[ML-news] [CFP] RKDE@ECML-PKDD International Tutorial and Workshop on Responsible Knowledge Discovery in Education
Francesca Malloci
Please send to interested colleagues and students.
RKDE 2024 - Call for Papers
2nd International Tutorial and Workshop on Responsible Knowledge Discovery in Education
to be held as part of ECML-PKDD 2024.
Event: September 9-13th 2024 (TBD), Radisson Blu Hotel, Vilnius, Lithuania.
Website: http://rkde2024.isti.cnr.it
CONTEXT & OBJECTIVES
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 the 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.
IMPORTANT DATES
- Paper Submission Deadline: Jun 15, 2024
- Accept/Reject Notification: Jul 15 , 2024
- Camera-ready Deadline: 1Jul 31, 2024
- Event: TBD
TOPICS
We welcome contributions that address (but are not limited to) the following topics of interest:
- Dataset Collection and Preparation:
- New tools and systems for capturing educational data
- Modeling representations of learners from data
- Building representations of domain knowledge from data
- Integrating data from multiple (and diverse) data sources
- Creating datasets that allow to explore ethical dimensions
- Designing collection protocols tailored to responsible knowledge discovery
- Techniques and Models:
- Multimodal / semantic approaches for learners' behavior modeling or personalization
- Adaptive question-answering and dialogue or automatically generating test questions
- Personalized support tools and systems for communities of learners
- Natural language processing applied on exam data in order to assign a grade to them
- Temporal, behavioral, and physiological analysis of learners' behavior
- Student engagement assessment via machine-learning techniques
- Systems that detect and/or adapt the platform to emotional states of learners
- Techniques to provide automated proctoring support during online examinations
- Tools able to predict the learner's success or failure along the educational path
- Developing fair and explainable models for different kinds of stakeholders
- Developing privacy-protecting algorithms for learners' data processing
- Evaluation Protocol and Metric Formulation:
- Performing auditing studies with respect to bias and fairness
- Defining objective metrics for knowledge discovery in education
- Formulating bias-aware protocols to evaluate existing algorithms
- Evaluating existing mitigation strategies in unexplored domains
- Comparative studies of existing evaluation protocols and strategies
- Analyzing efficiency and scalability issues of debiasing methods
- Replicating previous studies with different samples, domains and/or contexts
- Case Study Exploration:
- Educational games
- Learning management systems
- Interactive simulations
- Intelligent tutoring
- Language assessment
- Francesca Maridina Malloci, University of Cagliari, Italy
- Mirko Marras, University of Cagliari, Italy
- Paola Mejia, EPFL, Switzerland
- Agathe Merceron, Berliner Hochschule für Technik, Germany
- Anna Monreale, University of Pisa, Italy
- Daniela Rotelli, University of Pisa, Italy
