This paper surveys the state of the art on prediction in MOOCs through a systematic
literature review (SLR). The main objectives are: first, to identify the characteristics of the …

Several studies have been conducted related to dropouts from on-campus and distance
education courses. However, no clear definition of dropout from academic courses was …

Learning Analytics focuses on the collection and analysis of learners' data to improve their
learning experience by providing informed guidance and to optimise learning materials. To …

To successfully reduce student attrition, it is imperative to understand what the underlying
determinants of attrition are and which students are at risk of dropping out. We develop an …

The second edition of this bestseller provides a practical and accessible introduction to the
main concepts, foundation, and applications of Bayesian networks. This edition contains a …

In this paper, a dropout prediction method for e-learning courses, based on three popular
machine learning techniques and detailed student data, is proposed. The machine learning …

With recent advances in mobile learning (m-learning), it is becoming possible for learning
activities to occur everywhere. The learner model presented in our earlier work was …

Students are characterized by different learning styles, focusing on different types of
information and processing this information in different ways. One of the desirable …

In this paper we present eTeacher, an intelligent agent that provides personalized
assistance to e-learning students. eTeacher observes a student's behavior while he/she is …

Persistence in learning processes is perceived as a central value; therefore, dropouts from
studies are a prime concern for educators. This study focuses on the quantitative analysis of …