The role that mathematics plays in college- and career-readiness: evidence from PISA

ABSTRACT

Many studies have found a strong relationship between the mathematics students study in school and their performance on an academic or school mathematics assessment but not on an assessment of mathematics literacy (ML). With many countries, like the USA, placing emphasis on finishing secondary education being mathematically literate and prepared for college or career, this raises the question about the relationship between the mathematics studied in school and any ML students may have. The Programme for International Student Assessment (PISA) ML assessment is embedded in real-world contexts that provide an important window on how ready students are to tackle the situations and problems that await them whether they intend to pursue further education beyond high school or intend to go directly into the labour force. In this paper, we draw upon the PISA 2012 data to investigate the extent to which the cumulative exposure to rigorous mathematics content, such as that embedded in college- and career-ready standards, is associated with ML as assessed in PISA. Results reveal that both exposure to rigorous school mathematics and experiencing the instruction of this mathematics through real-world applications are significantly related to all the real-world contextualized PISA ML scores.

KEYWORDS:

• Mathematics literacy
• mathematics content
• opportunity to learn
• career ready
• college ready

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Full details on the PISA sampling design and the corresponding sampling weights are presented in Chapters 4 and 8 of the PISA 2012 Technical Report (OECD, 2014b).

2. The PISA report (OECD, 2013b) provides statistics for all PISA participants including the OTL indices but not for the IGP-weighted version of the School Mathematics variable used here. Reliabilities of the OTL indices are reported in Table 16.20 for all OECD countries in the PISA 2012 Technical Report (OECD, 2014b, p. 325). Applied Math (EXAPPLM) reliabilities ranged from 0.69 to 0.82. The OECD median reliability was 0.77. The reliabilities for School Mathematics (FAMCON in Table 16.20) ranged from 0.81 to 0.91. The OECD median reliability was 0.85. Since the School Mathematics used here is a linear transformation of FAMCON in Table 16.20, the reliabilities are the same. No reliability is reported for Word Problems as this index consists of a single item.

3. Only the weighted means for the variables used in this paper are listed in Table 1. Standard errors and other statistical measures are included in the first report volume and the PISA 2012 Technical Report volume (OECD, 2013b, 2014b). All OECD countries are listed except for Norway as Norway did not include the School Math item on their student questionnaire. For brevity’s sake, only those non-OECD countries that participated in at least one of the computer-based assessments are included in Tables 1–3.

4. The percentage of the PISA student sample in each grade is presented in Table A2.4a on page 274 of the PISA 2012 Technical Report (OECD, 2104b). The PISA student sample design which includes a discussion of grade level is on page 267. Page 261 explains the creation and use of the PISA modal grade variable.

5. The term ‘effect’ is used here consistent with its classical ANOVA meaning. It is not meant to be interpreted in a causal way.

6. The PISA SES index is made up of three other indices: home possessions (itself a composite of four other scales), highest parental education, and highest parental occupation. See page 351 and following of the PISA 2012 Technical Report for a full discussion of this (OECD, 2014b).

Additional information

Funding

This work was supported by the Thomas J. Alexander Fellowship.

Notes on contributors

Leland S. Cogan

Leland S. Cogan is a Senior Researcher with the Center for the Study of Curriculum in the College of Education at Michigan State University. His research interests focus on mathematics and science curricula and the preparation of teachers to teach these subjects in schools.

William H. Schmidt

William H. Schmidt is a University Distinguished Professor of statistics and education at Michigan State University. His current work is focused on educational policy related to mathematics, science and testing in general.

Siwen Guo

Siwen Guo, is a PhD candidate in measurement and quantitative methods at Michigan State University. Her research interests focus on the application of quantitative methods to large-scale datasets and policy research, and the effect of mathematics curriculum on student performance.