Analysing matric data to identify 'promising' schools in mathematics performance

Debra Shepherd (debrashepherd@sun.ac.za) and Servaas van der Berg (svdb@sun.ac.za)

No 16/2020, Working Papers from Stellenbosch University, Department of Economics

Abstract: This paper applies rigorous data analysis to the NSC fulltime 2010 to 2018 data to identity 'promising' schools in Mathematics amongst the group of Quintile 1 to Quintile 3 (Q1-Q3) public sector secondary schools. Several indicators of performance are investigated, including consistency in performance, above-expectations performance (using education production function analysis) and wasted Mathematics 'potential' through allowing and/or encouraging good matric candidates to take Mathematical Literacy. We find that enrolment in Mathematics is highest amongst Q1 and Q2 schools. However, enrolment in Mathematics is negatively correlated with average school performance in Mathematics in Q1-Q3 schools. Although the overall proportions of matriculants from Q4 and Q5 schools passing mathematics has declined over time, the pass rates of Q1-Q3 schools in Mathematics increased by roughly 30% between 2010 and 2018. However, despite a rise in the numbers of students achieving better (50% and higher) passes in Mathematics, the vast majority of Q1-Q3 schools show consistently low or inconsistent average performance. Roughly 10% of Q1-Q3 schools perform consistently above expected, less than 6% meet the potential to substantially improve their Mathematics pass rates, and only 21 Q1-Q3 schools indicate the potential to outperform Q5 schools.

Keywords: mathematics performance; schools with promise; matric (NSC) data; South Africa (search for similar items in EconPapers)
JEL-codes: I10 I21 (search for similar items in EconPapers)
Date: 2020
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