Mobile-learning adoption in teacher education amidst COVID-19: Identifying two critical stages by exploring teachers' emotions

ABSTRACT

Mobile learning (ML) is extremely relevant to distance teaching. Although much is known about ML usage in teacher education, less is known about crucial points in teachers' ML adoption process under constraints such as the COVID-19 pandemic. The aim of this exploratory case study was to gain insight into the ML adoption process, including its critical points, by examining teachers' emotion-related language. This study investigated the emotional response of 32 inservice teachers to Mobile Learning (ML) adoption while attending ML training during the COVID-19 pandemic. The data were collected using semi-structured interviews (10), focus groups (3), and participants' reflections (96) at five time points. The data underwent multilevel analysis (content and linguistic analyses), revealing two critical stages during the ML adoption process and indicating several factors that may affect the quality of emotional response, thereby promoting or impeding this process. The study highlights the critical sages and their related features that must be addressed to promote optimal ML adoption in teacher education in both routine and emergency conditions.