Self-Regulation, Teaching Presence, and Social Presence: Predictors of Students’ Learning Engagement and Persistence in Blended Synchronous Learning

Blended synchronous learning (BSL) is becoming increasingly widely implemented in many higher education institutions due to its accessibility and flexibility. However, little research has been conducted to explore students’ engagement and persistence and their possible predictors in such a learning mode. The purpose of this study was to investigate how to facilitate students’ engagement and persistence in BSL. In detail, this study used structural equation modeling to explore the relationships among specific predictors (self-regulation, teaching presence, and social presence), learning engagement, and learning persistence in BSL. We recruited 319 students who were enrolled in BSL at a Chinese university. The online survey was administered to gather data on the variables of this study. The results demonstrated that self-regulation, teaching presence, and social presence were positively associated with learning engagement. Self-regulation and learning engagement were positively associated with learning persistence. Moreover, learning engagement mediated the relationships between self-regulation, teaching presence, social presence, and learning persistence. This study suggests that self-regulation, teaching presence, and social presence are significant predictors for student learning engagement and persistence in BSL.

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.)

Investigating Students’ Adoption of MOOCs during COVID-19 Pandemic: Students’ Academic Self-Efficacy, Learning Engagement, and Learning Persistence

Students’ learning environments are significantly influenced by massive open online courses (MOOCs). To better understand how students could implement learning technology for educational purposes, this study creates a structural equation model and tests confirmatory factor analysis. Therefore, the aim of this study was to develop a model through investigating observability (OB), complexity (CO), trialability (TR), and perceived usefulness (PU) with perceived ease-of-use (PEU) of MOOCs adoption by university students to measure their academic self-efficacy (ASE), learning engagement (LE), and learning persistence (LP). As a result, the study used an expanded variant of the innovation diffusion theory (IDT) and the technology acceptance model (TAM) as the research model. Structural Equation Modeling (SEM) with Smart-PLS was applied to quantitative data collection and analysis of 540 university students as respondents. Student responses were grouped into nine factors and evaluated to decide the students’ ASE, LE, and LP. The findings revealed a clear correlation between OB, CO, and TR, all of which were important predictors of PU and PEU. Students’ ASE, LE, and LP were affected by PEU and PU. This study’s established model was effective in explaining students’ ASE, LE, and LP on MOOC adoption. These findings suggest implications for designing and developing effective instructional and learning strategies in MOOCs in terms of learners’ perceptions of themselves, their instructors, and learning support systems.