RKDE 2024

By offering a large number of highly diverse resources, learning platforms have been attracting lots of participants, and the interactions with these systems have generated a vast amount of learning-related data. Their collection, processing and analysis have promoted a significant growth of machine learning and knowledge discovery approaches and have opened up new opportunities for supporting and assessing educational experiences in a data-driven fashion. Being able to understand students' behavior and devise models able to provide data-driven decisions pertaining to the learning domain is a primary property of learning platforms, aiming at maximizing learning outcomes.

However, the use of knowledge discovery in education also raises a range of ethical challenges including transparency, reliability fairness, and inclusiveness. The purpose of RKDE, Responsible Knowledge Discovery in Education, is to encourage principled research that will lead to the advancement of explainable, transparent, ethical and fair data mining and machine learning in the context of educational data. RKDE is an event organized into two moments: a tutorial to introduce audience to the topic, and a workshop to discuss recent advances in the research field. The tutorial will provide a broad overview of the state of the art on the major applications for responsible approaches and their relationship with the educational context. Moreover, it will present hands-on case studies that practically shows how knowledge discovery tasks can be responsibly addressed in education. The workshop will seek top-quality submissions addressing uncovered important issues related to ethical, fair, explainable and transparent data mining and machine learning in education. Papers should present research results in any of the topics of interest for the workshop as well as application experiences, tools and promising preliminary ideas. RKDE asks for contributions from researchers, academia and industries, working on topics addressing these challenges primarily from a technical point of view, but also from a legal, ethical or sociological perspective.

All contributions will be reviewed by at least three members of the Program Committee. All papers should be anonymized (double-blind review process). We strongly encourage making code and data available anonymously (e.g., in an anonymous GitHub repository via Anonymous GitHub. Moreover they should be written in English and be in LNCS format. Author instructions, style files, and the copyright form can be downloaded here:
https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines

The following kinds of submissions will be considered:

It is planned that accepted papers will be published after the workshop by Springer in a volume of Lecture Notes in Computer Science (LNCS). Condition for inclusion in the post-proceedings is that at least one of the co-authors has presented the paper at the workshop. Pre-proceedings will be available online before the workshop. We also allow accepted papers to be presented without publication in the conference proceedings, if the authors choose to do so. Some of the full paper submissions may be accepted as short papers after review by the Program Committee.

The submission link is:
https://cmt3.research.microsoft.com/ECMLPKDDWorkshops2024/Submission/Index (choosing the right track corresponding to your workshop name)

The event will take place at the ECML-PKDD 2024 Conference at Radisson Blu Hotel, Vilnius, Lithuania

The registration to the workshop is managed by the ECML-PLDD main conference at
https://ecmlpkdd.org/2024/attending-registration/

All inquiries should be sent to: