Educational quality and technological complexity on recognition of enhanced learning platform in developing countries using PLS-SEM in a post COVID era.

The e-learning platform provides a new teaching-learning channel in which instructors provide information to learners irrespective of method used to access the platform. The purpose of this study is to examine instructors' acceptance of e-learning in Nigerian universities. The study adopted a quantitative approach, with a total of 299 questionnaires collected from instructors. The result was analyzed using PLS-SEM. The study results indicate the factors affecting instructors' adoption of e-learning platforms and the subsequent impact on instructors' behavioral intention, and consequenly, an impact on the acceptance of the e-learning platform in Nigeria. Based on the study, self-efficacy, educational quality, and ease of use, perceive usefulness, and behavioral intention were found to be predictors for instructors' acceptance of the e-learning platform in Nigeria. Also, the results drawn from the study revealed that there are individual challenges that come from the use of the e-learning platform. We discussed both theoretical and educational implications.

Examining factors influencing e-learning engagement among university students during covid-19 pandemic: a mediating role of “learning persistence”

The sudden outbreak of COVID-19 made universities switch rapidly to e-learning, which enabled continuous access to education. Thus, the evaluation of e-learning engagement is essential to ensure students are engaged in their studies just as it is in the conventional face-to-face classroom. The students are totally in control of their participation in the e-learning platform, and little is known about what instructors can do to facilitate their engagement in the platform during the COVID-19 pandemic. Similarly, the extant literature has reported that one of the challenges posed by e-learning is that many university students engage in off-task behaviors during lectures. Therefore, a systematic model for assessing university students’ e-learning engagement, learning persistence, and academic benefits was developed based on a thorough literature review. Data was collected from 274 students using e-learning platforms, and this study adopted the quantitative method of Partial Least Square-Structural Equation Modelling to validate the model empirically. A total of nine first-order constructs were used to measure e-learning engagement. They all explained 75% of the variance of e-learning engagement, while 42% and 66% explained the variance of learning persistence and academic benefits, respectively. All the hypotheses tested were positive, except for the relationship between learning persistence and academic benefits. [ FROM AUTHOR] Copyright of Interactive Learning Environments is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)