# Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning +
## 6 Conclusions
The lessons learned from this research can be useful for educational technology researchers or designers of learning analytics solutions. These stakeholders will be able to use the definition of local persistence proposed in this paper (or an adapted version if needed) when they design a learning analytics dashboard to provide information to students and teachers (e.g., profiles based on local persistence) and can also make some personalization decisions based on the conducted analyses (e.g., personalizing depending on the clusters of students obtained). In addition, results of the predictive analysis can provide insights to be used when designing early warning systems to alert students at-risk of dropout or failure. While these results are directly applicable to learning analytics designers, they can also indirectly benefit teachers and students, as learning analytics systems will be designed targeting students (e.g., showing information about students' behaviors and possible risks of dropout). This research has also some limitations that are worth mentioning. First, the results obtained might be tied to the specific context. In our case, the typology and maximum number of attempts for each specific exercise are unknown. This is a very important limitation because for example, it is easier to be persistent (at the local level) when exercises have a limited set of answers (e.g., multiple-choice questions) than in open-ended questions (e.g., numeric answer). Indeed, in our case, most exercises have a limited number of answers so this could explain higher values of local persistence. For this analysis, some filters are included to alleviate this issue, but the ideal case would be having information about this. In addition, the limitation on the number of possible answers entailed that persistence could not be calculated for many of the learners, which limited the analysis and the use of local persistence in predictive models (as imputation had to be used for missing values in several students). Furthermore, another limitation is related to the way local persistence is computed. As local persistence is a subjective characteristic of students, several ways to measure it could be presented, and each one could have its advantages and disadvantages. The measure in this paper can give an idea of how persistent the student is in the exercises s/he has done, but for example, it does not consider what s/he has not done, and the model does not take into account how the exercises are solved (e.g., a trial and error strategy is a bad strategy). While the last limitation is out of the scope of this article, it would also be interesting to analyze it in future models. As future work, in relation to the aforementioned limitations, it would be relevant to analyze local persistence in educational platforms where information about the context and methodology is known. Furthermore, it could be possible to add a corrective factor for non-attempted exercises and exercises where there is no reflection between attempts (e.g., elapsed time between attempts is very short). In addition, it would be relevant to use the model in courses where students may usually need more attempts and grades are not typically binary (0/1). Moreover, it would be interesting to delve into the profiles of local persistence. For example, it would be interesting to include other variables, such as the type of resources, in the clustering analysis to discover whether different students can be more persistent at the local level in different kinds of activities. In addition, the analysis of the evolution of local persistence of time could be extended by analyzing longer periods than a week and using sliding windows of time. Furthermore, a more detailed analysis could be done to explore the relationship between local persistence and other variables related to students' behaviors and performance, and the types of exercises/resources.