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Inaugural Editorial for the ICCK Transactions on Educational Data Mining
Editorial | Published: 10 October 2025
ICCK Transactions on Educational Data Mining Volume 1, Issue 1, 2025: 1-5
Volume 1, Issue 1, ICCK Transactions on Educational Data Mining
Volume 1, Issue 1, 2025
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ICCK Transactions on Educational Data Mining, Volume 1, Issue 1, 2025: 1-5

Open Access | Editorial | 10 October 2025
Inaugural Editorial for the ICCK Transactions on Educational Data Mining
by
Zongwen Fan Zongwen Fan 1 *
1 College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
* Corresponding Author: Zongwen Fan, [email protected]
DOI: 10.62762/TEDM.2025.646805
Received: 27 September 2025, Accepted: 29 September 2025, Published: 10 October 2025  
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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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CC BY Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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