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

Abstract

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.

Encouraging teacher participation in Professional Learning Communities: exploring the Facilitating or restricting factors that Influence collaborative activities

“…Effectively community interactions are dependent on constant activity and content exchanges between learning community members and participants. This increases collaborative learning performance, which is consistent with the results stated in the existing literature (e.g., Abdigapbarova & Zhiyenbayeva 2022 ; Tsiotakis & Jimoyiannis, 2016 ; Frei-Landau et al, 2022 ; vanOostveen et al, 2019 ; Yücel & Usluel, 2016 ; Zalavra & Papanikolaou, 2022 ).…”

“…The broad scope of professional knowledge and increasing complexities associated with topic knowledge require teachers to adopt a highly personalized and cooperative learning model, as well as the practical operations of collaborative learning (Frei-Landau et al, 2022 ; Garcia, 2021 ; vanOostveen et al, 2019 ; Yadegaridehkordi et al, 2019 ; Zalavra & Papanikolaou, 2022 ). This consequently allowed them to focus their efforts on collaborative learning and professional development in order to transform real and context-based ideas into next generation course plans.…”

Assessing the effect of innovation diffusion and technology readiness theories on attitude, behavioral intention and implementation of smart learning

“…Accordingly, this study offers a broad IDT emphasis by accounting for the dual effect of the smart learning implementation and the whole process (Frei-Landau et al, 2022). Since IDT can serve as a comprehensive conceptual framework for understanding the diffusion of innovation, many scholars (Agag and El-Masry, 2016;El Alfy et al, 2017;Ferreira et al, 2014;Lou et al, 2017) have combined IDT with other theoretical models to conduct empirical studies (Shiau et al, 2018).…”

“…Accordingly, this study offers a broad IDT emphasis by accounting for the dual effect of the smart learning implementation and the whole process (Frei-Landau et al. , 2022).…”

How can technology leverage university teaching & learning innovation? A longitudinal case study of diffusion of technology innovation from the knowledge creation perspective

“…To reveal how the dynamic interactions between individuals and organisations regarding technology adoption evolve and produce innovation, we combine Nonaka et al ( 2001 ), Nonaka et al ( 2006 )) organisational knowledge creation theory with the diffusion of TETL innovation framework in HEIs (Frei-Landau et al, 2022 ; Graham et al, 2013 ; Pinho et al, 2021 ; Rogers, 2003 ) to conceptualise the dynamics and innovation in universities.…”