About the Journal

The Journal of Educational Data Mining (JEDM; ISSN: 2157-2100; see indexing) 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, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings in which they learn.
The journal welcomes basic and applied papers describing mature work involving computational approaches of educational data mining. Specifically, it welcomes high-quality original work including but not limited to the following topics:
  • ►Processes or methodologies followed to analyse educational data
  • ►Integrating data mining with pedagogical theories
  • ►Describing the way findings are used for improving educational software or teacher support
  • ►Improving understanding of learners' domain representations
  • ►improving assessment of learners' engagement in the learning tasks
From time to time, the journal also welcomes survey articles, theoretical articles, and position papers, in as much as these articles build on existing work and advance our understanding of the challenges and opportunities unique to this area of research. More information about the journal can be found here.
Editor: Agathe Merceron, Berliner Hochschule für Technik, Germany

Associate Editors:
Ryan S. Baker, University of Pennsylvania, USA
Andrew M. Olney, University of Memphis, USA (editor, 2017-2021)
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: 2021-12-28


Tracing Systematic Errors to Personalize Recommendations in Single Digit Multiplication and Beyond

Alexander O. Savi, Benjamin E. Deonovic, Maria Bolsinova, Han L. J. van der Maas, Gunter K. J. Maris
Abstract 451 | PDF Downloads 255

Page 1-30

Predictive and Explanatory Models Might Miss Informative Features in Educational Data

Nicholas T. Young, Marcos D. Caballero
Abstract 394 | PDF Downloads 283

Page 31-86

The goal of this CSEDM special issue of JEDM is to showcase the state-of-the-art in CSEDM research, which combines educational data mining, AI or learning analytics with discipline-specific expertise in computing education to produce novel insights into how students Computer Science learn and how to support them. We invite researchers from both the CS Education and EDM/AIED/LAK research communities to contribute, and especially invite collaborative work that bridges across these communities. Effective submissions will make a contribution to both research communities, including novel EDM approaches, as well as insights for CS Education.

See the full call.