Journal of Educational Data Mining

Educational Data Mining is an emerging discipline dedicated to developing methods that explore the unique data generated in educational settings. The goal is to deepen our understanding of students and their learning environments through innovative and impactful research. Key data sources in EDM include:

• Student interactions within interactive learning environments.
• Learner test data and assessment artifacts.
• Digital didactic materials.
• Usage patterns in learning management systems.

Types of Submissions:

The journal seeks high-quality original work that emphasizes novelty and impact in the field. Accepted submissions should extend beyond mere application and must include elements that contribute to broader knowledge, such as generalizable methodologies or comparative analyses. Specific areas of interest include, but are not limited to:

• Innovative Processes or Methodologies: Developing and detailing new processes or methodologies for analyzing educational data.
• Integration with Pedagogical Theories: Research that advances pedagogical theories through data-driven insights.
• Broader Applicability of Educational Software: Work that not only improves educational software but also demonstrates the generalizable applicability of findings across different contexts.
• Advancing Understanding of Learner Cognition: Research that enhances our understanding of learners' domain representations and cognitive processes.
• Comparative Assessment of Learner Engagement: Studies that compare different approaches to assessing learner engagement and effectiveness.

The journal also welcomes survey articles, theoretical articles, and position papers, provided they build on existing research and offer significant contributions to the field.  Please look here for additional information.