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Erschienen in:
Methodology for Teaching Electronic Instrumentation with Embedded Systems Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

MOOC Learner Behaviour: Attrition and Retention Analysis and Prediction Based on 11 Courses on the TELESCOPE Platform

verfasst von : Massimo Vitiello, Christian Gütl, Hector R. Amado-Salvatierra, Rocael Hernández

Erschienen in: Learning Technology for Education Challenges

Verlag: Springer International Publishing

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Abstract

Massive Open Online Courses (MOOCs) have become an important online learning tool for educators and learners, but one of the major issues are the high drop-out rates. Recent research suggests not only to identify and support learners at-risk to drop-out but also to differentiate between the group of healthy attrition (intentionally leaving the MOOC) and unhealthy attrition (struggling to complete the MOOC). In this paper, we focus on two research questions: Firstly, can we already identify learners at-risk to drop-out a MOOC in an early stage? Secondly, can we differentiate between the group of healthy attrition and unhealthy attrition? Experimentation with Support Vector Machines based on learners logs from eleven MOOCs on the Telescope platform show first promising results.

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Vorheriges Kapitel Applying PBL and SRL to Enhance Learning Strategies and Collaboration in a Blended Learning Environment
Nächstes Kapitel Blended Learning as a Tool for Work-Life Oriented Master Courses
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Metadaten
Titel
MOOC Learner Behaviour: Attrition and Retention Analysis and Prediction Based on 11 Courses on the TELESCOPE Platform
verfasst von
Massimo Vitiello
Christian Gütl
Hector R. Amado-Salvatierra
Rocael Hernández
Copyright-Jahr
2017
Buch
Learning Technology for Education Challenges

Print ISBN: 978-3-319-62742-7

Electronic ISBN: 978-3-319-62743-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-62743-4_9

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