Abstract
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.