JEDM - Journal of Educational Data Mining

The Journal of Educational Data Mining (JEDM; ISSN 2157-2100) 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 the data mining with pedagogical theories,
  • describing the way findings are used for improving educational software or teacher support,
  • improving understanding of learners' domain representations, and
  • 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.
Editor: Michel C. Desmarais, Polytechnique Montreal, Canada
Associate Editors:
Ryan S. Baker, Teachers College Columbia University, USA
Agathe Merceron, University of Applied Sciences, Germany
Mykola Pechenizkiy, Technische Universiteit Eindhoven, Netherlands
Kalina Yacef, University of Sydney, Australia (Founding editor-in-chief 2008-2013)
Web Editor: Behzad Beheshti, Polytechnique Montreal, Canada
Author guidelines and submission guidelines can be found here. All other inquiries should be emailed to:


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Vol 7, No 2 (2015): EDM Journal track special issue

The EDM conference and the Journal of Educational Data Mining (JEDM) share a common track, the EDM 2015 Journal Track. The intention is to accommodate researchers who want to contribute a more substantial contribution than space allows at the EDM conference, and yet share the opportunity to have a presence at the conference and present their work to a live audience.

Table of Contents

EDM 2015 Journal Track

Radek Pelánek
Benjamin Clement, Didier Roy, Pierre-Yves Oudeyer, Manuel Lopes
Aditi Mallavarapu, Leilah Lyons, Tia Shelley, Brian Slattery
April Galyardt, Ilya Goldin