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: 2024-10-17

Special Section EDM Cup 2023

Predicting Students’ Future Success: Harnessing Clickstream Data with Wide & Deep Item Response Theory

Shi Pu, Yu Yan, Brandon Zhang
Abstract 147 | HTML Downloads 57 PDF Downloads 137

Page 1-31

ClickTree: A Tree-based Method for Predicting Math Students’ Performance Based on Clickstream Data

Narjes Rohani, Behnam Rohani, Areti Manataki
Abstract 113 | HTML Downloads 35 PDF Downloads 70

Page 32-57

Articles

ACE: AI-Assisted Construction of Educational Knowledge Graphs with Prerequisite Relations

Mehmet Cem Aytekin, Yücel Saygın
Abstract 59 | HTML Downloads 10 PDF Downloads 14

Page 85-114

Explaining Explainability: Early Performance Prediction with Student Programming Pattern Profiling

Muntasir Hoq, Peter Brusilovsky, Bita Akram
Abstract 39 | HTML Downloads 11 PDF Downloads 8

Page 115-148

Estimation of ICAP States Based on Interaction Data During Collaborative Learning

Yoshimasa Ohmoto, Shigen Shimojo, Junya Morita, Yugo Hayashi
Abstract 7 | HTML Downloads 3 PDF Downloads 0

Page 149-176