Graduating in uncertain times: The impact of COVID-19 on recent graduate career prospects,trajectories and outcomes

This article examines the impacts of the COVID-19 pandemic on recent UK graduates' initial employment outcomes and how they experience the transition into a challenging labour market context. We draw on longitudinal survey and interview data, collected from recent graduates who had mainly graduated during the onset of the COVID-19 pandemic in summer 2020 that examines graduate perception of the labour market, impacts on labour market entry impacts and early career progression and effects of periods of unemployment or under-employment. The article shows some of the main impacts of the recent pandemic-affected labour market, including: widespread concerns about job opportunities and employer support, the perceived employment impacts of the pandemic and early signs of scarring and labour market disorientation amongst those who were struggling to find employment of their choice. Such experiences are clearly intensified during the specific COVID-19 context, but the policy implications they raise have wider relevance for supporting graduates during future periods of labour market volatility.     

Perception of the cursive handwriting movement in writers and pre-writers

The objective of this study was to confirm the existence of knowledge relating to the cursive writing movement for French pupils in 3rd year of kindergarten, 2nd grade and 5th grade of elementary school. 141 pupils were asked to watch a visual presentation of cursive handwriting to determine whether they were able to detect violations of two rules of handwriting: continuity and sequentiality of the cursive handwriting movement. Our results showed progressive development of the understanding of characteristics of the cursive handwriting movement, with different developmental trajectories of knowledge for the different rules. The ability to detect continuity of the cursive writing movement developed earlier than the ability to detect sequentiality. Correct decisions were not always accompanied by correct justifications, which developed more slowly than detection of rule violations.     

A machine learning approach to investigating the effects of mathematics dispositions on mathematical literacy

Mathematics competency is fast becoming an essential requirement in ever greater parts of day-to-day work and life. Thus, creating strategies for improving mathematics learning in students is a major goal of education research. However, doing so requires an ability to look at many aspects of mathematics learning, such as demographics and psychological dispositions, in an integrated way as part of the same system. Large-scale assessments such as the Programme for International Student Assessment (PISA) provide an accessible and large volume of coherent data, and this gives researchers the opportunity to employ data-driven approaches to gain an overview of the system. For these reasons, we have used machine learning to explore the relationships between psychological dispositions and mathematical literacy in Australian 15-year-olds using the PISA 2012 data set. Our results from this strongly data-driven approach re-affirm the primacy of mathematics self-efficacy and highlight novel complex interactions between mathematics self-efficacy, mathematics anxiety and socio-economic status. In this paper, we demonstrate how education researchers can usefully employ data-driven modelling techniques to find complex non-linear relationships and novel interactions in a multidimensional data set.