Teaching and Learning with Technology During the COVID-19 Pandemic: Highlighting the Need for Micro-Meso-Macro Alignments

Joke Voogt; Gerald Knezek

doi:10.21432/cjlt28150

Authors

Joke Voogt University of Amsterdam, Department of Child Development and Education https://orcid.org/0000-0001-5035-9263
Gerald Knezek University of North Texas Department of Learning Technologies

DOI:

https://doi.org/10.21432/cjlt28150

Keywords:

micro-meso-macro alignments, COVID-19, pandemic, EDUsummIT, learning contexts, teacher support, partnerships

Abstract

All over the world teaching and learning transitioned to forms of online education due to the COVID-19 pandemic. In this contribution, we recognize challenges that this disruptive change brought about for teachers and learners. We reflect on these challenges, based on discussions at EDUsummIT2019 in Quebec about the theme “Learners and learning contexts: New alignments for the digital age”. Informed by theoretical conceptualization and empirical evidence we identify micro-meso-macro alignments that need to be in place to move education into the digital age: alignments for quality learning contexts, alignments in support for teachers, and alignments through partnerships.

Author Biographies

Joke Voogt, University of Amsterdam, Department of Child Development and Education

Joke Voogt is Professor of ICT and Curriculum at the University of Amsterdam and Co-founder of EDUsummIT. Her research focuses on the integration of Information and Communication Technology in curriculum and educational practice in national and international settings.

Gerald Knezek, University of North Texas Department of Learning Technologies

Gerald Knezek is Regents Professor of Learning Technologies at the University of North Texas (UNT), USA, Co-director of the Institute for the Integration of Technology into Teaching and Learning (IITTL) and Co-founder of EDUsummIT. His teaching and research focuses on technology integration, measurement of socio-emotional constructs, STEM education and multidimensional scaling methods.

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