Temporal learning analytics to explore traces of self-regulated learning behaviors and their associations with learning performance, cognitive load, and student engagement in an asynchronous online course.

Self-regulated learning (SRL) plays a critical role in asynchronous online courses. In recent years, attention has been focused on identifying student subgroups with different patterns of online SRL behaviors and comparing their learning performance. However, there is limited research leveraging traces of SRL behaviors to detect student subgroups and examine the subgroup differences in cognitive load and student engagement. The current study tracked the engagement of 101 graduate students with SRL-enabling tools integrated into an asynchronous online course. According to the recorded SRL behaviors, this study identified two distinct student subgroups, using sequence analysis and cluster analysis: high SRL (H-SRL) and low SRL (L-SRL) groups. The H-SRL group showed lower extraneous cognitive load and higher learning performance, germane cognitive load, and cognitive engagement than the L-SRL group did. Additionally, this study articulated and compared temporal patterns of online SRL behaviors between the student subgroups combining lag sequential analysis and epistemic network analysis. The results revealed that both groups followed three phases of self-regulation but performed off-task behaviors. Additionally, the H-SRL group preferred activating mastery learning goals to improve ethical knowledge, whereas the L-SRL group preferred choosing performance-avoidance learning goals to pass the unit tests. The H-SRL group invested more in time management and notetaking, whereas the L-SRL group engaged more in surface learning approaches. This study offers researchers both theoretical and methodological insights. Additionally, our research findings help inform practitioners about how to design and deploy personalized SRL interventions in asynchronous online courses.

GPS sensor-based mobile learning for English: an exploratory study on self-efficacy, self-regulation and student achievement.

This study investigated the effects of self-efficacy and self-regulation on student achievement in a context-aware learning environment. Particularly, an innovative global positioning system (GPS) sensor-based mobile learning system was used to facilitate English learning of different plants on campus. A total of 41 university students participated in the study, divided into high and low groups of self-efficacy and self-regulation. The findings showed that both self-regulation and self-efficacy were significant predictors of learning achievement in the mobile context-aware learning (MCL) context. Moreover, while the GPS-based MCL learning session had positive effects on learning achievement, no significant increase in self-regulation or self-efficacy was found in either the high or the low group, supposedly due to the short duration of the activity. The participants found the system easy to use and useful, but they also raised critical concerns that can inform future improvements. We hope this exploratory study can serve as a starting point from which more interactive, user-friendly GPS sensor-based learning systems will be generated and more areas of application will be further explored to foster self-regulated, self-motivated ubiquitous learning of mobile learners.