Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive Analysis
Educational data mining (EDM) enables improving educational systems by using data mining techniques on educational data to analyze students’ learning processes to extract valuable information that helps optimize teaching strategies and improve student achievement. EDM has been an importan...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10295479/ |
| _version_ | 1797640382403575808 |
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| author | Ozcan Ozyurt Hacer Ozyurt Deepti Mishra |
| author_facet | Ozcan Ozyurt Hacer Ozyurt Deepti Mishra |
| author_sort | Ozcan Ozyurt |
| collection | DOAJ |
| description | Educational data mining (EDM) enables improving educational systems by using data mining techniques on educational data to analyze students’ learning processes to extract valuable information that helps optimize teaching strategies and improve student achievement. EDM has been an important area of research and application in recent years. The aim of this study is to describe the current situation of the EDM field and reveal its future perspective. The study employs descriptive analysis and topic modeling, utilizing a corpus of 2792 studies indexed in the Scopus database since 2007. Firstly, the study determines the document types, distribution by years, prominent authors, countries, subject areas, and journals of the studies in the field of EDM. Then, using topic modeling analysis, which is an unsupervised machine learning technique, the study determines hidden patterns, research interests, and trends within the field. This study is innovative and the first as it reveals latent research interests and trends in the field of EDM through machine learning-based topic modeling-based analysis. The descriptive characteristics of the study emphasize the continuous development of the field and its multidisciplinary aspect. The outputs of the topic modeling analysis reveal that the studies can be grouped into twelve topics. The most frequently studied topic is “Learning pattern and behavior”, and the topic whose frequency of study increases the most over time is “Dropout risk prediction”. When comparing the frequency of study of the topics over time to other topics, the first topic that stands out is “Performance prediction”. The results of this study can be expected to make significant contributions to the field in terms of revealing the big picture of the current literature in the field of EDM and providing a future perspective. Therefore, the results of the study are expected to give direction to the field and provide important insights or guidance to decision makers and education policy makers. |
| first_indexed | 2024-03-11T13:31:02Z |
| format | Article |
| id | doaj.art-ff9c1f7a3ccb4b0eb8906272d18253e8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T13:31:02Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ff9c1f7a3ccb4b0eb8906272d18253e82023-11-02T23:01:06ZengIEEEIEEE Access2169-35362023-01-011112019212020810.1109/ACCESS.2023.332762410295479Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive AnalysisOzcan Ozyurt0https://orcid.org/0000-0002-0047-6813Hacer Ozyurt1https://orcid.org/0000-0001-8621-2335Deepti Mishra2https://orcid.org/0000-0001-5144-3811Department of Software Engineering, Faculty of Technology, Karadeniz Technical University, Trabzon, TurkeyDepartment of Software Engineering, Faculty of Technology, Karadeniz Technical University, Trabzon, TurkeyDepartment of Computer Science (IDI), Educational Technology Laboratory, Norwegian University of Science and Technology, Gjøvik, NorwayEducational data mining (EDM) enables improving educational systems by using data mining techniques on educational data to analyze students’ learning processes to extract valuable information that helps optimize teaching strategies and improve student achievement. EDM has been an important area of research and application in recent years. The aim of this study is to describe the current situation of the EDM field and reveal its future perspective. The study employs descriptive analysis and topic modeling, utilizing a corpus of 2792 studies indexed in the Scopus database since 2007. Firstly, the study determines the document types, distribution by years, prominent authors, countries, subject areas, and journals of the studies in the field of EDM. Then, using topic modeling analysis, which is an unsupervised machine learning technique, the study determines hidden patterns, research interests, and trends within the field. This study is innovative and the first as it reveals latent research interests and trends in the field of EDM through machine learning-based topic modeling-based analysis. The descriptive characteristics of the study emphasize the continuous development of the field and its multidisciplinary aspect. The outputs of the topic modeling analysis reveal that the studies can be grouped into twelve topics. The most frequently studied topic is “Learning pattern and behavior”, and the topic whose frequency of study increases the most over time is “Dropout risk prediction”. When comparing the frequency of study of the topics over time to other topics, the first topic that stands out is “Performance prediction”. The results of this study can be expected to make significant contributions to the field in terms of revealing the big picture of the current literature in the field of EDM and providing a future perspective. Therefore, the results of the study are expected to give direction to the field and provide important insights or guidance to decision makers and education policy makers.https://ieeexplore.ieee.org/document/10295479/Educational data miningtopic modelingresearch trendsmachine learning |
| spellingShingle | Ozcan Ozyurt Hacer Ozyurt Deepti Mishra Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive Analysis IEEE Access Educational data mining topic modeling research trends machine learning |
| title | Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive Analysis |
| title_full | Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive Analysis |
| title_fullStr | Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive Analysis |
| title_full_unstemmed | Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive Analysis |
| title_short | Uncovering the Educational Data Mining Landscape and Future Perspective: A Comprehensive Analysis |
| title_sort | uncovering the educational data mining landscape and future perspective a comprehensive analysis |
| topic | Educational data mining topic modeling research trends machine learning |
| url | https://ieeexplore.ieee.org/document/10295479/ |
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