Learner Performance in South Africa

Compiled by the Research Programme on Human Resources Development, Human Sciences Research Council Commissioned by JET Education Services and funded by the Business Trust Published by HSRC Press Private Bag X9182, Cape Town, 8000, South Africa www.hsrcpress.ac.za © 2005 Human Sciences Research Council First published 2005 All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. ISBN 0-7969-2041-9 Cover by Flame Design Produced by comPress Distributed in Africa by Blue Weaver Marketing and Distribution, P.O. Box 30370, Tokai, Cape Town, South Africa, 7966, South Africa. Tel +27 +21 701-4477 Fax: +27 +21 701-7302 email: booksales@hsrc.ac.za Distributed worldwide, except Africa, by Independent Publishers Group, 814 North Franklin Street, Chicago, IL 60610, USA. www.ipgbook.com To order, call toll-free: 1-800-888-4741 All other enquiries, Tel: +1 +312-337-0747 Fax: +1 +312-337-5985 email: Frontdesk@ipgbook.com Contents List of figures and tables iv Executive summary v Acknowledgements ix Abbreviations x 1 Introduction 1 1.1 Structure of this report 2 2 Features of the Quality Learning Project 5 3 Data preparation, statistical procedures and methodology 7 3.1 Introduction to the statistical analysis 7 3.2 Data preparation 7 3.3 Data reliability problems 9 3.4 Analytical strategy 13 3.5 School effects 15 3.6 Household effect variables 19 3.7 Removing the household effects from the school environment and language and mathematics interest and experience variables 21 3.8 The set of explanatory variables 24 3.9 The language and mathematics test scores 24 3.10 Do richer communities have better schools? 27 3.11 Regression of language and mathematics scores on the explanatory variables 28 4 Commentary on the findings 33 Appendices Appendix 1 Appendix 2 Appendix 3 Appendix 4 Appendix 5 Appendix 6 Appendix 7 Appendix 8 References Districts participating in the QLP 35 The QLP Model 36 The programmes of the QLP 39 Sampling methodology 41 Developing and administering the instruments 46 Conversion of data from the learner questionnaire into the list of variables used in this study 52 Learner background questionnaire 55 List of schools ordered by general factor, Grade 11 factor and Senior Certificate 73 76 List of figures and tables Figures Figure 1: Survey sample 5 Figure 2: Education of mothers 11 Figure 3: Education of fathers 11 Figure 4: Household wealth vs household income 12 Figure 5: General school factor on household wealth 27 Figure 6: School test factor on household wealth 28 Figure A3.1: QLP programmes 40 Tables Table 1: Total sample obtained for the baseline fieldwork in 2000 6 Table 2: Language most often spoken at home, by population group 9 Table 3: Education of mother and father, by grade 10 Table 4: Table 5: Expected level of the household wealth score at different levels of income 13 Analysis of variance of school environment, interest and learning experience scores 16 Table 6: Correlation matrix and factor analysis of the regression coefficients of school-level variables 17 Table 7: Regression coefficients for Senior Certificate pass rates on general and Grade 11 factors, 1999 and 2000 18 Table 8: Distribution of the use of the language of instruction at home 19 Table 9: Distribution of household scores 20 Table 10: Regression of study aids, meals, parental support, time use and home reading scores on whether an African language is spoken at home, logarithm of household wealth, parental support score (time use and home reading only) and a Grade 11 dummy variable 21 Table 11: Regression of interest and experience residuals on home effects variables 22 Table 12: Correlation matrix and factor analysis of the regression coefficients of school- level variables after the school and household effects have been removed 23 Table 13: Distribution of language and mathematics marks 25 Table 14: Factor analysis of test scores and Senior Certificate results 26 Table 15: Percentage test scores regressed on explanatory variables 29 Table 16: Language scores by quintile 31 Table 17: Mathematics scores by quintile 32 Table A 2.1: Outcomes and covariates at district level 37 Table A 4.1: Number of schools sampled per district 42 Table A 4.2: Number of learners assessed by school district 44 Table A 4.3: Total sample obtained for the baseline fieldwork in 2000 45 Table A 5.1: List of instruments 46 Table A 5.2: Test topics – mathematics, Grades 9 and 11 48 Table A 5.3: Reading and writing skills tested, Grades 9 and 11 49 Table A 5.4: Testing duration of the assessment instruments in minutes 50 iv v ©HSRC 2005 Executive summary Introduction Despite all the difficulties associated with the expansion of its educational system during the twentieth century, South Africa has done well in systematically lengthening the average education of each successive age cohort. By the 1990s, more than 90 per cent of the 7–16 age group was enrolled in school, although not every learner was putting in a full day’s attendance. But the quality of the output from the school system has left much to be desired. For many years, close to half of the Senior Certificate candidates have failed the examination outright. And international comparisons, such as the Third International Mathematics and Science Study of 1995, have given no comfort. South Africa was bottom of both the mathematics and science tables, even though a number of similar, middle-income countries were included in the study. In seeking to identify the reasons for this situation, it is important to relate educational outputs (competencies, as measured for instance by Senior Certificate examinations or standardised tests) to inputs. The most obvious input is that of the school itself – the quality of teachers, facilities and management. Another input is that of the household – the education of the parents, household income and wealth, and support for learners. Education is a joint product of school and home, and learners who are backed by strong household resources have an advantage. The abilities and proclivities of individual learners is a third input; learners from the same household and the same school can end up with very different profiles of achievement. Determining the relative contributions of these inputs to educational outputs is not straightforward. Partly this is a data problem. The information necessary to carry out a comprehensive analysis is extensive and usually not fully available. Partly it is a statistical problem: many of the explanatory variables are themselves related in complex ways, so identifying the true drivers of the situation under analysis is difficult. Moreover, in South Africa very little educational production function analysis has been undertaken, so there are few landmark results from which one can take one’s bearings. It is no exaggeration to say that educational production function analysis in South Africa is in a preliminary exploration phase. The results of this study must be interpreted in that light. Up until the Quality Learning Project’s (QLP) baseline study in 2000, no South African data set had ever included test results, school characteristics and information on the household circumstances of individual learners. Before that one could, as in the analysis of Senior Certificate results, relate results to schools and schools to the communities within which they were located. But one could not relate individual learners to the households from which they came. The QLP data set therefore offers a new analytical opportunity. The research question posed for this study was: what are the effects of socio-economic variables on educational outcomes in the QLP schools? Limitations of the study Before describing the methods and the findings of the analysis, it is important to note two limitations of this study. The first is that, within the time and resources available, it was not possible to use the full QLP data set. This study, therefore, concentrates on the learner background questionnaire (which elicited information on households) and the learner achievement questionnaire. It did not use (with one small exception) the data v ©HSRC 2005 ... - tailieumienphi.vn