The readiness of Iraqi EFL teachers to use mobile learning in teaching English in schools

Abstract

The importance of mobile learning (M-learning) has increased during the outbreak of Coronavirus disease 2019 (COVID-19). Level of English foreign language (EFL) teachers to use the technology should be assessed to understand the perception and readiness of EFL teachers to use M-learning. Building on existing literature, this study proposed that performance expectancy, effort expectancy, social influence and facilitating condition will affect positively the readiness to use M-learning. The study also proposed attitude as a mediator and technology self-efficacy as a moderator. The data was collected from 163 EFL teachers in Iraq. The findings showed that facilitating condition, performance expectancy, effort expectancy, and social influence affected positively the readiness to use M-learning. Attitude fully mediated the effect of performance expectancy, effort expectancy, social influence on readiness to use M-learning. Technology self-efficacy did not moderate the effect of the variables but it has a significant direct effect on readiness to use M-learning. Decision makers are recommended to enhance the infrastructure and to initiate training courses to enhance the information technology knowledge of the EFL teachers.

Model of intention and actual use mobile learning in higher education institutions in Iraq

“…The measurement of variables such as PE, EE, SI, FC, ITUML, and AU were adopted from [8]- [10]. Measurement of PEN was adopted from [17], measurement of SE was adopted from [23], measurement of enjoyment was adopted from [29], measurement of QoS and satisfaction was adopted from [31]. Prior to data collection, the measurement was validated, and a pilot study was carried out.…”

“…Prior literature on ML is dominated by the technical approach which examines the usage of ML from the network, programs, software, and hardware perspectives, while the behavioral approach is examined less frequently [16]. Among the behavioral approach, the use of targeted analysis model (TAM) which is the technology acceptance model by [17] dominates the literature. However, when it comes to using models such as UTAUT, the percentage dropped to between 1%-4% of the previous studies [3], [4].…”

Continued Usage Intention of Mobile Learning (M-Learning) in Iraqi Universities Under an Unstable Environment: Integrating the ECM and UTAUT2 Models

“…Iraq's diverse population, with various ethnicities and languages, presents a unique context that necessitates localized content and multilingual support in m-learning platforms. This complexity is underscored by Mussa and Sazalli (2021), who emphasize the significance of cultural factors in m-learning. Therefore, a comprehensive exploration of contextual factors in Iraq is vital for optimizing m-learning strategies that accommodate the region's distinct sociocultural dynamics, technological limitations, and security considerations, ultimately enhancing the adoption and effectiveness of m-learning in Iraqi universities (Younis et al, 2020(Younis et al, , 2023.…”

Prediction of student satisfaction on mobile-learning by using fast learning network

“…Prediction of student satisfaction on mobile-learning by using fast learning network (Laman Radi Sultan) 489 mobile technologies to e-learning within the concept of distance learning (d-learning), the idea of M-learning has made great progress in the education sector [14]. Many aspects can influence the M-learning experience, including the quality of support for systems and networks, quality of material content such as texts (i.e., e-books and slides), audios, videos, pictures, combined forms, learning time, learning location, evaluations, and capabilities of the mobile device [15].…”