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, 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, Berlin University of Applied Sciences, Germany

Associate Editors:
Ryan S. Baker, University of Pennsylvania, United States
Min Chi, North Carolina State University, United States
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)
 

Former Editors

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: 2023-06-21

Editorial Acknowledgment

Agathe Merceron, Ryan S. Baker, Min Chi, Andrew M. Olney, Anna Rafferty, Maria Mercedes T. Rodriguo, Kalina Yacef
Abstract 64 | PDF Downloads 74
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Page i-iii

Articles

KT-Bi-GRU: Student Performance Prediction with a Bi-Directional Recurrent Knowledge Tracing Neural Network

Marina Delianidi, Konstantinos Diamantaras
Abstract 208 | PDF Downloads 243
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Page 1-21

EDM 2023 Journal Track

Using Demographic Data as Predictor Variables: a Questionable Choice

Ryan S. Baker, Lief Esbenshade, Jonathan Vitale, Shamya Karumbaiah
Abstract 482 | PDF Downloads 204
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Page 22-52

Using Auxiliary Data to Boost Precision in the Analysis of A/B Tests on an Online Educational Platform: New Data and New Results

Adam C. Sales, Ethan B. Prihar, Johann A. Gagnon-Bartsch, Neil T. Heffernan
Abstract 95 | PDF Downloads 66
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Page 53-85

Extended Articles from the EDM 2022 Conference

Automatically Predicting Peer Satisfaction During Collaborative Learning with Linguistic, Acoustic, and Visual Features

Yingbo Ma, Gloria Ashiya Katuka, Mehmet Celepkolu, Kristy Elizabeth Boyer
Abstract 89 | PDF Downloads 82
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Page 86-122

Less But Enough: Evaluation of peer reviews through pseudo-labeling with less annotated data

Chengyuan Liu, Divyang Doshi, Ruixuan Shang, Jialin Cui, Qinjin Jia, Edward Gehringer
Abstract 123 | PDF Downloads 83
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Page 123-140