Predicting Academic Success in Large Online Courses at a Mega ODL University

Saykılı A., Aydın S., Uğurhan Y. Z. C., Öztürk A., Birgin M. K.

TECHNOLOGY, KNOWLEDGE AND LEARNING, vol.0, no.0, pp.1-26, 2024 (ESCI)


Learning analytics offer unprecedented opportunities for tracking and storing learningbehaviors, thereby providing chances for optimizing learner engagement and success. Thelimited adoption of learning analytics by educational institutions hinders efforts to optimizelearning processes through organizational and educational interventions, especially inopen and distance learning settings. Therefore, this study attempts to investigate whetherlearning analytics data and learner demographic characteristics predict academic success intwo distinct online large-scale undergraduate courses delivered at a mega distance teachinguniversity. The prediction research design was employed, and correlation and multipleregression analyses were conducted to investigate the relationships between the predictorand outcome variables in this study. The results revealed that both learning analyticsand learner demographics significantly predict academic success. However, there weredisciplinary differences in terms of access to learning resources, their relative impact onacademic success, and the impact of learner demographics on academic success.