Journal of Educational Data Mining

Scope

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. Such data types include student traces within interactive learning environments, learner test data and artefacts used for assessment, didactic material available in electronic form for mining, and usage traces left by students and instructors in learning management systems of all kinds. 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. Submissions that extend previously published conference papers are welcome provided that the journal submission makes a substantive new contribution (at least 1/3 new content) and any copyright permission on previous material is obtained. Submissions that are extensions of previously published conference papers must be accompanied by a cover letter outlining the new contributions.