Publications

This systematic literature review sheds light on social inequalities in students’ access to and experiences of international student mobility (ISM) in the context of the Covid-19 pandemic. Following a scoping approach based on the 2020 PRISMA guidelines, it synthesises 48 empirical studies published in the most intense phase of the Covid-19 pandemic, namely between January 2020 and June 2022.

The effectiveness of embedding methods is crucial for optimizing text classification performance in Automated Machine Learning (AutoML). However, selecting the most suitable pre-trained model for a given task remains challenging. This study introduces the Corpus-Driven Domain Mapping (CDDM) pipeline, which utilizes a domain-annotated corpus of pre-fine-tuned models from the Hugging Face Model Hub to improve model selection. Integrating these models into AutoML systems significantly boosts classification performance across multiple datasets compared to baseline methods. Despite some domain recognition inaccuracies, results demonstrate CDDM’s potential to enhance model selection, streamline AutoML workflows, and reduce computational costs.

The paper cluster comprises four systematic reviews assessing the impact of the Covid-19 pandemic on (1) macro-level flows of internationally mobile students, (2) social inequalities in access to, during, and in the wake of study abroad periods, (3) the social and cultural integration of international students, and (4) labour market expectations and experiences of internationally mobile students. Taken together, these reviews provide a comprehensive overview of the state of global research on how the pandemic has influenced several key aspects of ISM, simultaneously highlighting significant heterogeneities in terms of its impact on the different actors involved in ISM, including students, higher education institutions, and governments.