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The promise of SA-SAMS & DDD data for tracking progression, repetition and drop-out

Author

Listed:
  • Servaas van der Berg

    () (Department of Economics, Stellenbosch University)

  • Chris van Wyk

    () (Department of Economics, Stellenbosch University)

  • Rebecca Selkirk

    () (Department of Economics, Stellenbosch University)

  • Kate Rich

    () (Department of Economics, Stellenbosch University)

  • Nicola Deghaye

    () (Department of Economics, Stellenbosch University)

Abstract

This paper analyses the SA-SAMS school administration data that the Michael and Susan Dell Foundation in partnership with the Department of Basic Education collects quarterly from schools in order to assess its usefulness for better understanding the school system. The disaggregated SA-SAMS data housed in the Data Driven Districts operational data store is typically provided in the form of data dashboards for analytical purposes to the education authorities. Although only non-random samples of the data are available in longitudinal form, the analysis shows that this can already be used to investigate important relationships and features of the education system. These include the relationship between performance in earlier grades and performance in matric, the relationship between performance, repetition and subsequent dropout, the choice between Mathematics and Mathematical Literacy, and the utility of using school-based assessments in investigating later educational outcomes. The SA-SAMS data also contains much better information on the number of disabled learners in schools than previous Annual Survey of Schools (ASS or EMIS) data. Expanding such analysis in the future with lengthened longitudinal data and larger samples as data collection improves should be very fruitful for an improved understanding of the school system.

Suggested Citation

  • Servaas van der Berg & Chris van Wyk & Rebecca Selkirk & Kate Rich & Nicola Deghaye, 2019. "The promise of SA-SAMS & DDD data for tracking progression, repetition and drop-out," Working Papers 17/2019, Stellenbosch University, Department of Economics.
    • Handle: RePEc:sza:wpaper:wpapers331
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    File URL: https://www.ekon.sun.ac.za/wpapers/2019/wp172019/wp172019.pdf
    File Function: First version, 2019
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    Cited by:

    1. Servaas van der Berg & Chris van Wyk & Rebecca Selkirk, 2020. "Schools in the time of COVID-19: Possible implications for enrolment, repetition and dropout," Working Papers 20/2020, Stellenbosch University, Department of Economics.

    More about this item

    Keywords

    South Africa; education; educational outcomes; longitudinal data; school-based assessment; school dropout; repetition;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I25 - Health, Education, and Welfare - - Education - - - Education and Economic Development
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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