About the Journal
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 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
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
Articles
Adjusting the L Statistic when Self-Transitions are Excluded in Affect Dynamics
Jeffrey Matayoshi, Shamya Karumbaiah
Page 1-23
Extending the Hint Factory for the Assistance Dilemma: A Novel, Data-driven HelpNeed Predictor for Proactive Problem-solving Help
Mehak Maniktala, Christa Cody, Amy Isvik, Nicholas Lytle, Min Chi, Tiffany Barnes
Page 24-65
LogCF: Deep Collaborative Filtering with Process Data for Enhanced Learning Outcome Modeling
Fu Chen, Ying Cui
Page 66-99
- Vol 12, No 4 (2020)
- Vol 12, No 3 (2020)
- Vol 12, No 2 (2020)
- Vol 12, No 1 (2020)
- Vol 11, No 3 (2019)
- Vol 11, No 2 (2019)
- Vol 11, No 1 (2019)
- Vol 10, No 3 (2018)
- Vol 10, No 2 (2018)
- Vol 10, No 1 (2018)
- Vol 9, No 2 (2017)
- Vol 9, No 1 (2017)
- Vol 8, No 2 (2016)
- Vol 8, No 1 (2016)
- Vol 7, No 3 (2015)
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- Vol 6, No 1 (2014)
- Vol 5, No 2 (2013)
- Vol 5, No 1 (2013)
- Vol 4, No 1 (2012)
- Vol 3, No 1 (2011)
- Vol 2, No 1 (2010)
- Vol 1, No 1 (2009)