Types of dropout in adaptive open online courses
| Published | May 2017 |
| Conference | 5th European MOOCs Stakeholders Summit, EMOOCs 2017, Madrid, Spain, May 22-26, 2017, Digital Education: Out to the World and Back to the Campus |
| Series | Lecture Notes in Computer Science Edition 1, Volume 10254, Pages 273-279 |
| Publisher | Springer International Publishing |
| Editors | Kloos, Carlos Delgado · Jermann, Patrick · Pérez-Sanagustín, Mar · Seaton, Daniel T. · White, Su |
| Country | Spain, Europe |
ABSTRACT
This study is devoted to different types of students' behavior before they drop an adaptive course. The Adaptive Python course at the Stepik educational platform was selected as the case for this study. Student behavior was measured by the following variables: number of attempts for the last lesson, last three lessons solving rate, the logarithm of normed solving time, the percentage of easy and difficult lessons, the number of passed lessons, and total solving time. We applied a standard clustering technique, K-means, to identify student behavior patterns. To determine optimal number of clusters, the silhouette metrics was used. As the result, three types of dropout were identified: "solved lessons'', "evaluated lessons as hard'', and "evaluated lessons as easy''.| Keywords | adaptive learning · clustering · dropout · MOOC |
| Published at | Madrid |
| ISBN | 978-3-319-59044-8 |
| Refereed | Yes |
| Rights | © Springer International Publishing AG 2017 |
| DOI | 10.1007/978-3-319-59044-8_32 |
| URL | https://link.springer.com/chapter/10.1007/978-3-319-59044-8_32 |
| Export options | BibTex · EndNote · Tagged XML · Google Scholar |
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