JEDM - Journal of Educational Data Mining

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 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, and
  • 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.
 
All papers should describe the supporting evidence in ways that can be verified or replicated by other researchers to a large extent. It is encouraged, though not required, for researchers to make their data sets, software code, and intermediate results available to the community for inspection and re-use. Submitted papers should also detail the data mining/modeling/analysis component of the submitted work clearly and include discussions of the findings in relation to educational questions.
 
Editor: Michel C. Desmarais, Polytechnique Montreal, Canada
 
Associate Editors:
Ryan S. Baker, Teachers College Columbia University, USA
Agathe Merceron, University of Applied Sciences, Germany
Mykola Pechenizkiy, Technische Universiteit Eindhoven, Netherlands
Kalina Yacef, University of Sydney, Australia (Founding editor-in-chief 2008-2013)
 
 
Web Editor: Behzad Beheshti, Polytechnique Montreal, Canada
 
Author guidelines and submission guidelines can be found here. All other inquiries should be emailed to: jedm.editor@gmail.com.



Vol 5, No 2 (2013)

Table of Contents

Articles

Properties of the Bayesian Knowledge Tracing Model PDF
Brett van de Sande 1-10
A First Step in Learning Analytics: Pre-processing Low-Level Alice Logging Data of Middle School Students PDF
Linda Werner, Charlie McDowell, Jill Denner 11-37
Clustering Educational Digital Library Usage Data: A Comparison of Latent Class Analysis and K-Means Algorithms PDF
Beijie Xu, Mimi Recker, Xiaojun Qi, Nicholas Flann, Lei Ye 38-68
Beacon- and Schema-Based Method for Recognizing Algorithms from Students’ Source Code PDF
Ahmad Taherkhani, Lauri Malmi 69-101
Toward a Framework for Learner Segmentation PDF
Bahareh Azarnoush, Jennifer M. Bekki, George C. Runger, Bianca L. Bernstein, Robert K. Atkinson 102-126


ISSN: 2157-2100