Malmö University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0001-9376-9844
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-8512-2976
2022 (English)In: Information, E-ISSN 2078-2489, Vol. 13, no 4, article id 203Article, review/survey (Refereed) Published
Abstract [en]

The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature's themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

Place, publisher, year, edition, pages
MDPI , 2022. Vol. 13, no 4, article id 203
Keywords [en]
AI, ML, DL, digital education, literature review, dropouts, intelligent tutors, performance prediction
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-51752DOI: 10.3390/info13040203ISI: 000786209900001Scopus ID: 2-s2.0-85129306474OAI: oai:DiVA.org:mau-51752DiVA, id: diva2:1661970
Available from: 2022-05-30 Created: 2022-05-30 Last updated: 2024-02-05Bibliographically approved

Open Access in DiVA

fulltext(609 kB)6034 downloads
File information
File name FULLTEXT01.pdfFile size 609 kBChecksum SHA-512
64f597885fb852b5330d7d1dbcca50605f079059f7b38ea93d9a064373000440a6922cbc28b115633aca37308adce3d489ed65504b1e83361736a93ba4e66fc4
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Munir, HussanVogel, BahtijarJacobsson, Andreas

Search in DiVA

By author/editor
Munir, HussanVogel, BahtijarJacobsson, Andreas
By organisation
Department of Computer Science and Media Technology (DVMT)Internet of Things and People (IOTAP)
In the same journal
Information
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 6040 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 351 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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