CFP

EDM2024: Seventeenth International Conference on Educational Data Mining (EDM 2024)

Atlanta, GA, United States, July 15-19, 2024

Conference website	https://educationaldatamining.org/edm2024/
Submission link	https://easychair.org/conferences/?conf=edm2024
Abstract registration deadline	February 9, 2024
Submission deadline	February 16, 2024

Topics: machine learning education learning analytics educational technology

New tools, new prospects, new risks - educational data mining in the age of generative AI

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 17^th iteration of the conference, EDM 2024, will take place in a hybrid format, both online and in-person, to facilitate participation and networking for all.

The theme of this year’s conference is “New tools, new prospects, new risks - educational data mining in the age of generative AI”. This year’s theme focuses on the movement from descriptive and predictive models to generative artificial intelligence (AI) and what that means for learning environments and processes. While the new methods unlock exciting new potentials for educational data mining, they also foreground many ethical considerations and risks that are associated with all types of machine learning and artificial intelligence. In addition to the general topics listed below, we welcome research in the following areas: mitigating biases and harms that may result from model use, accounting for the stereotypes that are inherent to the large models that drive generative AI, separating the hype surrounding these new technologies from their potential in educational settings, and finding ways to use these models to better understand learning processes and support learning.

Submission Guidelines

For all tracks, the references section at the end of the paper does not count towards the listed page limits.

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 (and accepted before May 30, 2024) 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, results so far, and aspects of the research on which advice is sought.
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. Workshop organizers should indicate whether they would prefer to host their event in a hybrid format (supporting both in-person and remote attendees) or a remote-only format.
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. Tutorial organizers should indicate whether they would prefer to host their event in a hybrid format (supporting both in-person and remote attendees) or a remote-only format.

All accepted papers will be published in the open-access proceedings of the conference, except for the Journal track as stated above. Papers submitted to workshops will be published separately in the workshop proceedings. All paper submissions must be submitted for double-blind reviewing.

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 should be formatted according to the EDM template:

Special Instructions

Workshop and Tutorial proposals

Workshop and Tutorial proposals should use the EDM proceedings template (LaTeX or Word) and include at least the following elements:

Title.
Length of workshop/tutorial: full or half-day.
Proposed format of the workshop/tutorial (e.g., approximate timeline) and type of activities (e.g., paper presentations, discussions, demos, etc.).
Description of the workshop/tutorial content and themes.
Plans for supporting remote attendees (either as a hybrid or fully-remote event)
Names, short biographies, and contact information of workshop/tutorial chair(s). For tutorials, this biography must include detailed information about the qualifications of the proposer to conduct the tutorial on the proposed topic. For workshops, include a list of organizing/program committee members, who should be from multiple universities.

JEDM 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

by selecting the option “EDM 2024 Journal Track” in the corresponding Section box.

List of Topics

Topics of interest to the conference include but are not limited to:

Developing new techniques for mining educational data.
Closing the loop between 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 with different formats, including programming practices, essays, and videos
- Algorithms to improve existing domain models
- Novel 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
- Offline and online evaluation methods for 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
- Developing, improving, and evaluating explainable EDM algorithms
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
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

Organizing Team

General Chair:

David Joyner (Georgia Tech, USA)

Program Chairs:

Benjamin Paaßen (they/them) - Bielefeld University, Germany
Carrie Demmans Epp (she/her) - University of Alberta, Canada

Web Chairs:

Paul Salvador Inventado (he/him) - California State University Fullerton, USA
Ramkumar Rajendran (he/him) - IIT Bombay, India

Industry Track Chairs:

Carol M. Forsyth (she/her) - Educational Testing Service, USA
Avi Segal (he/him) - Ben-Gurion University of the Negev, Israel

Doctoral Consortium Chairs:

Neil Heffernan - Worcester Polytechnic Institute, USA
Luc Paquette (he/him) - University of Illinois Urbana-Champaign, USA

JEDM Track Chairs:

Maria Mercedes T. Rodrigo (she/her) - Ateneo de Manila University, Philippines
Agathe Merceron (she/her) - Berlin Hochschule für Technik, Germany

Poster & Demo Track Chairs:

Heeryung Choi (she/her) - Massachusetts Institute of Technology, USA
Irena Koprinska (she/her) - The University of Sydney, Australia

Workshop & Tutorial Chairs:

Bita Akram (she/her) - North Carolina State University, USA
Sergey Sosnovsky - Utrecht University, Netherlands

Equity, Diversity, and Inclusion Chairs:

Anna Rafferty (she/her) - Carleton College, USA
Jie Tang (he/him) - Tsinghua University, China

Accessibility Chairs:

Nigel Bosch (he/him) - University of Illinois Urbana-Champaign, USA

Social Media & Publicity Chairs:

Oleksandra Poquet - Technical University of Munich, Germany
Jill-Jênn Vie (he/him) - Inria Saclay, France

Proceedings Chairs:

Mirko Marras (he/him) - University of Cagliari, Italy

Venue

Atlanta, Georgia, USA