Using traces of behaviors to predict outcomes is useful in varied contexts ranging from buyer
behaviors to behaviors collected from smart-home devices. Increasingly, higher education …

This study sought to identify significant behavioral indicators of learning using learning
management system (LMS) data regarding online course achievement. Because self …

A sense of community is central to student engagement and satisfaction. However, many
students struggle with developing connections in online programs. Drawing on interviews …

This study explores the interactivity of course-management systems (CMSs). First, this study
reviews the concepts of interactivity, interactivity dimension, and interaction type on the basis …

Online learning has become more popular in higher education since it adds convenience
and flexibility to students' schedule. But, it has faced difficulties in the retention of the …

This study explored how social networking technology can be used to supplement face-to-
face courses as a means of enhancing students' sense of community and, thus, to promote …

This study examined the prediction of dropouts through data mining approaches in an online
program. The subject of the study was selected from a total of 189 students who registered to …

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 …

This study analyses the online questions and chat messages automatically recorded by a
live video streaming (LVS) system using data mining and text mining techniques. We apply …

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 …