The development of Internet and Web 2.0 technologies has changed the provision of
education. The online learning environment is the product of these changes where learners
are at their home far from school. In online learning where there is no instructor or classmates,
students’ self-directed learning, learning self-efficacy, and learning engagements are
important variables to be successful in their academic performance and to minimize dropout
rates from the program. This study aimed to investigate the prediction of demographic factors
and SDL variables on students’ learning self-efficacy and student engagement and to validate
the prediction of learning self-efficacy on students’ learning engagement in online learning
environments. This study used a cross-sectional survey design, specifically an online survey,
to collect data from students of higher education institutions in Taiwan. Data were collected
from 354 participants from higher education students of different fields and years of study.
For data analysis, an independent t test and ANOVA were used to answer the question of
whether gender and years of study had a different effect on students’ learning self-efficacy
and learning engagement in online learning environments, and the remaining research
questions were analyzed using two-step structural equation modeling (SEM). These are (1)
confirmatory factor analysis (CFA; measurement model) to determine how well the latent
variables (motivation, self-monitoring, self-management, learning self-efficacy, and learning
engagement) (constructs) are based on indicators (items) as measurement methods and (2)
structural equation modeling (SEM) to evaluate the research model and the hypotheses, to
assess one or more contingent relationships connecting the constructs of the hypothesized
model using Mplus. The result of the study showed that demographic factors do not predict
students’ learning self-efficacy and engagement of higher education students in online
iv
learning environments. From the SDL components, motivation directly positively predicted
learning self-efficacy, behavioral, cognitive, and emotional engagement significantly.
Similarly, self-monitoring directly positively predicted learning self-efficacy and all three
components of learning engagement. However, self-management positively and directly
predicted learning self-efficacy significantly but did not predict any of the three components
of learning engagement. The result of the study also showed that learning self-efficacy
partially mediated the effect of motivation and self-monitoring on learning engagement and
fully mediated the effect of self-management on learning engagement of higher education
students in online learning environments. Finally, necessary discussion and implications of
the study for practice are given.

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