About the Journal

The Journal of Educational Data Mining (JEDM; ISSN: 2157-2100; see indexing) is published by the International Educational Data Mining Society (IEDMS). It is an international and interdisciplinary forum of research on computational approaches for analyzing electronic repositories of student data to answer educational questions. It is completely and permanently free and open-access to both authors and readers

Educational Data Mining is an emerging discipline dedicated to developing methods that explore the unique data generated in educational settings. The goal is to deepen our understanding of students and their learning environments through innovative and impactful research. Key data sources in EDM include:

  • Student interactions within interactive learning environments.
  • Learner test data and assessment artifacts.
  • Digital didactic materials.
  • Usage patterns in learning management systems.

Types of Submissions:

The journal seeks high-quality original work that emphasizes novelty and impact in the field. Accepted submissions should extend beyond mere application and must include elements that contribute to broader knowledge, such as generalizable methodologies or comparative analyses. Specific areas of interest include, but are not limited to:

  • Innovative Processes or Methodologies: Developing and detailing new processes or methodologies for analyzing educational data.
  • Integration with Pedagogical Theories: Research that advances pedagogical theories through data-driven insights.
  • Broader Applicability of Educational Software: Work that not only improves educational software but also demonstrates the generalizable applicability of findings across different contexts.
  • Advancing Understanding of Learner Cognition: Research that enhances our understanding of learners' domain representations and cognitive processes.
  • Comparative Assessment of Learner Engagement: Studies that compare different approaches to assessing learner engagement and effectiveness.

The journal also welcomes survey articles, theoretical articles, and position papers, provided they build on existing research and offer significant contributions to the field.  Please look here for additional information.

 
Editor: Philip I. Pavlik Jr., University of Memphis, United States

Associate Editors:
Ryan S. Baker, University of Pennsylvania, United States
Min Chi, North Carolina State University, United States
Agathe Merceron, Berlin University of Applied Sciences, Germany (editor, 2022-2024 July,18)
Andrew M. Olney, University of Memphis, United States (editor, 2017-2021)
Luc Paquette, University of Illinois at Urbana-Champaign, United States
Anna N. Rafferty, Carleton College, United States
Olga C. Santos, Universidad Nacional de Educación a Distancia, Spain
Kalina Yacef, University of Sydney, Australia (founding editor, 2008-2013)
 
Accessibility Production Editor:
Nigel Bosch, University of Illinois Urbana-Champaign, USA
 

Former Editors:

Agathe Merceron, Berlin University of Applied Sciences, Germany, 2022-2024 July,18
Andrew M. Olney, University of Memphis, United States, 2017-2021
Michel Desmarais, Polytechnique Montreal, Canada, 2014-2016
Kalina Yacef, University of Sydney, Australia, founding editor, 2008-2013
 
 
Author guidelines and submission guidelines can be found here. All other inquiries should be emailed to: info@jedm.educationaldatamining.org.
 

Current Issue

Published: 2025-01-15

Editorial Acknowledgement

Philip Irvin Pavlik Jr., Carrie Demmans Epp, Benjamin Paaßen, Agathe Merceron, Roberto Matinez-Maldonado, Nigel Bosch
Abstract 46 | PDF Downloads 45 HTML Downloads 27

Page i-v

Articles

Optimizing Bayesian Knowledge Tracing with Neural Network Parameter Generation

Anirudhan Badrinath, Zachary Pardos
Abstract 418 | PDF Downloads 368 HTML Downloads 226

Page 41-65

Extended Articles from the EDM 2024 Conference

PharmaSimText: A Text-Based Educational Playground filled with RL-LLM Agents That Work Together Even in Disagreement

Bahar Radmehr, Adish Singla, Tanja Käser
Abstract 482 | PDF Downloads 218 HTML Downloads 310

Page 1-40

Designing Safe and Relevant Generative Chats for Math Learning in Intelligent Tutoring Systems

Zachary Levonian, Owen Henkel, Chenglu Li, Millie-Ellen Postle
Abstract 678 | PDF Downloads 391 HTML Downloads 523

Page 66-97

Optimizing Speaker Diarization for the Classroom: Applications in Timing Student Speech and Distinguishing Teachers from Children

Jiani Wang, Shiran Dudy, Xinlu He, Zhiyong Wang, Rosy Southwell, Jacob Whitehill
Abstract 395 | PDF Downloads 232 HTML Downloads 260

Page 98-125

Workload Overload? Late Enrollment Leads to Course Dropout

Conrad Borchers, Yinuo Xu, Zachary A. Pardos
Abstract 521 | PDF Downloads 279 HTML Downloads 222

Page 126-156

360-Degree Cameras vs Traditional Cameras in Multimodal Learning Analytics: Comparative Study of Facial Recognition and Pose Estimation

Robin Jephthah Rajarathinam, Chris Palaguachi, Jina Kang
Abstract 148 | PDF Downloads 107 HTML Downloads 144

Page 157-182

Propositional Extraction from Collaborative Naturalistic Dialogues

Videep Venkatesha, Abhijnan Nath, Ibrahim Khebour, Avyakta Chelle, Mariah Bradford, Jingxuan Tu, Hannah VanderHoeven, Brady Bhalla, Austin Youngren, Jack Fitzgerald, James Pustejovsky, Nathaniel Blanchard, Nikhil Krishnaswamy
Abstract 238 | PDF Downloads 149 HTML Downloads 81

Page 183-216

Intrinsic and Contextual Factors Impacting Student Ratings of Automatically Generated Questions: A Large-Scale Data Analysis

Benny G. Johnson, Jeffrey S. Dittel, Rachel Van Campenhout
Abstract 174 | PDF Downloads 117 HTML Downloads 202

Page 217-247

EDM 2025 Journal Track

Evaluating the Effects of Assignment Report Usage on Student Outcomes in an Intelligent Tutoring System: A Randomized-Encouragement Design

Wen Chiang Lim, Neil T. Heffernan, Adam Sales
Abstract 195 | PDF Downloads 121 HTML Downloads 125

Page 248-275

Predicting Perceived Text Complexity: The Role of Person-Related Features in Profile-Based Models

Boris Thome, Friederike Hertweck, Stefan Conrad
Abstract 133 | PDF Downloads 126 HTML Downloads 391

Page 276-307

Using a Randomized Experiment to Compare Mastery Learning Thresholds

Jeffrey Matayoshi, Eric Cosyn, Hasan Uzun, Eyad Kurd-Misto
Abstract 88 | PDF Downloads 72 HTML Downloads 86

Page 308-336