The promise of SA-SAMS & DDD data for tracking progression, repetition and drop-out
Stellenbosch Working Paper Series No. WP17/2019
Servaas van der Berg, Chris van Wyk, Rebecca Selkirk, Kate Rich, and Nicola Deghaye
Publication date: December 2019
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.
JEL Classification:
I21, I24, I25, O12, C81
Keywords:
South Africa, education, educational outcomes, longitudinal data, school-based assessment, school dropout, repetition
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