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MOOC Dropout Prediction Using Machine Learning Techniques: Review and Research Challenges
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Univ Coll Southeast Norway, Norway.ORCID iD: 0000-0001-7520-695x
Norwegian Univ Sci & Technol NTNU, Norway.
Norwegian Univ Sci & Technol NTNU, Norway.
2018 (English)In: Proceedings of 2018 IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education, IEEE, 2018, p. 1007-1014Conference paper, Published paper (Refereed)
Abstract [en]

MOOC represents an ultimate way to deliver educational content in higher education settings by providing high-quality educational material to the students throughout the world. Considering the differences between traditional learning paradigm and MOOCs, a new research agenda focusing on predicting and explaining dropout of students and low completion rates in MOOCs has emerged. However, due to different problem specifications and evaluation metrics, performing a comparative analysis of state-of-the-art machine learning architectures is a challenging task. In this paper, we provide an overview of the MOOC student dropout prediction phenomenon where machine learning techniques have been utilized. Furthermore, we highlight some solutions being used to tackle with dropout problem, provide an analysis about the challenges of prediction models, and propose some valuable insights and recommendations that might lead to developing useful and effective machine learning solutions to solve the MOOC dropout problem.

Place, publisher, year, edition, pages
IEEE, 2018. p. 1007-1014
Series
IEEE Global Engineering Education Conference, ISSN 2165-9567
Keywords [en]
MOOC, review, dropout prediction, machine learning, artificial intelligence
National Category
Software Engineering
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-76904DOI: 10.1109/EDUCON.2018.8363340ISI: 000434866100141Scopus ID: 2-s2.0-85048098466ISBN: 978-1-5386-2957-4 (print)OAI: oai:DiVA.org:lnu-76904DiVA, id: diva2:1233262
Conference
IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education, APR 17-20, 2018, Santa Cruz de Tenerife, SPAIN
Available from: 2018-07-17 Created: 2018-07-17 Last updated: 2019-05-27Bibliographically approved

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Dalipi, Fisnik

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf