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Teaching ‘out of field’ in STEM subjects in Australia: Evidence from PISA 2015

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  • Shah, Chandra
  • Richardson, Paul
  • Watt, Helen

Abstract

Science, technology, engineering and mathematics (STEM) education is a critical part of a modern education system. Motivating students to learn STEM subjects is however a challenge. Teachers have a critical role in motivating students but to do this effectively they need to have appropriate subject matter knowledge. Data from PISA 2015 show a substantial proportion of teachers in Australian schools are teaching STEM subjects ‘out-of-field’, which is that they do not have the qualifications to teach these subjects. This paper examines the effects of individual teacher characteristics and school context on of out-of-field teaching in STEM subjects. In particular, it examines the role of school autonomy and staff shortage in this. The results show these two variables have a strong association with out-of-field teaching, however, other factors either mediate or confound their effects. A full understanding of the results requires knowing the role of school funding and school budgets in out-of-field teaching. While we do not have direct measures of these in the data, we can infer their likely roles through the effects of other factors, such as school sector and education level of parents of students in the school, in the model.

Suggested Citation

  • Shah, Chandra & Richardson, Paul & Watt, Helen, 2020. "Teaching ‘out of field’ in STEM subjects in Australia: Evidence from PISA 2015," GLO Discussion Paper Series 511 [rev.], Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:511r
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    References listed on IDEAS

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    1. Charles T. Clotfelter & Helen F. Ladd & Jacob L. Vigdor, 2010. "Teacher Credentials and Student Achievement in High School: A Cross-Subject Analysis with Student Fixed Effects," Journal of Human Resources, University of Wisconsin Press, vol. 45(3).
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    3. Clotfelter, Charles T. & Ladd, Helen F. & Vigdor, Jacob L., 2007. "Teacher credentials and student achievement: Longitudinal analysis with student fixed effects," Economics of Education Review, Elsevier, vol. 26(6), pages 673-682, December.
    4. Richard Williams, 2012. "Using the margins command to estimate and interpret adjusted predictions and marginal effects," Stata Journal, StataCorp LLC, vol. 12(2), pages 308-331, June.
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    Keywords

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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I22 - Health, Education, and Welfare - - Education - - - Educational Finance; Financial Aid
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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