EDM 2021 - 14th International Conference on Educational Data Mining

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 14th iteration of the conference, EDM 2021, 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 *Improving Blended and Distance Learning (BDL)*. The theme focuses on identifying learning or teaching strategies that can be used to improve learning in various formats, such as partially or fully online, synchronous or asynchronous, and centralized or federated. In addition to the general topics listed below, we welcome research in the following areas: receiving implicit and explicit feedback from learners in BDL environments, interacting with students to ensure no learner is left behind, integrating and utilizing learning analytics in BDL environments to cope with switching between in-person and online modes, and addressing emerging privacy and ethical challenges in the new learning setting.
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* Topics of Interest *
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
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* Submission Types*
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, 2021) 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.
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/J...
All other papers should be formatted according to the EDM template:
* Word: https://educationaldatamining.org/edm2020/wp-conte...
* LaTeX: https://educationaldatamining.org/edm2020/wp-conte...
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.
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* Important Dates *
* JEDM track papers ----- January 3, 2021
* Workshop and Tutorial proposals ----- January 22, 2021
* Acceptance notifications for workshops and tutorials ----- February 5, 2021
* Abstracts for full and short papers ----- February 19, 2021
* Full papers and short papers ----- February 26, 2021
* Industry papers ----- February 26, 2021
* Posters and demos ----- March 5, 2021
* Doctoral consortium papers ----- March 5, 2021
* Acceptance notifications for full and short papers ----- April 9, 2021
* Acceptance notifications for posters, demos, and doctoral consortium papers ----- April 16, 2021
* Camera-ready copy due ----- April 30, 2021
* Due dates and acceptance notifications for workshop papers ----- Set by workshop organizers
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* Organizing Team *
General Chairs:
* François Bouchet (Sorbonne University, Paris, France)
* Jill-Jênn Vie (Inria, France)
Program Chairs:
* Sharon Hsiao (Arizona State University, AZ, USA)
* Shaghayegh Sherry Sahebi (University at Albany, NY, USA)
Workshop & Tutorial Chairs:
* Thomas Price (North Carolina State University, NC, USA)
* Sweet San Pedro (ACTNext)
Industry Track Chairs:
* Giora Alexandron (Weizmann Institute of Science, Israel)
* Niki Gitinabard (North Carolina State University, NC, USA)
Doctoral Consortium Chairs:
* Min Chi (North Carolina State University, NC, USA)
* Gautam Biswas (Vanderbilt University, TN, USA)
JEDM Track Chairs:
* Amal Zouaq (Ecole Polytechnique de Montréal, QC, Canada)
* Olga Santos (UNED, Spain)
Poster & Demo Track Chairs:
* Ange Tato (Université du Québec à Montréal, QC, Canada)
* Hassan Khosravi (University of Queensland, Australia)
Publication/Proceedings Chairs:
* Fatima Harrak (Sorbonne University, Paris, France)
* Cheng-Yu Chung (Arizona State University, AZ, USA)
Sponsorship Chair:
* Benoît Choffin (Paris-Saclay University, France)
Publicity/Social Media Chair:
* Khushboo Thaker (University of Pittsburgh, PA, USA)
Web Chair:
* Paul Salvador Inventado (California State University Fullerton, CA, USA)