Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

In education, several studies have tried to track student persistence (i.e., students' ability to keep on working on the assigned tasks) using di fferent definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to analyze student behaviors based on logs and using learning analytics. These analyses can be used to provide personalized and adaptative feedback in Smart Learning Environments. In this line, this work proposes the analysis and measurement of two types of persistence based on students' interactions in online courses: (1) local persistence (based on the attempts used to solve an exercise when the student answers it incorrectly), and (2) global persistence (based on overall course activity/completion). Results show that there are different students' profiles based on local persistence, although medium local persistence stands out. Moreover, local persistence is highly a ffected by course context and it can vary throughout the course. Furthermore, local persistence does not necessarily relate to global persistence or engagement with videos, although it is related to students' average grade. Finally, predictive analysis shows that local persistence is not a strong predictor of global persistence and performance, although it can add some value to the predictive models.

Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning Articles