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Types of dropout in adaptive open online courses
Skryabin, Maxim

PublishedMay 2017
Conference5th European MOOCs Stakeholders Summit, EMOOCs 2017, Madrid, Spain, May 22-26, 2017, Digital Education: Out to the World and Back to the Campus
SeriesLecture Notes in Computer Science
Edition 1, Volume 10254, Pages 273-279
PublisherSpringer International Publishing
EditorsKloos, Carlos Delgado · Jermann, Patrick · Pérez-Sanagustín, Mar · Seaton, Daniel T. · White, Su
CountrySpain, 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 atMadrid
ISBN978-3-319-59044-8
RefereedYes
Rights© Springer International Publishing AG 2017
DOI10.1007/978-3-319-59044-8_32
URLhttps://link.springer.com/chapter/10.1007/978-3-319-59044-8_32
Export optionsBibTex · EndNote · Tagged XML · Google Scholar


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