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: Andrew M. Olney, University of Memphis, USA
 
Associate Editors:
Ryan S. Baker, University of Pennsylvania, USA
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 on the NAEP Data Mining Competition.

Ryan S. Baker, Neil T. Heffernan, Thanaporn Patikorn, Carol M. Forsyth, and Irvin R. Katz, Editors

Published: 2021-08-26

Editorial Acknowledgments and Introduction to the Special Issue for the NAEP Data Mining Competition

Ryan S. Baker, Neil T. Heffernan, Thanaporn Patikorn Patikorn, Carol M. Forsyth, Irvin R. Katz
Abstract 98 | PDF Downloads 41
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Page i

Scientific Findings from the NAEP 2019 Data Mining Competition

Process Mining Combined with Expert Feature Engineering to Predict Efficient Use of Time on High-Stakes Assessments

Nathan A. Levin
Abstract 155 | PDF Downloads 82
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Page 1-15

Modeling NAEP Test-Taking Behavior Using Educational Process Analysis

Nirmal Patel, Aditya Sharma, Tirth Shah, Derek Lomas
Abstract 254 | PDF Downloads 106
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Page 16-54

AutoML Feature Engineering for Student Modeling Yields High Accuracy, but Limited Interpretability

Nigel Bosch
Abstract 116 | PDF Downloads 58
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Page 55-79

Applying Psychometric Modeling to aid Feature Engineering in Predictive Log-Data Analytics: The NAEP EDM Competition

Fabian Zehner, Beate Eichmann, Tobias Deribo, Scott Harrison, Daniel Bengs, Nico Andersen, Carolin Hahnel
Abstract 120 | PDF Downloads 61
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Page 80-107