Abstract
The goal of this study was to develop a new model and conduct confirmatory factor analysis to learn more about how students use M-learning in higher education. The study is theoretically based on the unified theory of acceptance and use of technology (UTAUT) theory and the technology acceptance model (TAM). Theoretically, the factors related to the adoption of M-learning in higher education, identified as contributory to perceived ease of use, perceived usefulness, and attitudes towards M-learning and actual use of M-learning, were analyzed. A questionnaire survey was distributed to 362 university students who were randomly selected. Structural Equation Modeling (SEM)-AMOS was used for data analysis. Based on the findings, M-learning appears to be one of the most promising educational technologies for development in educational environments. Perceived facilitating conditions, performance expectancy, effort expectancy, social influence, and perceived enjoyment have a significant positive effect on the perceived ease of use and perceived usefulness, while performance expectancy has a negative effect on the perceived ease of use. Perceived ease of use and perceived usefulness have a positive and significant effect on attitudes towards using M-learning and actual use of M-learning. Therefore, we recommend lecturers encourage students to utilize M-learning for educational purposes in higher education.
Highlights
In recent years, M-learning systems have become an essential instrument for students and educators alike [1]
In order to investigate the use of M-learning in South Korea, Sung et al [59] used the unified theory of acceptance and use of technology and the results show that performance expectancy is associated with behavioral intention
The effects of factors of M-learning acceptance were explored by a complete model based on unified theory of acceptance and use of technology and technology acceptance model factors influencing the use of M-learning
Summary
M-learning systems have become an essential instrument for students and educators alike [1]. A technology acceptance model with a unified theory of acceptance and use of technology [19,21], which was developed to investigate the acceptance of computers in a workplace context, incorporates a unified theory of acceptance and use of technology factors with the technology acceptance model as key constructs in this model Some of these factors are facilitating conditions (FC), performance expectancy (PEX), effort expectancy (EEX), social influence (SI), perceived enjoyment (PE), perceived usefulness (PU), perceived ease of use (PEOU), attitude towards using (ATM), and actual use of M-learning (AUML). A smart classroom is positioned in the center of the surrounding area and can assist conventional classroom activities through computers and mobile phones [27,28] This technique contains a suggestive structure that allows the instructors to adapt and improve their teaching methods in response to the system’s instructions. Despite previous research demonstrating several benefits of M-learning, M-learning has not been successful in all universities due to differences in student attitudes and institutional culture
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