SCOPE

Data mining and big data analytics have significant potential for improving learning outcomes and supporting decision making in educational systems. By analyzing large amounts of data, researchers and educators can gain insights into student learning conditions and behavior, as well as identify patterns and trends that can inform instructional strategies and improve educational outcomes. Data can be leveraged by researchers to validate education and research findings at a larger scale, leading to a better understanding of student learning conditions and improved teaching support. Educators can monitor student progress and enhance the teaching process, while students can benefit from more effective course selection and educational management. Additionally, with the aid of large amounts of data, predictions regarding student dropout rates, motivations, and diversity can be significantly enhanced. It also becomes possible to gain a more comprehensive understanding of particular student groups, ultimately resulting in improved adaptivity and personalization for individual students. However, it is important to recognize that data mining poses risks to user privacy and security. As educational institutions collect and store large amounts of student data, it is important to ensure that this data is secure and protected from unauthorized access or misuse.

ACCEPTED PAPERS

Intelligent Practical Teaching Platform based on Data Mining
Qi Liu, Xiao Chen, Kun Niu, Wanru Zhang, Yiman Gao, and Jin Wei

A Deep Memory-Aware Attentive Model for Knowledge Tracing
Juntai Shi, Wei Su, Lei Liu, Shenglin Xu, Tianyuan Huang, Jiamin Liu, Wenli Yue, and Shihua Li

Optimization and Improvement of Fake News Detection using Voting Technique for Societal Benefit
Sribala Chinta, Karen Farnandes, Ningxi Cheng, Jordan Fernandez, Shamim Yazdani, Zhipeng Yin, Zichong Wang, Xuyu Wang, Weifeng Xu, Jun Liu, CHONG Siang Yew, Puqing Jiang, and Wenbin Zhang

Exploring Approaches for Teaching Cybersecurity and AI for K-12
Yu Cai and Drew Youngstrom

CALL FOR PAPERS

The purpose of this workshop is to unite researchers from various fields such as data mining, big data, machine learning, security, privacy, and cognitive science. Our objective is to foster a discussion and exchange of ideas that focuses on innovative and pragmatic research and educational approaches, methods, and obstacles related to data mining for education. We welcome submissions of papers covering a wide range of topics of interest, including but not limited to:

  • Data mining and big data analytics for personalized learning and adaptive teaching
  • Predictive analytics for identifying at-risk students and enhancing student success
  • Machine learning techniques for educational data analysis
  • Comparative analysis of different data mining algorithms in educational settings
  • Data visualization for educational data mining
  • Educational data mining for curriculum design and development
  • Data mining for measuring and improving student engagement
  • Educational data mining for teacher professional development and support
  • Security and privacy issues related to educational data mining
  • Ethical and legal considerations in educational data mining
  • Educational data mining for decision-making and policy development in education
  • Impact of educational data mining on equity and inclusivity in education.
  • Novel approaches and challenges in educational data mining

IMPORTANT DATES

  • Paper Submission: September 15th, 2023
  • Author Notification: September 26th, 2023
  • Camera-Ready: October 1st, 2023
  • Registration: October 15th, 2023
  • Conference Date: December 1st -- 4th, 2023

SUBMISSION

Paper submissions should be limited to a maximum of 8 pages (excluding references), and follow the IEEE ICDM format. More detailed information is available in the IEEE ICDM 2023 Submission Guidelines (https://www.cloud-conf.net/icdm2023/call-for-papers.html). Submitted manuscripts must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.



Paper submission link: Workshop on Data Mining for Education (DME)

ORGANISERS

Workshop Chairs:




Yu Cai
Michigan Technological University, Michigan, USA


Wenbin Zhang
Florida International University, Florida, USA

Web Chairs:

Zichong Wang
Florida International University, Florida, USA

Tongjia Yu
                Columbia University, New York, USA                  

Publicity Chairs:

Shichao Pei
          University of Notre Dame, Indiana, USA    

Zhen Liu
Guangdong University of Foreign Studies,
Guangdong, China

Publication Chair:

Zhipeng Yin
Florida International University, Florida, USA

Technical Committee Members:

  • Heitor Murilo Gomes, Victoria University of Wellington
  • Israat Haque, Dalhousie University
  • Jun Liu, Carnegie Mellon University
  • Liang Luo, University of Electronic Science and Technology
  • Yang Liu, Meta Platforms
  • Travers Barclay Child, China Europe International Business School
  • Ziyi Kou, University of Notre Dame
  • Kehan Guo, University of Notre Dame
  • Qiannan Zhang, King Abdullah University of Science and Technology
  • Yu Pang, Chongqing University of Posts and Telecommunications
  • Zhipeng Huang, Case Western Reserve University
  • Enza Messina, University of Milano Bicocca
  • Mingli Zhang, Mcgill University
  • Qingzhao Kong, Jimei University
  • Ruijun Chen, The University of Hong Kong
  • Nripsuta Saxena, University of Southern California
  • CHONG SIANG YEW, Southern University of Science and Technology
  • Bitao Peng, Guangdong University of Foreign Studies

PUBLICATION

All accepted workshop papers will be included in the Proceedings of ICDM Workshop (ICDMW) 2023, published by the IEEE Computer Society Press and each assigned a Digital Object Identifier (DOI).


All accepted papers must have at least one “FULL” registration. A full registration is either a “member” or “non-member” registration. Student registrations are not considered full registrations. All authors are required to register by November, 2023.


For registration queries please contact: registration@computer.org

Last Update: 04/2023