Adoption of mobile technology for mobile learning by university students during COVID-19

Abstract

PurposeDue to the eruption of the COVID-19 pandemic, many universities were forced to shift from the traditional learning practices to digital learning. Hence, the purpose of this study is to evaluate the factors that affect the university student's adoption of mobile technologies for mobile learning (m-learning) in their learning process.Design/methodology/approachTechnology acceptance model (TAM) is incorporated to study the adoption of mobile learning by university students. Quantitative research technique is used as core research approach in this study. Structural equation modelling (SEM), which is a part of quantitative research method, was employed on the congregated data via a set of questionnaire from 268 University students. SEM is used to explore the relationships among the hypothesized constructs. SPSS and AMOS software were used for the analysis of data.FindingsThis study validated the updated TAM model and assessed the students' adoption of mobile technologies for m-learning during COVID-19. All the constructs of proposed model were found to be significant with more than 50% average variance extracted. It was found that two external constructs mobile system efficacy and mobile service efficacy appended in technology acceptance model show the direct positive effect on perceived usefulness and perceived ease of use constructs. However, hypothesized relationships were found to be unsupported among perceived usefulness and perceived ease of use. Furthermore, perceived usefulness and ease of use during m-learning impact the students' usage attitude which consequently impact the students' adoption behaviour towards adoption of mobile technology.Research limitations/implicationsSix constructs were considered for this study; however, mobile information quality for mobile learning was not included which could affect students' adoption criteria. Additionally, this study is limited to a country where future study needs validation of propose constructs in different demographic settings.Originality/valueNo study allied to the students' adoption of mobile technology for m-learning has accomplished in the context of India during COVID-19. Furthermore, TAM model has been updated with regard to the students' adoption of mobile learning during COVID-19 in Indian higher education setting.

Students' attitudes on the use of Zoom in higher educational institutes of Sri Lanka

“…, 2020). The path values of H4 ( β = 0.354, t = 4.539, p = 0.000) and H5 ( β = 0.224, t = 2.695, p = 0.007) suggest that the PU and PEU of Zoom impact ATT toward online learning, which is consistent with earlier research that confirms the relationships (Zaidi et al. , 2021).…”

“…These findings are consistent with previous studies (Cathy et al, 2015;Cakır and Solak, 2015;Ibrahim et al, 2017;Al Kurdi et al, 2020). The path values of H4 (β 5 0.354, t 5 4.539, p 5 0.000) and H5 (β 5 0.224, t 5 2.695, p 5 0.007) suggest that the PU and PEU of Zoom impact ATT toward online learning, which is consistent with earlier research that confirms the relationships (Zaidi et al, 2021). The path value of H8 (β 5 0.328, t 5 4.625, p 5 0.000) indicates that BI toward Zoom positively impacts the AU of Zoom.…”

Self-Regulation in E-Learning Environment

“…Not only computer equipment, Internet connection, and other necessary devices, but even ergonomic factors such as furniture, lighting, noise, temperature, and chromatography can cause difficulties in elearning [86]. The electronic environment not only limits the possibilities of the teacher's direct influence on immersion but also can offer new ways of engagement (interactive elements [87], augmented [88] and virtual reality [89], mobile technology [90], etc.). Boekaerts's model (Figure 3) demonstrates how feedback can influence the choice of a student's strategy.…”

Academia's responses to crisis: A bibliometric analysis of literature on online learning in higher education during COVID‐19

“…Similar to Cluster 3, researchers tended to apply SEM to explore impacts of student beliefs (eg, self‐efficacy; El‐Sayad et al., 2021 ), perceived usefulness and ease of technology use (eg, Zaidi et al., 2021 ), and perceptions towards social presence (eg, Park & Kim, 2020 ) on engagement and satisfaction with online learning. Additionally, “self‐regulated learning” co‐occurred frequently with other psychological factors from Cluster 4.…”