Conference Theme
Educational Data Mining Across Borders: Bridging Disciplines, Contexts, and Perspectives
Educational Data Mining is a leading international forum for high-quality research that mines datasets to answer educational research questions, including exploring how people learn and how they teach. These data may originate from a variety of learning contexts, including learning and information management systems, interactive learning environments, intelligent tutoring systems, educational games, and data-rich learning activities. Educational data mining considers a wide variety of types of data, including but not limited to log files, student-produced artifacts, discourse, learning content and context, sensor data, and multi-resource and multimodal streams. The overarching goal of the Educational Data Mining research community is to support learners and teachers more effectively, by developing data-driven understandings of the learning and teaching processes in a wide variety of contexts and for diverse learners.
The 19th iteration of the conference, EDM 2026, will participate in the Festival of Learning 2026.
The theme of this year’s conference is “Educational Data Mining Across Borders: Bridging Disciplines, Contexts, and Perspectives”. Educational Data Mining has reached a stage where methods and findings are relevant across multiple areas of research and practice. Many of the challenges in learning and teaching cannot be addressed from a single perspective, and solutions are strengthened by crossing disciplinary, contextual, and cultural boundaries. EDM 2026 emphasizes the opportunities that emerge through building connections across disciplines, contexts, geographies, cultures, and societies.This year’s theme highlights work that integrates perspectives, links research and practice, and broadens the impact of educational data mining to support diverse and inclusive learning opportunities.
Topics of Interest
Topics of interest to the conference include but are not limited to:
- Developing new techniques for mining educational data
- Bridging EDM research and learning sciences
- Informing data mining research with educational and/or motivational theories
- Actionable advice rooted in educational data mining research, experiments, and outcomes
- Evaluating the efficacy of curriculum and interventions
- Domain Knowledge Modeling
- Deriving representations of domain knowledge from data
- Algorithms for discovering relationships, associations, and prerequisite structures between learning resources
- Algorithms to improve existing domain models
- Human-in-the-loop methods to collect domain knowledge models, including crowd-sourcing and expert tagging
- Educational Recommenders, Instructional Sequencing, and Personalized Learning
- Learning resource recommendation algorithms, remedial recommendations, and learner
- choice in selecting the next activity
- Goal-oriented instructional sequencing
- Personalized course recommendations
- Peer recommendation for collaborative learning
- Methodologies for evaluating educational recommender systems and sequencing algorithms
- Equity, Privacy, Transparency, and Fairness
- Ethical considerations in EDM
- Legal and social policies to govern EDM
- Developing privacy-protecting EDM algorithms and detecting learner privacy violations in
- existing methods
- Developing and applying fairer learning algorithms, and detecting and correcting instances of algorithmic unfairness in existing methods
- Human Factors, Transparency, and Explainability
- Developing, improving, and evaluating explainable schemes for EDM systems
- Human factors that shape educational users’ acceptance of EDM-based
- recommendations
- Educational users’ trust and attitudes towards EDM technologies
- Learner Cognitive and Behavior Modeling and its association with performance
- Modeling and detecting students’ affective and cognitive states (e.g., engagement, confusion) with multimodal data
- Temporal patterns in student behavior including gaming the system, procrastination, and
- sequence modeling
- Data mining to understand how learners interact with various pedagogical environments such as educational games and exploratory learning environments
- Learner Knowledge and Performance Modeling
- Automatically assessing student knowledge
- Learner knowledge gain and forgetting models in domains with complex concept structures
- Modeling real-world problem-solving in open-ended domains
- Causal inference of students’ learning
- Predicting students’ future performance
- Detecting cheating and academic dishonesty
- Comparing Human and Artificial Intelligence
- Analyzing generative AI’s capabilities to perform complex learning tasks
- Developing methods to identify the use of generative AI in learning environments (e.g., for cheating)
- Learning Analytics
- Institutional analytics
- Learner profiling
- Multimodal analytics
- Social and Collaborative Learning
- Modeling student and group verbal and non-verbal interactions for collaborative and/or competitive problem-solving
- Social network analysis of student and teacher interactions
- Data mining to understand how learners interact in formal and informal educational contexts
- Peer-assessment modeling
- Social learner modeling
- Reproducibility
- Replicating previous studies with larger sample sizes, in different domains, and/or in more diverse contexts
- Facilitating accessible benchmarking systems and publishing educational datasets that are useful for the community
Submission Types
For all tracks, the acknowledgements and references sections at the end of the paper do not count towards the listed page limits. Note that long papers with borderline scores will not be accepted as short papers. It is the authors’ responsibility to consider the best fit for their submission in terms of length.
- Full Papers — 10 pages. Should describe original, substantive, mature, and unpublished work.
- Short Papers — 6 pages. Should describe original, unpublished work. This includes early stage, less developed works in progress.
- JEDM Journal Track Papers — Papers submitted to the Journal of Educational Data Mining track in the section “EDM 2026 Journal Track” (and accepted before May 31, 2026) will be published in JEDM and presented during the JEDM track of the conference.
- Industry Papers — 6 pages. Should describe innovative uses of EDM techniques in a commercial setting.
- Doctoral Consortium — 2-4 pages. Should describe the graduate/postgraduate student’s research topic, proposed contributions, and results so far. See below for details.
- Posters/Demos — 2-4 pages. Posters should describe original unpublished work in progress or last-minute results. Demos should describe EDM tools and systems, or educational systems that use EDM techniques.
- Workshop proposals — 2-4 pages. Should describe the organizers’ plan both to conduct the workshop (e.g., format, rough schedule, proposed list of speakers) and to stimulate growth in the workshop’s area of focus.
- Tutorial proposals — 2-4 pages. Should motivate and describe succinctly the field or tool that will be presented, as well as a plan for attendees to learn it in a hands-on way.
Submission Guidelines
- All accepted papers will be published in the proceedings of the conference, except for the Journal track as stated above. Workshop papers will be published based on the decision and under the responsibility of the workshop organizers.
- All paper submissions must be submitted for double-blind reviewing.
- All papers must not have been submitted for publication at other venues.
- Links to existing source code are encouraged, however to keep the double-blind reviewing we suggest using a service such as Anonymous GitHub (https://anonymous.4open.science).
- All papers – except the papers submitted to the JEDM Journal Track, see below – should be formatted according to the EDM template:
- Papers not adhering to the provided templates, not respecting the page limits associated to the respective track, making unauthorized changes (e.g., decreasing font sizes, altering the position of captions in figures and tables, changing margins of columns or between sections, or allowing text, figures and/or tables to extend beyond the column margins), or failing to comply with accessibility guidelines (e.g., not providing alt text for figures) may be rejected without review.
- Accepted papers will be subject to the EDM publication agreement, available at https://educationaldatamining.org/EDM_ORG/wp-content/uploads/2022/02/IEDMS-publishing-agreement.docx, which is based on Creative Commons 4.0. DOIs will be assigned to all the accepted papers via Zenodo, and an ISBN will be included in the proceedings. The papers will also be submitted for indexing consideration by DBLP, Google Scholar, and Scopus.
Papers can be submitted through the Easychair platform: https://easychair.org/conferences/?conf=edm2026
JEDM Journal Track Papers
JEDM track papers should be formatted according to the JEDM guidelines and should be submitted to the journal directly at: https://jedm.educationaldatamining.org/index.php/JEDM/about/submissions Select the option “EDM 2026 Journal Track” in the corresponding Section box.
Important Dates
All dates refer to 23:59 (11:59 pm) Anywhere on Earth. All deadlines are firm. No extension will be granted.
| JEDM track papers cut-off deadline 1 | November 1, 2025 |
| JEDM track papers cut-off deadline 2 | December 1, 2025 |
| JEDM track papers cut-off deadline 3 | January 26, 2026 |
| Abstracts for full and short papers | February 2, 2026 |
| Full papers and short papers | February 9, 2026 |
| Industry papers | February 9, 2026 |
| Workshop and Tutorial proposals | February 9, 2026 |
| Acceptance notifications for workshop and tutorial submissions | February 23, 2026 |
| Acceptance notifications for full, short, and industry papers | March 30, 2026 |
| Revisions due for submissions with conditional accept decisions | April 6, 2026 |
| Posters and demos; Doctoral consortium papers | April 6, 2026 |
| Acceptance notifications for posters, demos, and Doctoral consortium papers | April 20, 2026 |
| Camera-ready copy due | April 30, 2026 |
| Conference Dates | June 29 – July 3, 2026 |