About the Journal

The Journal of Educational Data Mining (JEDM; Impact factor: 3.68; 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
  • ►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: Andrew Olney, University of Memphis, USA
 
Associate Editors:
Ryan S. Baker, University of Pennsylvania, USA
Michel C. Desmarais, Polytechnique Montreal, Canada (editor, 2013-2017)
Agathe Merceron, Beuth University of Applied Sciences Berlin, Germany
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

Special issue of the EDM 2018 conference journal track.

Min Chi and Irena Koprinska, Editors

   

Published: 2018-10-25

Editorial Acknowledgements and Introduction to the Special Issue on EDM Journal Track

Min Chi, Irena Koprinska; Andrew Olney
Abstract 20 | PDF Downloads 76
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Page i-ii

EDM 2018 Journal Track

dAFM: Fusing Psychometric and Connectionist Modeling for Q-matrix Refinement

Zachary A. Pardos, Anant Dadu
Abstract 28 | PDF Downloads 115
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Page 1-27

Deep Learning vs. Bayesian Knowledge Tracing: Student Models for Interventions

Ye Mao, Chen Lin, Min Chi
Abstract 89 | PDF Downloads 178
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Page 28-54

Most of the Time, It Works Every Time: Limitations in Refining Domain Models with Learning Curves

Ilya Goldin, April Galyardt
Abstract 25 | PDF Downloads 64
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Page 55-92

Special Issue Call for Papers: Scientific Findings from the ASSISTments Longitudinal Data Competition 2018-08-01

Guest Editors
Ryan S. Baker (Associate Editor), University of Pennsylvania
Neil T. Heffernan, Worcester Polytechnic Institute
Thanaporn “March” Patikorn, Worcester Polytechnic Institute

Aim of Special Issue
We invite paper submissions for a special issue of the peer-reviewed Journal of Educational Data Mining that focuses on the scientific findings from the ASSISTments Longitudinal Data Competition.

JEDM Impact Factor 2018-01-26

Although JEDM is not currently indexed, we have calculated the two-year impact factor for 2017 using the Journal Citation Report (JCR) methodology with Google Scholar. Because Google Scholar includes a wide range of publication types, we applied additional criteria of excluding e-prints and technical reports. Even so, our impact factor is more permissive than JCR because it is not restricted to indexed sources.

JEDM's two-year impact factor in 2017 is 3.68