Analyzing Students' Persistence using an Event-Based Model
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2019-06-27
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CEUR-WS.org
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Abstract
In education, persistence can be defined as the students' ability to keep on working on the assigned tasks (e.g., exercises) despite the difficulties. From previous studies, persistence might be an important factor in students' performance. However, these studies were limited because they only relied on students' self-reported data to measure persistence. This article aims to contribute with a novel model to measure persistence from students' logs, which is general enough to be applied to different educational platforms. In this work, persistence is measured taking students' interactions with automatic correction exercises. Simple metrics such as the average of students' attempts are not valid for a precise calculation of persistence since some exercises should count more for persistence as they have been done incorrectly many times but with some limit so that a single exercise cannot bias the indicator; or when a student answers correctly we should not add new attempts. In this paper, we propose a model to measure persistence on exercises which is valid to many digital online educational platforms. The analysis of students' persistence shows that there are not statistically significant differences of persistence between students who drop out the course or not, although persistence is shown to have a positive relationship with average grades in most of the cases. In contrast, persistence is not related to engagement with videos. These results provide an initial exploration about students' persistence, which can be important to understand how students behave and to properly adapt the course to students' needs.
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Proceeding of: LASI-SPAIN 2019 proceedings: Learning Analytics Summer Institute Spain 2019: Learning Analytics in Higher Education, Vigo, Spain, June 27-28, 2019.
Keywords
Persistence, Learning analytics, Students' behaviors, Behaviors, Behaviours
Bibliographic citation
LASI-SPAIN 2019 proceedings: Learning Analytics Summer Institute Spain 2019: Learning Analytics in Higher Education, Vigo, Spain, June 27-28, 2019, pp. 56-70. ISBN 978-84-16829-40-8. CEUR-WS.org.