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education reform

Can overhauling ‘teaching’ reform schools in Kenya?

Suvojit Chattopadhyay's picture

Kenyan schools are not doing well. About a 120 of them were set alight in arson attacks last year alone which were largely blamed on fears arising from a government crackdown on cheating in national exams. Amid national schooling reforms, many pupils and parents continue to be unhappy about the changes. Where do the teachers figure within this period of heavy reform?

Both the best and worst performers in East Africa are in Kenya
Although school enrolment has gone up steadily, over a million children are still out of school. In terms of learning outcomes, Kenya performs relatively better than its neighbours, but results from internationally recognised competency test, Uwezo, shows that learning levels are poor, and have stagnated over time. For instance, in the 2014 Uwezo assessment, 39% of children aged 7-13 years passed a test that required them to demonstrate competence of Standard 2 level numeracy and literacy. This was not significantly different from the performance in previous years: 40% in 2011, 37% in 2012 and 41% in 2013. Looking at student learning levels, both the best and worst performing districts in East Africa are in Kenya. The extremities in quality within Kenyan education are huge. For instance, according to the same Uwezo data, “a child in the Central region is over seven times more likely to have attained a Standard 2 level of literacy and numeracy than a child in the North Eastern region”.

Fixing the education system in Kenya is an onerous task. The Government of Kenya has time and time again, reiterated its commitment to improving the state of education, and has outlined its vision in the National Education Sector Plan 2013- 2018. Alongside, a host of national and international development agencies in Kenya have over the years, financed numerous programmes, targeting various components of the education sector. These efforts have yielded a wealth of evidence. One should consider such evidence, while attempting to answer the question – how can we improve the quality of schooling in Kenya?

Weekly wire: The global forum

Roxanne Bauer's picture
World of NewsThese are some of the views and reports relevant to our readers that caught our attention this week.
 

How does political context shape education reforms and their success? Lessons from the Development Progress project
ODI

Achieving Sustainable Development Goal 4 – ‘Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all’ – is one of the most important and challenging tasks in international development. In order to fulfil it, we require a better understanding of why progress and the impact of interventions varies so widely by context. One striking gap in our knowledge here is a lack of analysis as to how education systems interact with political contexts that they operate in. This report addresses this gap by drawing on evidence from eight education-focused country case studies conducted by ODI’s Development Progress project and applying political settlements analysis to explore how political context can shape opportunities and barriers for achieving progress in education access and learning outcomes.

Combining satellite imagery and machine learning to predict poverty
Science

Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries—Nigeria, Tanzania, Uganda, Malawi, and Rwanda—we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains. Data imagery of the report is available on the project website.