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The government is investing over 500 million Euros to promote Technical and Vocational Education and Training (TVET) in the country.
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Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence”...
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As the Ministry of Education, Science and Technology focuses on the agenda for free and quality education in all stages of teaching and learning in Sierra Leone, It is obvious that Teachers are at the center of all school activities. To ease the challenges of teachers in teaching, it is important to search for the problems of teachers on teaching practices. This study was conducted to find out some of the challenges that teachers on teaching practice experience in schools in Sierra Leone....
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The purpose of this article is to overview various challenges that prospective authors of quality systematic reviews should be prepared to address. These challenges pertain to all phases of the review process: from posing a critical question worthy of pursuit and executing a search procedure that is appropriately framed and transparently recorded, to discerning patterns and trends within the resulting data that speak directly to the critical question framing the review. For each of these...
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Accelerated learning programmes (ALPs) provide a fast-track second-chance opportunity to complete formal education, enabling disadvantaged children and youth to catch up with their peers. In 2005, after a preliminary pilot phase, the Government of Iraq, in partnership with the United Nations Children’s Fund (UNICEF) implemented an ALP initially in 10 of Iraq’s 18 governorates with the intention of providing an estimated 50,000 out-of-school, often traumatised and disenfranchised children...
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Non-completion in higher education is a persistent problem and even worse of a problem in higher online education. Although there is a lot of research on predictors of non-completion, less is known about what interventions resolve the non-completion problem and to what extent these interventions focus on relevant predictors of non-completion. To close that gap, the literature was systematically reviewed with a twofold aim: 1. Identify modifiable predictors of non-completion in higher...
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This article proposes a three-step methodological framework called computational grounded theory, which combines expert human knowledge and hermeneutic skills with the processing power and pattern recognition of computers, producing a more methodologically rigorous but interpretive approach to content analysis. The first, pattern detection step, involves inductive computational exploration of text, using techniques such as unsupervised machine learning and word scores to help researchers to...
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This systematic literature review seeks to provide information on the limitations and opportunities faced by the urban poor in leveraging the potential benefits of education. The review covers the period 1995–2017 and includes 66 articles. The analysis addresses: a) the educational achievement of the urban poor and b) the conditions under which they receive education. Results show that, despite attending school, the urban poor are not able to fully satisfy their right to education. Their...
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Traditional sample designs for household surveys are contingent upon the availability of a representative primary sampling frame. This is defined using enumeration units and population counts retrieved from decennial national censuses that can become rapidly inaccurate in highly dynamic demographic settings. To tackle the need for representative sampling frames, we propose an original grid-based sample design framework introducing essential concepts of spatial sampling in household surveys....
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The massive expansion of scientific literature on climate change1 poses challenges for global environmental assessments and our understanding of how these assessments work. Big data and machine learning can help us deal with large collections of scientific text, making the production of assessments more tractable, and giving us better insights about how past assessments have engaged with the literature. We use topic modelling to draw a topic map, or topography, of over 400,000 publications...
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