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The definition of mobile learning (M-learning) is “learning that takes place when students have access to information whenever and wherever they are using mobile technology to engage in genuine actions as part of their learning” (Al‐Rahmi et al., 2021a; Martin & Ertzberger, 2013). M-learning presents a unique chance to draw on learners’ formal and informal learning experiences (Alturki & Aldraiweesh, 2022; Granić & Marangunić, 2019). Mobile computing devices’ mobility and flexibility enable learners to contextualize their learning in a useful way, apply what they learn to real-world challenges, and customize their learning (Sánchez-Prieto et al., 2019; Viberg et al., 2021). Since the idea of M-learning first emerged, information systems (IS) and educational specialists have examined ways to incorporate it into instructional practices. The fact that M-learning systems enable students to access their course materials over wireless networks “anytime, anywhere” is the basis for those researchers’ steadfast insistence on the importance of M-learning (Al-Emran & Teo, 2020; Al‐Rahmi et al., 2021b). Notwithstanding this enthusiasm, investing in M-learning technologies calls for an appreciation of students’ low incentive to use them for educational purposes (Aguilera-Hermida, 2020). Students must be aware of its benefits and incorporate it into their academic lives in order for M-learning platforms to be used for educational practices (Alghazi et al., 2021).
M-learning can be utilized to lessen the problems related to schooling, according to a number of studies (Al-Rahmi, et al., 2022a; Hill et al., 1977; Kong, 2018; Qashou, 2021). According to Al-Rahmi, Shamsuddin, Wahab, Al-Rahmi, Alismaiel, et al. (2022b), M-learning transforms an instructional strategy into a student-focused one that can foster meaningful, holistic learning experiences. Additionally, M-learning gives teachers access to a wide range of pedagogies, including group work, quizzes, and educational games, all of which may be used to cater to the unique learning preferences of students (Alturki & Aldraiweesh, 2022). The availability of instructional and evaluation materials at all times and locations is made possible through M-learning (Almaiah et al., 2019). M-learning makes it possible to use graphical science experiments, which can help learners better comprehend science ideas and provide comprehensive explanations of those subjects (Liu et al., 2021). M-learning enhances lecturers’ participation in their students’ education, which in turn enhances students’ drive and achievement in STEM-related topics, according to Gamage et al. (2022) and Kong (2018).
Many theoretical models, including the theory of reasoned action (TRA; Al-Emran et al., 2018), the technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT; Alghazi et al., 2021), and the theory of planned behavior (TPB; Ajzen, 1985), were used to comprehend the factors influencing the adoption of M-learning. Due to its simplicity, versatility, and soundness, TAM is thought to be one of the most often used theoretical models for forecasting the adoption of various technologies (Liu et al., 2021). More particularly, it was recently discovered that TAM was the most frequently utilized theoretical model for comprehending the adoption of M-learning (Aburub & Alnawas, 2019). TAM’s effective explanatory ability and successful validation using a number of measurement scales were other considerations (Al-Emran et al., 2018). The TAM’s fundamental variables, “perceived ease of use” and “perceived usefulness,” which examine how people embrace various technologies, have good empirical backing, increasing the model’s applicability across disciplines (Aburub & Alnawas, 2019; Hamidi & Chavoshi, 2018).