Background: The growth of online education has provided flexibility and access to a wide range of courses. However, the self-paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at-risk students, but multiple issues have not been…

Background: There have been several studies that involved technology-based self-monitoring procedures to increase on-task behavior. Although there are continued advancements in technology application such as functions that are embedded in the application (e.g., reinforcement, feedback), the appropriate use of technology is required to be…

Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning…

Background: The impact of mobile technology usage on student learning in various educational stages has been the subject of ongoing empirical and review research. The most recent meta-analyses on various types of mobile technology use for potential benefits of learning covered the empirical studies up to about nine years ago. Since then, the use…