How do students deal with forced digitalisation in teaching and learning? Implications for quality assurance

QUALITY ASSURANCE IN EDUCATION(2023)

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摘要
Purpose This paper aims to investigate student subgroups' responses to the coercive digitalisation of teaching and learning processes during the pandemic. Respective variance is discussed in terms of digital inequality and is interpreted as a need to individualise teaching and learning and quality assurance practices. Design/methodology/approach This study uses data from surveys (N = 955) on student perceptions of the introduction of emergency digitalisation - an important aspect of higher education. The authors perform latent class analyses to identify student subgroups. The students were asked to rate digital learning processes and their overall learning experiences. Findings The identified student subgroups are proponents, pragmatics and sceptics of digitalised teaching and learning processes. These subgroups have different preferences with regard to teaching and learning modes of delivery, which implies the relevance of individualised educational services and respective quality assurance practices to reflections on improvement needs. Research limitations/implications The data are from a single, typical German university; therefore, the scope of the results may be limited. However, this study enriches future research on the traits of student subgroups and students' coping strategies in an ever-changing learning environment. Practical implications The findings may help individualise universities' counselling services to enhance overall teaching performance and quality assurance practices in a digitalised environment. Originality/value The findings provide insights into students' responses to the COVID-19 pandemic and its impact on teaching and learning. This paper enriches the research on student heterogeneity and relates this to development needs of quality assurance practice.
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关键词
Coercive digitalisation, Latent class analysis, Teaching and learning, Individualisation, Latent class analyses
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