Early prediction of learners at risk in self-paced education: A neural network approach

To address the demands of modern education and increase flexibility, many higher education institutions are considering self-paced education programs. However, student retention is yet a widely recognized challenge faced in self-paced education. While many studies have examined the potential of the use of data about student interaction with learning technologies to predict student success, studies that focus on self-paced education are scarce. To address this gap in the literature, this paper reports on the findings of a study that has investigated the performance of a well-known