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