Held in conjunction with International Conference on Computers in Education (ICCE 2025)
Chennai, India
1 or 2 December, 2025
Workshop Date: 1 or 2 December, 2025
Important deadlines
Submission deadline for workshop papers: Extended to 28th August, 2025
Acceptance notification of workshop papers: 10th September,2025
Final camera-ready version due for workshop papers: 15th September, 2025
(Submission link: https://easychair.org/cfp/RELD@ICCE2025)
The rapid growth of digital learning environments has led to an unprecedented volume of learner data, enabling powerful applications of Learning Analytics (LA) and Educational Data Mining (EDM). These technologies offer the potential to personalize learning, support educators, and inform institutional decision-making. However, they also raise critical ethical challenges related to privacy, data ownership, algorithmic bias, and the commodification of educational data.
This workshop aims to bring together researchers, practitioners, and policymakers to explore the emerging ethical dimensions of LA & EDM. Topics will include ethical data governance, transparency and explainability of algorithms, balancing pedagogical benefits with commercial interests, and the development of equitable and inclusive analytics systems. We will also discuss the need for new research ethics frameworks and institutional policies that can guide responsible innovation in this field.
This is the first workshop organized by the APSCE SIG on Learning Analytics and Educational Data Mining. It aims to foster a community of inquiry around the responsible use of learning data and to promote interdisciplinary dialogue across technical, pedagogical, and ethical domains.
This workshop aligns with ICCE’s broader goals of promoting ethical, inclusive, and data-informed approaches to educational technology and offers a timely space to advance discourse and practices in this rapidly evolving area.
The APSCE SIG on Learning Analytics and Educational Data Mining invites submissions for the 1st Workshop on Emerging Ethical Challenges in LA & EDM at ICCE 2025. As learning analytics and educational data mining become increasingly embedded in digital education systems, it is vital to address the ethical, legal, and social implications of these technologies.
We welcome contributions that address, but are not limited to, the following topics:
Ethical frameworks for learning analytics and educational data mining
Privacy-preserving analytics and data protection strategies
Algorithmic transparency, fairness, and accountability
Bias detection and mitigation in educational data systems
Governance models for institutional data use
Pedagogical implications of ethical data practices
Stakeholder perspectives (students, teachers, administrators, parents)
Case studies of ethical dilemmas in LA & EDM implementation
Policy and regulatory considerations for educational data use
Interoperability and data sharing challenges across platforms
We encourage both theoretical and empirical contributions, including conceptual papers, case studies, and technical demonstrations.
All workshop participants must register for the main ICCE conference.
There will be no additional workshop fee.
Both in-person and online participation will be supported, following ICCE policies.
Types of Submissions
A paper submitted to this workshop must fit within at least one of the following categories:
• Theoretical Paper – Conceptual frameworks or ethical paradigms in LA & EDM
• Empirical Paper – Studies evaluating ethical dimensions of data practices
• Technical Demonstration – Tools or systems showcasing ethical safeguards
• Policy Paper – Governance or institutional policy analysis
• Case Study – Descriptions of real-world ethical dilemmas and responses
Each accepted paper will be presented in a thematically grouped session, followed by a discussant-led cross-paper dialogue. Authors are encouraged to include short video clips or interactive demos to illustrate systems or dilemmas related to ethical LA & EDM.
This workshop looks for Full and Short papers that follow the author guidelines of ICCE2025.
Full paper (8-10 pages)
Short paper (5-6 pages)
Please download the latest version of the Paper Template file in preparing your manuscript.
This workshop practices a double-blind peer-review process.
Please submit your paper via EasyChair
https://easychair.org/cfp/RELD@ICCE 2025
All accepted papers will be published in proceedings, which will be submitted to Elsevier for inclusion in Scopus. All papers should follow the paper format of the main conference.
Important Dates
Submission deadline for workshop papers: 18th August 2025
Acceptance notification of workshop papers: 10th September, 2025
Final camera-ready version due for workshop papers: 15th September, 2025
Author registration: 15th September, 2025
Workshop date: 1st or 2nd December, 2025
Contact
Ashwin T S <ashwin.tudur@vanderbilt.edu>
Ashwin T S (Vanderbilt University, USA): Research Scientist at the Institute for Software Integrated Systems. He works on ethics, privacy, and bias in AI applications for K–12 educational vision data. He Chairs the APSCE SIG on Learning Analytics and Educational Data Mining (LA&EDM).
Shitanshu Mishra (UNESCO MGIEP, India): Chief of Digital Learning, AI, and IT at UNESCO MGIEP. His research focuses on AI ethics in education, student question posing, and technology-enhanced learning. He co-chairs the APSCE SIG-LA&EDM.
Sarah Burriss (Vanderbilt University, USA): Sarah is a Research Scientist at Vanderbilt University’s LIVE Learning Innovation Incubator and the EngageAI Institute. She investigates student and teacher perspectives on the use of multimodal data and machine learning in classroom settings, and studies broader ethical concerns in educational AI systems. She is particularly interested in designing for responsible and explainable student and teacher interactions with analytics and AI-enhanced learning systems.
Kshitij Sharma (Norwegian University of Science and Technology, Norway): Associate Professor in Learning Analytics. His work focuses on ethical and inclusive learning analytics, data literacy, and responsible AI in education.
Patrick Ocheja (Kyoto University, Japan): Patrick is a JSPS Research Fellow at Academic Center for Computing and Media Studies, Kyoto University. His work focuses on learning analytics, blockchain in education, and trustworthy AI. He explores how decentralized technologies and data governance frameworks can support ethical and learner-centered approaches to educational data. He co-chairs the APSCE SIG-LA&EDM.
Namrata Srivastava (Vanderbilt University, USA): Namrata is a Research Scientist at the Institute for Software Integrated Systems, Vanderbilt University. Her research focuses on learning analytics, modeling learner behaviors, and equitable AI in education. She works on developing interpretable machine learning models to identify learning patterns in multimodal datasets and supports the ethical design of educational technologies for diverse learner populations.
Ramkumar Rajendran (Indian Institute of Technology Bombay, India): Ramkumar is an Associate Professor at the Centre for Educational Technology, IIT Bombay. His research focuses on the design of intelligent learning environments, multimodal learning analytics, and affective computing in education. He investigates how emotion-aware and ethically-grounded AI systems can enhance learner engagement and support inclusive digital learning experiences across varied educational contexts.
Gautam Biswas (Vanderbilt University, USA): Gautam is a Cornelius Vanderbilt Professor of Engineering, and Professor of Computer Science and Computer Engineering in the CS Department. He also is a Senior Research Scientist at the Institute for Software Integrated Systems. His interdisciplinary research spans intelligent learning environments, model-based reasoning, and student modeling. He has led major projects on teachable agents, multimodal analytics, and learning by teaching. His recent work addresses transparency, fairness, and ethical design in AI-driven educational technologies.
Program Committee Members (alphabetically by first name)
Alyssa Wise (New York University, USA)
Bodong Chen (University of Pennsylvania, USA)
Dragan Gašević (Monash University, Australia)
Fu-Yun Yu (National Cheng Kung University, Taiwan)
Hendrik Drachsler (DIPF, Germany)
Olga Viberg (KTH Royal Institute of Technology, Sweden)
Rebecca Ferguson (Open University, UK)
Simon Knight (University of Technology Sydney, Australia)
Tiffany Barnes (North Carolina State University, USA)
Tsukasa Hirashima (Hiroshima University, Japan)