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In the present modern world, technology dominates to human lifestyle in every aspect. Massive development in technologies in recent decades affects directly education. Today it is difficult to think about education without the involvement of diversified kinds of technology (Ramírez-Montoya, 2018). Among these technologies, mobile telecommunication (MT) is very significant. Integration of MT with portable devices such as smartphones, tab computers, PDAs, and other portable communicative devices that have computing power with internet facilities for carrying out education emergence as mobile learning (ML) (Grant, 2019). On the other hand, the highest E-Learning growth rate of 17.3% on yearly basis is reported in Asia. While its global rate is 7.6% (Docebo, 2020), and according to Figure 1. Asian continent's digital marketing capability is growing rapidly (Statista, 2020). Hence, ML is a new phenomenon for educational stakeholders to extend their academic activities from traditional classroom activities to a new ubiquitous learning environment. Whereas, outdated obstacles for ML such as connectivity, accessibility, and cost are minimized with the huge advancement in mobile internet penetrations and curtail in smartphone market prices day by day (Bahia & Suardi, 2019).
Figure 1.
Digital marketing capability of Asian Tertiary Education
Besides teachers and learners, today, like to use ML as a teaching and learning medium due to facilities, include such as anytime-anywhere learning, pervasive learning, etc. (Ateş-Çobanoğlu, 2020). These facilities are empowered by various ML tools and these ML tools influence both teacher and learner to use them in various academic activities. ML tools can be integrated into mobile devices in the device production stage or can be added as an add-on or third-party subroutine in customizable ML environments. For instance, Moodle is the popular open-source content management system that can add functionalities as add-ons call plugins. Teachers and learners able to add ML tools as Moodle plugins for their ML environment or can be built their plugins and added to their ML environment (Dougiamas M., 2020). The researches have conducted many studies in ML related to numerous fields such as health, management, engineering, humanities, and social sciences and identified different impact factors (Larentis, Barbosa, & Barbosa, 2020), (Klein, Junior, Silva, Barbosa, & Baldasso, 2018). But lack of researches can be found in modeling the influencing factors for learners and teachers to use ML tools in academic activities. In this study, the authors intend to find the main influencing factors for learners and teachers to use ML tools for adopting ML in higher education. Therefore, as the contribution in this study, the authors proposed an impact model for influencing factors on ML tools which can be used when developing an applicable and sustainable ML environment in higher education. This article proposed an impact model for ML adoption using ML tools makes different research output compared to the existing literature.
The paper is structured as follows: next in the ML tools section, the ML tools considered in this study are described. The literature review section describes previous research related to ML and ML associated models. Also, it accumulates the artifacts for formulating the proposed model. The next impact model and hypothesis section describe the model creation and hypothesis generation. The system used in this study is described under the section system function and architecture. The next sections are methodology, results, discussion, and conclusion and implication.
TopIn ML, teachers and learners can use a variety of ML tools. However, we have selected six types of most popular ML tools for this study.