Though internationalisation at home is a relatively recent concept, it has already been embraced widely, particularly in northern and western Europe. Internationalisation at home aims to bring internationalisation to all students through the home curriculum. It is therefore primarily about teaching and learning, which implies that lecturers are increasingly becoming prominent players in internationalisation. After all, they are the ones who create learning environments with international and intercultural dimensions. In today’s blog, internationalisation at home expert Jos Beelen looks at where the international officer fits in.
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Geen digitale vaardigheden zonder kwetsbaarheid. Wat bedoelt Nick Degens, Lector User-Centered Design aan de Hanzehogeschool Groningen, daar precies mee? Digitalisering heeft vrijwel alle sectoren veranderd. In zijn blog gaat Nick in op digitale vaardigheden in het bedrijfsleven en onderwijs, en wat er voor nodig is om digitale vaardigheden integraal onderdeel te maken van het onderwijs.Deze blog maakt onderdeel uit van een reeks aan blogs over digitale competenties, in aanloop naar en rondom de lancering van de Digitale CompetentiePeiler 8 november.
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Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
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