The Effectiveness of Online Platforms after the Pandemic: Will Face-to-Face Classes Affect Students’ Perception of Their Behavioural Intention (BIU) to Use Online Platforms?

Abstract

The purpose of this study is to investigate students’ intention to continue using online learning platforms during face-to-face traditional classes in a way that is parallel to their usage during online virtual classes (during the pandemic). This investigation of students’ intention is based on a conceptual model that uses newly used external factors in addition to the technology acceptance model (TAM) contrasts; hence, it takes into consideration users’ satisfaction, the external factor of information richness (IR) and the quality of the educational system and information disseminated. The participants were 768 university students who have experienced the teaching environments of both traditional face-to-face classes and online classes during the pandemic. A structural equation modelling (SEM) test was conducted to analyse the independent variables, including the users’ situation awareness (SA), perceived ease of use, perceived usefulness, satisfaction, IR, education system quality and information quality. An online questionnaire was used to explore students’ perceptions of their intention to use online platforms accessibly in a face-to-face learning environment. The results showed that (a) students prefer online platforms that have a higher level of content richness, to be able to implement the three dimensions of users’ situation awareness (perception, comprehension and projection); (b) there were significant effects of TAM constructs on students’ satisfaction and acceptance; (c) students are in favour of using a learning platform that is characterised by a high level of educational system quality and information quality and (d) students with a higher level of satisfaction have a more positive attitude in their willingness to use the online learning system.

The Effects of the COVID-19 Pandemic on the Digital Competence of Educators

“…This approach has been widely used in the social science fields to analyze and evaluate the suitability of the theoretical model through empirical data [60,84,85]. SEM is also appropriately considered to test the assumptions in student or teacher acceptance of online learning media [86,87], such as micro-lectures. Meanwhile, the literature on SEM has demonstrated the need to adopt different approaches, based on the type of study, as well as sample normality and size [88].…”

“…According to [85], increasing sample size to 100 is recommended in order to reach acceptable levels of statistical power. Roldán and Cepeda [86] proposed specifying the size effect for each regression while consulting the power tables developed by Cohen [87] to obtain a more precise assessment.…”

“…The expected effect size (ES) and the significant alpha (α) and power (β) values were used to calculate the minimum necessary sample size. For this purpose, a multiple regression study was conducted with four predictors, an average effect size (ES) of 0.15, an alpha of 0.05, and a power of 0.95, in accordance with Cohen [87]. In this case, the minimum sample result was N = 129 participants.…”

Factors Influencing Students’ Continuous Intentions for Using Micro-Lectures in the Post-COVID-19 Period: A Modification of the UTAUT-2 Approach

“…This approach has been widely used in the social science fields to analyze and evaluate the suitability of the theoretical model through empirical data [60,84,85]. SEM is also appropriately considered to test the assumptions in student or teacher acceptance of online learning media [86,87], such as micro-lectures. Meanwhile, the literature on SEM has demonstrated the need to adopt different approaches, based on the type of study, as well as sample normality and size [88].…”

Measuring Institutions’ Adoption of Artificial Intelligence Applications in Online Learning Environments: Integrating the Innovation Diffusion Theory with Technology Adoption Rate

“…Trialability (TR), on the other hand, is the extent to which society trusts innovations. Trialability refers to the degree to which learners are encouraged to use AI technologies and applications in the future [28][29][30]. Complexity (CO) is the end user's perceived level of effort required to understand inventions and their simplicity of use.…”