiveness. • OTL specifically: – national/regional level (e.g. national curriculum), – classroom (content covered by teacher) – and to a lesser extent school level. 7
- 8. So a curriculum approach • Do students know best what contents is covered? • And if they do, then isn’t that more a proxy for achievement? • With content covered, teachers perhaps know best? • TIMSS more curriculum oriented than PISA (Rindermann & Baumeister, 2015) • TIMSS samples classrooms 8
- 9. TIMSS 2011 “TIMSS 2011 is the fifth in IEA’s series of international assessments of student achievement dedicated to improving teaching and learning in mathematics and science. First conducted in 1995, TIMSS reports every four years on the achievement of fourth and eighth grade students.“ http://timssandpirls.bc.edu/timss2011/
- 11. Curriculum model • TIMSS’ curriculum model (Mullis, Martin, Ruddock, O’Sullivan, & Preuschoff, 2009) – intended curriculum (the educational system's aims and goals) – implemented curriculum (the actual strategies, practices, and activities found in classrooms) – attained curriculum (student learning) 11 Multilevel: students in classrooms in countriesMultilevel: students in classrooms in countries
- 12. Methodology: analytical approach • Secondary data analysis of TIMSS 2011 data • Use multilevel models • Take into account complex sampling design of TIMSS 2011 – Different probabilities of units being selected (classrooms, students) → sampling weights – Rotated-booklet design → Plausible Values combined by Rubin’s rules (Rubin, 1987). – No jackknife for correct SE measurement → multilevel approach should cater for this 12
- 13. Methodology: analytical approach • IDB analyser used to create datasets for three levels • HLM 6.08 used to build four models – A null model – A model with SES variables – A model with OTL variables – A model with both SES and OTL variables • Further assumptions – Group-centered variables at student and classroom levels – Grand mean centered at country level – Full Maximum Likelihood – Missing data imputed with EM algorithm 13
- 14. Dataset • TIMSS 2011 grade 8 data • Data at three levels – achievement and background data of students, – classroom level data from the teacher questionnaire, and – curriculum data at the country level. • After data preparation: 287395 students in 11688 classrooms in 50 countries. • Choice of variables
- 15. Dependent variable • Five plausible values for maths achievement, BSMMAT01 to BSMMAT05 15
- 16. Independent variables: student level • ‘Home Economic Resources’ – Proxy for SES (and related to prior knowledge) – Numbers of books at home, highest level education of either parent, number of home study support – One scale through IRT scaling (Rasch partial credit model) • No OTL measure at student level for reasons explained previously. 16
- 17. Independent variables: class level • Mean SES in a class → Classroom SES • Newly created OTL measure → Classroom OTL – Percentage of the content domain covered with students – Maths instruction time – Variable between 0 and 2 of content coverage 17
- 18. Independent variables: country level • Mean SES in a country → Country SES • Newly created OTL measure → Classroom OTL – Is there a national curriculum? – Does curriculum prescribe goals and objectives? – Curriculum coverage (content domains: number, algebra, geometry and data and chance) – Variable between 0 and 3 content coverage • (Also made mean country OTL but strangely enough low correlation with teacher reporting) 18
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- 20. Descriptives 20
- 21. Models 21
- 22. Conclusions • SES variables reduce classroom and country variance • OTL variables also reduction but less • SES variables significant (country somewhat less strong) • OTL significant at classroom level not country level So here we can confirm big role SES plays, also in relation to OTL, but at the country level curriculum plays less of a role. It’s more about the classroom/teacher. 22
- 23. Discussion/further analyses • Content domains: number, algebra, geometry and data and chance. • Sampling classrooms • Correlations low: – Mean country OTL and newly created country OTL variable – Same with mean student perception ‘time spent’ and teacher ‘time spent’ for a classroom.
- 24. Discussion/further analyses • Country OTL measure: most have curriculum that prescribes objectives. • Specific country context: OTL in the English curriculum – Double/triple science – Setting
- 25. Thank you • Questions/comments? • More information: C.Bokhove@soton.ac.uk 25
- 26. References Carroll, J.B. (1963). A model of school learning. Teachers College Record, 64(8), 723-733. Chudgar, A. & Luschei, T.F. (2009). National income, income inequality, and the importance of schools: A hierarchical cross- national comparison. American Educational Research Journal, 46(3), 626-658. Creemers, B.P.M. & Kyriakides, L. (2008). The dynamics of educational effectiveness: a contribution to policy, practice and theory in contemporary schools. London: Routledge. Mullis, I.V.S., Martin, M.O., Ruddock, G.J., O’Sullivan, C.Y., & Preuschoff, C. (2009). TIMSS 2011 Assessment frameworks. Lynch School of Education, Boston College. Mullis, I.V.S., Martin, M.O., Foy, P., & Arora, A. (2012). TIMSS 2011 International results in mathematics. Lynch School of Education, Boston College. Rindermannm H, & Baumeister, A.E.E. (2015). Validating the interpretations of PISA and TIMSS tasks: A rating study. International Journal of Testing, 15(1), 1-22. Rubin, D. (1987). Multiple imputation for nonresponse in sample surveys. New York: John Wiley. Rutkowski, L., Gonzalez, E., Joncas, M., & von Davier, M. (2010). International large-scale assessment data: Issues in secondary analysis and reporting. Educational Researcher, 39(2), 142-151. Schmidt, W.H., Zoido, P., & Cogan, L.S. (2013). Schooling matters: Opportunity to learn in PISA 2012 (OECD Education Working Papers No. 95). Paris, France: Organisation for Economic Co-operation and Development. Schmidt, W.H., Burroughs, N.A., Zoido, P., & Houang, R.T. (2015). Educational Researcher, 44(7), 371-386. Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. 26
Editor's Notes
- #2: Previous studies have shown that socioeconomic status (SES) and ‘opportunity to learn’ (OTL), which can be typified as ‘curriculum content covered’, are significant predictors of students’ mathematics achievement. Seeing OTL as curriculum variable, this paper explores multilevel models (students in classrooms in countries) and appropriate classroom (teacher) level variables to examine SES and OTL in relation to mathematics achievement in the 2011 Trends in International Mathematics and Science Study (TIMSS 2011). Results suggest that the combination of SES and OTL explains a considerable amount of variance at the classroom and country level, but that this is not caused by country level OTL.
- #17: Assumptions variables not to include: Gender: no reason to believe there are differences in gender