Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/13/4/203 |
| _version_ | 1797410685169172480 |
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| author | Hussan Munir Bahtijar Vogel Andreas Jacobsson |
| author_facet | Hussan Munir Bahtijar Vogel Andreas Jacobsson |
| author_sort | Hussan Munir |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:33:53Z |
| format | Article |
| id | doaj.art-533c067ff5b141b3943fc22073f4e3c1 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T04:33:53Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-533c067ff5b141b3943fc22073f4e3c12023-12-03T13:31:23ZengMDPI AGInformation2078-24892022-04-0113420310.3390/info13040203Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic RevisionHussan Munir0Bahtijar Vogel1Andreas Jacobsson2Department of Computer Science and Media Technology, Malmö University, 20506 Malmö, SwedenDepartment of Computer Science and Media Technology, Malmö University, 20506 Malmö, SwedenDepartment of Computer Science and Media Technology, Malmö University, 20506 Malmö, SwedenThe 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.https://www.mdpi.com/2078-2489/13/4/203AIMLDLdigital educationliterature reviewdropouts |
| spellingShingle | Hussan Munir Bahtijar Vogel Andreas Jacobsson Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision Information AI ML DL digital education literature review dropouts |
| title | Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision |
| title_full | Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision |
| title_fullStr | Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision |
| title_full_unstemmed | Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision |
| title_short | Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision |
| title_sort | artificial intelligence and machine learning approaches in digital education a systematic revision |
| topic | AI ML DL digital education literature review dropouts |
| url | https://www.mdpi.com/2078-2489/13/4/203 |
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