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Citation Information

Type Conference Paper - 2nd IEA International Research Conference
Title Exploring student-level explanatory factors in science achievement in South African secondary schools
Author(s)
Publication (Day/Month/Year) 2006
Page numbers 0-0
City Washington D.C.
Country/State United States
URL http://www.iea.nl/fileadmin/user_upload/Publications/Electronic_versions/IRC2006_Proceedings_Vol1.pd​f
Abstract
South Africa’s education system is still deep in the throes of reform under its third Minister of Education since 1994. However, it is marked by underachievement of students at each level of the system. Poor communities, in particular, those of rural Africans, bear the brunt of the past inequalities, and these are continually reflected in the national results of the final-year examinations in Grade 12. Equity and access are at the top of the government’s priority list, which makes this paper important, as it attempts to analyze the progress made in terms of equity in education. South Africa participated in TIMSS 1995, 1999, and 2003. Secondary analyses of these studies have revealed the large inequities in the education system, with 55% of the variance in the students’ mathematics scores explained by differences between schools (Howie, 2002). This variance, in turn, is mostly explained by the historical inequities imposed on communities and schools over the past 40 years. The challenge was to explore the extent of the “gap” in students’ scores by comparing the advantaged and disadvantaged communities in this context, namely students in better-resourced, largely urban schools and students in largely under-resourced, black rural schools. The TIMSS-Repeat 1999 data were explored to focus on the extent of the gap in student science achievement between advantaged and disadvantaged communities and specifically on the factors at student level that predicted the outcomes in science in both types of communities. Three categories of students were ultimately identified. These were advantaged, semi-advantaged, and disadvantaged groups. Partial least square analysis was applied to explore the science performance in order to identify factors that predicted science performance across and within these groups. Very few factors were found that consistently predicted performance across and within these groups. However, one dominant factor emerged in these models and that was the students’ performance in the English test that provided a measure of students’ proficiency in English, the language in which more than 70% of the students wrote the science tests. Students who had a higher score on the English test also performed better in the science test, despite their backgrounds.

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