Editorial
Open Access
Inaugural Editorial for the ICCK Transactions on Educational Data Mining
1
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
* Corresponding Author:
Zongwen Fan,
[email protected]
Volume 1, Issue 1
2025
Received: 27 September 2025
Accepted: 29 September 2025
Published: 10 October 2025
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CC BY 4.0 License
Article Information
Published in
ICCK Transactions on Educational Data Mining
Volume/Issue
Volume 1, Issue 1, 2025
Pages
1-5
Abstract
This editorial presents the motivations underlying the establishment of the ICCK Transactions on Educational Data Mining (TEDM), an international, peer-reviewed journal dedicated to advancing theoretical, methodological, and applied research in Educational Data Mining (EDM). The journal is conceived as a platform to bring together researchers, educators, and practitioners from diverse disciplines, fostering cross-disciplinary dialogue and innovation in data-driven educational research. In particular, this editorial introduces the journal's objectives and scope, outlines representative techniques and methodological approaches employed in EDM, and highlights key trends, challenges, and opportunities that define the current landscape of the field.
Keywords
educational data mining
personalized learning
early warning systems
learning analytics
artificial intelligence in education
teaching optimization
student performance prediction
intelligent tutoring systems
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The author declares no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Fan, Z. (2025). Inaugural Editorial for the ICCK Transactions on Educational Data Mining. ICCK Transactions on Educational Data Mining, 1(1), 1–5. https://doi.org/10.62762/TEDM.2025.646805
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TY - JOUR AU - Fan, Zongwen PY - 2025 DA - 2025/10/10 TI - Inaugural Editorial for the ICCK Transactions on Educational Data Mining JO - ICCK Transactions on Educational Data Mining T2 - ICCK Transactions on Educational Data Mining JF - ICCK Transactions on Educational Data Mining VL - 1 IS - 1 SP - 1 EP - 5 DO - 10.62762/TEDM.2025.646805 UR - https://www.icck.org/article/abs/TEDM.2025.646805 KW - educational data mining KW - personalized learning KW - early warning systems KW - learning analytics KW - artificial intelligence in education KW - teaching optimization KW - student performance prediction KW - intelligent tutoring systems AB - This editorial presents the motivations underlying the establishment of the ICCK Transactions on Educational Data Mining (TEDM), an international, peer-reviewed journal dedicated to advancing theoretical, methodological, and applied research in Educational Data Mining (EDM). The journal is conceived as a platform to bring together researchers, educators, and practitioners from diverse disciplines, fostering cross-disciplinary dialogue and innovation in data-driven educational research. In particular, this editorial introduces the journal's objectives and scope, outlines representative techniques and methodological approaches employed in EDM, and highlights key trends, challenges, and opportunities that define the current landscape of the field. SN - 3070-5843 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Fan2025Inaugural,
author = {Zongwen Fan},
title = {Inaugural Editorial for the ICCK Transactions on Educational Data Mining},
journal = {ICCK Transactions on Educational Data Mining},
year = {2025},
volume = {1},
number = {1},
pages = {1-5},
doi = {10.62762/TEDM.2025.646805},
url = {https://www.icck.org/article/abs/TEDM.2025.646805},
abstract = {This editorial presents the motivations underlying the establishment of the ICCK Transactions on Educational Data Mining (TEDM), an international, peer-reviewed journal dedicated to advancing theoretical, methodological, and applied research in Educational Data Mining (EDM). The journal is conceived as a platform to bring together researchers, educators, and practitioners from diverse disciplines, fostering cross-disciplinary dialogue and innovation in data-driven educational research. In particular, this editorial introduces the journal's objectives and scope, outlines representative techniques and methodological approaches employed in EDM, and highlights key trends, challenges, and opportunities that define the current landscape of the field.},
keywords = {educational data mining, personalized learning, early warning systems, learning analytics, artificial intelligence in education, teaching optimization, student performance prediction, intelligent tutoring systems},
issn = {3070-5843},
publisher = {Institute of Central Computation and Knowledge}
}
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