IDEAS home Printed from https://ideas.repec.org/p/sza/wpaper/wpapers331.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.ekon.sun.ac.za/wpapers/2019/wp172019/wp172019.pdf
    File Function: First version, 2019
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chloé van Biljon & Cobus Burger, 2019. "The period effect: the effect of menstruation on absenteeism of school girls in Limpopo," Working Papers 20/2019, Stellenbosch University, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chris van Wyk, 2021. "Learner flow through patterns in the Western Cape using CEMIS datasets from 2007 to 2019: A longitudinal cohort analysis," Working Papers 01/2021, Stellenbosch University, Department of Economics.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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

      NEP fields

      This paper has been announced in the following NEP Reports:

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sza:wpaper:wpapers331. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Melt van Schoor (email available below). General contact details of provider: https://edirc.repec.org/data/desunza.html .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.