Using Rogers' diffusion of innovation theory to conceptualize the mobile-learning adoption process in teacher education in the COVID-19 era

Using mobile learning (ML) has become exceedingly relevant in times of distant teaching. Although much is known about the factors affecting ML usage, less is known about the ML adoption process under constraints such as the COVID-19 pandemic. The aim of this exploratory case study was to gain insigh...

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Bibliographic Details
Published in:Education and information technologies 2022-11, Vol.27 (9), p.12811-12838
Main Authors: Frei-Landau, Rivi, Muchnik-Rozanov, Yulia, Avidov-Ungar, Orit
Format: Article
Language:English
Subjects:
Adoption
Analysis
Case studies
Computer Appl. in Social and Behavioral Sciences
Computer Science
Computers and Education
Coronaviruses
COVID-19
Distance Education
Education
Educational Innovation
Educational Technology
Epidemics
Faculty Development
Focus Groups
Handheld Devices
Information Systems Applications (incl.Internet)
Interviews
Israel
Pandemics
Preservice Teachers
Semi Structured Interviews
Teacher education
Teacher Improvement
Teachers
Technology Integration
Telecommunications
Training
User Interfaces and Human Computer Interaction
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Summary:Using mobile learning (ML) has become exceedingly relevant in times of distant teaching. Although much is known about the factors affecting ML usage, less is known about the 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 using the lens of Rogers' Diffusion of Innovation Theory. Participants were in-service (32) and preservice (29) teachers who attended ML training. Data were collected using semi-structured interviews (20), focus groups (6), and participants' reflections (183) at three time points. Data underwent multilevel analysis (content and linguistic analysis), revealing 12 themes that denote the ML adoption process and demonstrated intergroup similarities and differences. The study provides theoretical insight into the ML adoption process under crisis and highlights the features that must be addressed to promote optimal ML adoption in teacher education in both routine and emergency conditions.
ISSN:1360-2357
1573-7608
DOI:10.1007/s10639-022-11148-8