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: 2024-06-27

Editorial Acknowledgment

Agathe Merceron, Maria Mercedes T. Rodrigo, Jaclyn Ocumpaugh, Ryan S. Baker, Min Chi, Andrew M. Olney, Luc Paquette, Anna Rafferty, Olga C. Santos, Kalina Yacef
Abstract 150 | HTML Downloads 256 PDF Downloads 84

Page i-iv

EDM 2024 Journal Track

The Knowledge Component Attribution Problem for Programming: Methods and Tradeoffs with Limited Labeled Data

Yang Shi, Robin Schmucker, Keith Tran, John Bacher, Kenneth Koedinger, Thomas Price, Min Chi, Tiffany Barnes
Abstract 291 | HTML Downloads 137 PDF Downloads 164

Page 1-33

Automated Evaluation of Classroom Instructional Support with LLMs and BoWs: Connecting Global Predictions to Specific Feedback

Jacob Whitehill, Jennifer LoCasale-Crouch
Abstract 224 | HTML Downloads 90 PDF Downloads 147

Page 34-60

An Approach to Improve k-Anonymization Practices in Educational Data Mining

Frank Stinar, Zihan Xiong, Nigel Bosch
Abstract 135 | HTML Downloads 79 PDF Downloads 205

Page 61-83

Exploring the Impact of Symbol Spacing and Problem Sequencing on Arithmetic Performance: An Educational Data Mining Approach

Avery Harrison Closser, Anthony F. Botelho, Jenny Yun-Chen Chan
Abstract 182 | HTML Downloads 100 PDF Downloads 116

Page 84-111

Effect of Gamification on Gamers: Evaluating Interventions for Students Who Game the System

Kirk P. Vanacore, Ashish Gurung, Adam Sales, Neil Heffernan
Abstract 153 | HTML Downloads 75 PDF Downloads 158

Page 112-140

LearnSphere: A Learning Data and Analytics CyberInfrastructure

John Stamper, Steven Moore, Carolyn Rose, Philip Pavlik, Kenneth Koedinger
Abstract 152 | HTML Downloads 93 PDF Downloads 153

Page 141-163

Session-based Methods for Course Recommendation

Md Akib Zabed Khan, Agoritsa Polyzou
Abstract 144 | HTML Downloads 75 PDF Downloads 162

Page 164-196

Articles

Analyzing Transitions in Sequential Data with Marginal Models

Jeffrey Matayoshi, Shamya Karumbaiah
Abstract 71 | HTML Downloads 57 PDF Downloads 81

Page 197-232

Supercharging BKT with Multidimensional Generalizable IRT and Skill Discovery

Mohammad M. Khajah
Abstract 93 | HTML Downloads 106 PDF Downloads 129

Page 233-278

Structural Neural Networks Meet Piecewise Exponential Models for Interpretable College Dropout Prediction

Chuan Cai, Adam Fleischhacker
Abstract 139 | HTML Downloads 79 PDF Downloads 115

Page 279-302

Extended Articles from the EDM 2023 Conference

Investigating Concept Definition and Skill Modeling for Cognitive Diagnosis in Language Learning

Boxuan Ma, Sora Fukui, Yuji Ando , Shinichi Konomi
Abstract 156 | HTML Downloads 61 PDF Downloads 136

Page 303-329

A Course Recommender System Built on Success to Support Students at Risk in Higher Education

Kerstin Wagner, Agathe Merceron, Petra Sauer, Niels Pinkwart
Abstract 161 | HTML Downloads 67 PDF Downloads 148

Page 330-364

A Comprehensive Study on Evaluating and Mitigating Algorithmic Unfairness with the MADD Metric

Mélina Verger, Chunyang Fan, Sébastien Lallé, François Bouchet, Vanda Luengo
Abstract 134 | HTML Downloads 91 PDF Downloads 94

Page 365-409