Weakest part of poorly-performing educational systems: an argument for focus on “teaching at the right level” and improved foundation-year performance

Autor(es): Crouch, Luis

Date: 2017

Pages: 4 p.

Serie: Insights

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The RISE Programme seeks to provide insights to policy-makers on how to improve systems of education in order to promote learning. This is a laudable, but a very high-level goal. As the Programme moves along, it may be worthwhile now and then, to ask whether there are sub-system issues that policy-makers ought to be paying attention to, in that they are foundational (in a causal sense) to the rest of the system, or because they are very symptomatic of overall problems. This note argues that in some thirty-five to forty countries that are expanding education very quickly, there are learning problems that originate in the earliest grades, showing as a massive over-enrolment during this stage. In Grade 1, it is not unheard of for ratios of enrolment to population of appropriate age to be as high as 150 percent. The countries in question are typically not upper-middle or high-income countries, where the issue has been resolved. Nor, are they the very poorest countries, which have not mobilised towards massive enrolment expansions, and so “don’t even have the problem yet.” Instead, the typical countries in this situation are countries such as Rwanda, Ethiopia, Malawi, Timor-Leste, and others that have received a great deal of funding and attention from development agencies, and have expanded enrolment quickly in the last decade or two. Out of the countries in the Global Partnership for Education, about forty percent suffer from the syndrome of issues highlighted in this note. It should be mentioned that, because of the very focus of RISE, only one out of the six RISE countries to date, suffers from this syndrome to a significant extent: Ethiopia. Therefore, the arguments in this note are for countries that have serious systems problems and will hopefully learn from the RISE experience, including the identification of which sub-systems the learning should be applied to.

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