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MOOC Dropout Prediction Using Machine Learning Techniques: Review and Research Challenges
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM), Institutionen för datavetenskap (DV). Univ Coll Southeast Norway, Norway.ORCID-id: 0000-0001-7520-695x
Norwegian Univ Sci & Technol NTNU, Norway.
Norwegian Univ Sci & Technol NTNU, Norway.ORCID-id: 0000-0002-0199-2377
2018 (Engelska)Ingår i: Proceedings of 2018 IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education, IEEE, 2018, s. 1007-1014Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE, 2018. s. 1007-1014
Serie
IEEE Global Engineering Education Conference, ISSN 2165-9567
Nyckelord [en]
MOOC, review, dropout prediction, machine learning, artificial intelligence
Nationell ämneskategori
Programvaruteknik
Forskningsämne
Data- och informationsvetenskap, Datavetenskap
Identifikatorer
URN: urn:nbn:se:lnu:diva-76904DOI: 10.1109/EDUCON.2018.8363340ISI: 000434866100141Scopus ID: 2-s2.0-85048098466ISBN: 978-1-5386-2957-4 (tryckt)OAI: oai:DiVA.org:lnu-76904DiVA, id: diva2:1233262
Konferens
IEEE Global Engineering Education Conference (EDUCON) - Emerging Trends and Challenges of Engineering Education, APR 17-20, 2018, Santa Cruz de Tenerife, SPAIN
Tillgänglig från: 2018-07-17 Skapad: 2018-07-17 Senast uppdaterad: 2020-05-12Bibliografiskt granskad

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