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)
