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Predicting Student Dropout in a MOOC: An Evaluation of a Deep Neural Network Model
Norwegian University of Science and Technology (NTNU), Norway.
University of South-Eastern Norway, Norway.ORCID iD: 0000-0001-7520-695x
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0002-0199-2377
2019 (English)In: Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence, ACM Publications, 2019, p. 190-195Conference paper, Published paper (Refereed)
Abstract [en]

Massive Open Online Courses (MOOCs) have transformed the way educational institutions deliver high-quality educational material to the onsite and distance learners across the globe. As a result, a new paradigm shifts as to how learners acquire and benefit from the wealth of knowledge provided by a MOOC at their doorstep nowadays in contrast to the brick and mortar settings is visible. Learners are therefore showing a profound interest in the MOOCs offered by top universities and industry giants. They have also attracted a vast number of students from far-flung areas of the world. The massive number of registered students in MOOCs, however, pose one major challenge, i.e., 'the dropouts'. Course planners and content providers are struggling to retain the registered students, which give rise to a new research agenda focusing on predicting and explaining student dropout and low completion rates in a MOOC. Machine learning techniques utilizing deep learning approaches can efficiently predict the potential dropouts and can raise an alert well before time. In this paper, we have focused our study on the application of feed-forward deep neural network architectures to address this problem. Our model achieves not only high accuracy, but also low false negative rate while predicting dropouts on the MOOC data. Moreover, we also provide an in-depth comparison of the proposed architectures concerning precision, recall, and F1 measure.

Place, publisher, year, edition, pages
ACM Publications, 2019. p. 190-195
Keywords [en]
ANN, Dropout prediction, MOOC, deep learning, distance learning, e-Learning, online learning
National Category
Information Systems, Social aspects
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-87087DOI: 10.1145/3330482.3330514ISBN: 978-1-4503-6106-4 (print)OAI: oai:DiVA.org:lnu-87087DiVA, id: diva2:1340296
Conference
5th International Conference on Computing and Artificial Intelligence, April 19-22, 2019
Funder
Knowledge Foundation, 67110033Available from: 2019-08-04 Created: 2019-08-04 Last updated: 2019-09-04Bibliographically approved

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Dalipi, FisnikKastrati, Zenun

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Citation style
  • apa
  • harvard1
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  • de-DE
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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