posted by system || 2889 views

DME 2023 : International Workshop on Data Mining for Education: Techniques, Challenges, and Applications


When Dec 1, 2023 - Dec 4, 2023
Where Shanghai, China
Submission Deadline Sep 1, 2023

Call For Papers

DME 2023 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of data mining for education.

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.

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

Related Resources

IEEE-Ei/Scopus-SGGEA 2024   2024 Asia Conference on Smart Grid, Green Energy and Applications (SGGEA 2024) -EI Compendex
Ei/Scopus-AACIP 2024   2024 2nd Asia Conference on Algorithms, Computing and Image Processing (AACIP 2024)-EI Compendex
AMLDS 2025   2025 International Conference on Advanced Machine Learning and Data Science
SPIE-Ei/Scopus-ITNLP 2024   2024 4th International Conference on Information Technology and Natural Language Processing (ITNLP 2024) -EI Compendex
ICONDATA 2024   6th International Conference on Data Science and Applications
AASDS 2024   Special Issue on Applications and Analysis of Statistics and Data Science
BDCAT 2024   IEEE/ACM Int’l Conf. on Big Data Computing, Applications, and Technologies
DMBDA 2024   2024 7th International Conference on Data Mining and Big Data Analytics(DMBDA 2024)
DSIT 2024   7th International Conference on Data Science and Information Technology
ACDSA 2025   2nd International Conference on Artificial Intelligence, Computer, Data Sciences and Applications