Abstract
In education, several studies have tried to track student persistence (i.e., students’ ability to keep on working on the assigned tasks) using different 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 affected 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.
Highlights
Smart Learning Environments (SLEs) [1] combine educational technologies, ubiquitous learning, and learning analytics, among others
We have included an analysis of local persistence over time, and we have developed predictive models in different stages of the course to forecast global persistence and students’ performance using local persistence
In this article, we aim to contribute with the analysis of local persistence based on students’ interactions in a digital platform, and we focus on the attempts needed until the student correctly solves the exercise
Summary
Smart Learning Environments (SLEs) [1] combine educational technologies, ubiquitous learning, and learning analytics, among others. One of the objectives of SLEs is to provide more information (e.g., through dashboards [2]) about students’ behaviors and performance, such as their progress in the course With this information, it is possible to develop systems that can provide adaptative and personalized learning experiences (e.g., provide adaptable materials or scaffolding questions if needed, as in Intelligent Tutoring Systems [3]) and feedback/support [4,5]. It is possible to develop systems that can provide adaptative and personalized learning experiences (e.g., provide adaptable materials or scaffolding questions if needed, as in Intelligent Tutoring Systems [3]) and feedback/support [4,5] Another possible use of SLEs is to detect situations in which to intervene (e.g., to detect students with difficulties, such as risk of dropout, failure, lack of engagement or motivation, etc.) [6].
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