Learning Analytics Intervention Using Prompts and Feedback for Measurement of E-Learners' Socially-Shared Regulated Learning

The future of university learning in Sub-Saharan Africa has become increasingly digitally transformed by both e-Learning, and learning analytics, post-COVID-19 pandemic. Learning analytics intervention is critical for effective support of socially-shared regulated learning skills, which are crucial for twenty-first-century e-Learners. Socially-shared regulation is the major determinant of successful collaborative e-learning. However, most e-learners lack such skills thereby facing socio-cognitive challenges, due to the unavailability of intelligent support during learning. This research aims to investigate and understand the effect of Learning Analytics instructional support using feedback and prompts, on e-learners' SSRL indicators. A theoretical model was derived from these factors and built from selected features. Both survey data and behavioral trace data were employed in the Learning analytics-based intervention. In this paper, only a segment of the data is discussed. The e-learners' perceptions and feedback confirmed that Learning Analytics-based interventions using prompts and feedback are effective in promoting SSRL in collaborative e-learning contexts. The findings indicated that the success of SSRLA-based intervention be tied to support from instructors and academic counselors, particularly feedback on previous problems and quizzes. This will improve e-learners' SSRL skills for quality educational experience, hence motivate e-learners, and help lecturers to identify at-risk learners in web programming problem-based courses. In conclusion, without adequate utilization of the Learning Analytics interventional trace data, critical information about learners' behavior patterns in terms of their online interactivity with the course activities and their SSRL profiles and strategies cannot be disclosed leading to little improvement of e-Learning interventions.