lnu.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
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
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM).ORCID-id: 0000-0002-0199-2377
2019 (Engelska)Ingår i: Proceedings of the 2019 5th International Conference on Computing and Artificial Intelligence, ACM Publications, 2019, s. 190-195Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
ACM Publications, 2019. s. 190-195
Nyckelord [en]
ANN, Dropout prediction, MOOC, deep learning, distance learning, e-Learning, online learning
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning
Forskningsämne
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
URN: urn:nbn:se:lnu:diva-87087DOI: 10.1145/3330482.3330514ISBN: 978-1-4503-6106-4 (tryckt)OAI: oai:DiVA.org:lnu-87087DiVA, id: diva2:1340296
Konferens
5th International Conference on Computing and Artificial Intelligence, April 19-22, 2019
Forskningsfinansiär
KK-stiftelsen, 67110033Tillgänglig från: 2019-08-04 Skapad: 2019-08-04 Senast uppdaterad: 2019-09-04Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltext

Personposter BETA

Dalipi, FisnikKastrati, Zenun

Sök vidare i DiVA

Av författaren/redaktören
Dalipi, FisnikKastrati, Zenun
Av organisationen
Institutionen för datavetenskap och medieteknik (DM)
Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 44 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf