# Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning +
## 6 Conclusions  
This work introduces definitions of global and local persistence and presents a novel method to determine students' local persistence using low-level events. This model can identify the local persistence of a student on the scale 0-1, taking into account if a student keeps on trying an exercise s/he failed until solving it correctly. The model could be used with several educational platforms provided that the number of exercises and attempts on them per student are available. However, this model could be customized if the educational context is known. This model was applied to compute students' local persistence in 28 SPOC. The analysis of the prevalence of local persistence allows identifying several profiles. Most students have either a fair/moderate local persistence (between 0.3 and 0.7) or a high local persistence (above 0.9). Results allowed identifying two profiles of students with low engagement but with very different values (very high or relatively low) of local persistence. In both groups, students attempt very few exercises; however, in one group students almost always finish what they attempt, unlike in the other group. Instructors should try to motivate students that belong to these two groups so that they complete the activities they attempt. There are also groups in which students get high grades because they get many exercises correct at the first attempt, although they are not persistent at the local level. The analysis of the evolution of local persistence over time shows that although local persistence is part of students' personality, it can be highly affected by the course design (in line with what suggested [26; 35]) and it can fluctuate over the weeks in a course. This suggests that instructors may need to reflect about the intended local persistence in their course design to try to motivate students to be more persistent, even when the exercises do not have limited options and students may need more effort to solve them. When local persistence is compared to other variables, results also show that there is no statistically significant difference in terms of local persistence between persistent students at the global level and students who drop out the course. This can happen, e.g., when students attempt few exercises and/or use trial and error strategies. No relationship is found between watching videos and local persistence either. In contrast, the average grade is positively related with local persistence, as also found in [34; 35]. However, there are cases where the average grade is low but the local persistence is high, which probably means that students are not learning, but getting the right answers by brute force instead. For those cases, educators should try make students reflect after each attempt so that they can learn from their mistakes and try to use these to get to the correct answer. For example, feedback messages could be provided after incorrect answers to make students reflect and/or the exercise could be blocked for a while (e.g., a few minutes) to give student time to think about how to solve the exercise. In any case, it would also be important that educators would encourage their students to complete all the exercises to grasp the course contents. In addition, local persistence can slightly improve predictive models. When predicting global persistence/dropout and average grade, the percentage of attempted exercises is a prominent variable (as it also happened in [29]) and can achieve good results by itself. This limits the predictive power of other variables. However, when the percentage of attempted exercises is not used, local persistence can add certain value to the models. Predictive models also show that accurate early predictions can be achieved in the first weeks of the course. This would allow instructors to make decisions that can have an impact on learners. Global persistence related to activity is an exception for these early predictions.