About the Journal
The Journal of Educational Data Mining (JEDM; ISSN: 2157-2100; see indexing) is published by the International Educational Data Mining Society (IEDMS). It 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 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.
Former Editors:
Current Issue
Editorial Acknowledgment
Page i-iv
EDM 2024 Journal Track
The Knowledge Component Attribution Problem for Programming: Methods and Tradeoffs with Limited Labeled Data
Page 1-33
Automated Evaluation of Classroom Instructional Support with LLMs and BoWs: Connecting Global Predictions to Specific Feedback
Page 34-60
An Approach to Improve k-Anonymization Practices in Educational Data Mining
Page 61-83
Exploring the Impact of Symbol Spacing and Problem Sequencing on Arithmetic Performance: An Educational Data Mining Approach
Page 84-111
Effect of Gamification on Gamers: Evaluating Interventions for Students Who Game the System
Page 112-140
LearnSphere: A Learning Data and Analytics CyberInfrastructure
Page 141-163
Session-based Methods for Course Recommendation
Page 164-196
Articles
Analyzing Transitions in Sequential Data with Marginal Models
Page 197-232
Supercharging BKT with Multidimensional Generalizable IRT and Skill Discovery
Page 233-278
Structural Neural Networks Meet Piecewise Exponential Models for Interpretable College Dropout Prediction
Page 279-302
Extended Articles from the EDM 2023 Conference
Investigating Concept Definition and Skill Modeling for Cognitive Diagnosis in Language Learning
Page 303-329
A Course Recommender System Built on Success to Support Students at Risk in Higher Education
Page 330-364
A Comprehensive Study on Evaluating and Mitigating Algorithmic Unfairness with the MADD Metric
Page 365-409
- Vol 16, No 1 (2024)
- Vol 15, No 3 (2023)
- Vol 15, No 2 (2023)
- Vol 15, No 1 (2023)
- Vol 14, No 3 (2022)
- Vol 14, No 2 (2022)
- Vol 14, No 1 (2022)
- Vol 13, No 4 (2021)
- Vol 13, No 3 (2021)
- Vol 13, No 2 (2021)
- Vol 13, No 1 (2021)
- 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)
- Vol 7, No 2 (2015)
- Vol 7, No 1 (2015)
- 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)