# Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning +
## Related Work
### Global Persistence  
Global persistence, as it is understood here, is the ability to keep on working on a course/program in the long term. Following an educational experience has an implicit difficulty since the learning process usually requires some effort. However, global persistence does not usually take into account specific explicit difficulties. Global persistence can be related to dropout (e.g., a student who drops out a course is not persistent (at the global level) in the course, or a student who do not finish a degree). In fact, global persistence might be considered a binary variable, and in that case, a student who does not persist in a course or a degree is usually a dropout. Because of that, this article considers dropout and global persistence as similar terms. While there can be several specific definitions of dropout [19] (e.g., a student who drops out a course can be a student who fails a course, does not complete it, does not frequently interact with it, etc. ), it can be said that dropout is related to the absence of certain actions that should be done in a course in order to complete it and/or succeed at it. Dropout is an important issue because its rates can sometimes be very high, both at course and academic (whole degree) level [20]. Several studies have tried to analyze the reasons behind low global persistence and dropout. [PERSON] and [PERSON] [21] analyzed the effect of using blended learning and concluded that blended learning had a negative effect on student's global persistence for adult learners, although it had an overall positive effect on the passing rates. In addition, [PERSON] and [PERSON] [22] concluded that teachers' beliefs about students' capability of being taught had a high impact on dropout rates. Furthermore, other articles, such as [23; 24], also analyzed students' emotions and found that pessimistic students were more likely to dropout, and anxiety, confusion and frustration were the most correlated emotions related to dropout. Current research has also focused on developing predictive models. Forecasting global persistence and/or dropout is one of the main objectives of predictions in the literature, together with students' performance [6]. Both dropout (and global persistence) and performance are very related, although being persistent at the global level does not mean success (good performance) because some students may engage with all course activities and complete them, but still fail. [PERSON] et al. [25] predicted dropout (considering those students who did not complete the activities of the course) using logistic regression with Moodle data. These authors also evaluated how interventions (send e-mails to and phone potential dropouts) could reduce dropout rate by 14%. In addition, [PERSON] et al. [26] also used Moodle logs to predict learners' performance in nine undergraduate blended learning courses. Their analysis showed how important the course context is, as engagement with the Learning Management System (LMS) and the features used in it could differ between courses. This entails that some variables could be significant predictors for some courses but not in others because of the course context. Despite this fact, research has analyzed the predictive power of different features in dropout to discover to what extent they can be good predictors in different contexts. Authors have mainly focused in variables related to activity in an online platform, students' performance (scores on summative/formative tasks or courses), engagement with videos, forum activity, and demographics [6] (although not all variables are always available or analyzed). For example, [PERSON] and [PERSON] [27] predicted dropout using data from an online course and combining multiple information sources. Particularly,they considered variables related to engagement with course assignments, watching videos, accessing to course objects, wiki and forum. In addition, [PERSON] et al. [28] predicted dropout of high school students.