Educational Data Mining Approaches for Predicting Student Performance and Retention
DETAILS
Call for Papers
Educational Data Mining Approaches for Predicting Student Performance and Retention
Journal: Africa Education Review
Publisher: Taylor & Francis Group
Manuscript Submission Deadline: 11 November 2026
Africa Education Review invites high-quality submissions for its Special Issue on Educational Data Mining Approaches for Predicting Student Performance and Retention.
About the Special Issue
Educational Data Mining (EDM) has become an essential tool for predicting student performance, identifying learners at risk, and improving student retention in educational institutions. By analyzing academic records, learning behaviors, engagement patterns, and digital learning data, EDM enables educators to implement timely interventions that enhance learning outcomes and institutional success.
This Special Issue seeks original research that explores innovative Educational Data Mining techniques for predicting student performance, improving retention, and supporting data-driven educational decision-making. Contributions highlighting Machine Learning, Deep Learning, Explainable AI (XAI), ensemble learning, and intelligent analytics for early warning systems, personalized learning, and curriculum enhancement are particularly encouraged.
Topics of Interest
Submissions may include, but are not limited to:
Educational Data Mining for student performance prediction
Student retention analytics and early warning systems
Machine Learning, Deep Learning, and Explainable AI (XAI) in education
Classification, regression, clustering, and ensemble learning models
Learning analytics and behavioral pattern analysis
Academic performance prediction using educational datasets
Student engagement analysis and intervention strategies
Personalized learning and adaptive educational systems
Curriculum improvement through educational data analytics
Data-driven educational decision-making and institutional planning
Preferred Article Types
The journal welcomes:
Original Research Articles
Review Articles
Empirical Studies
Case Studies
Submission Information
Submission Deadline: 11 November 2026
Manuscripts should align with the scope of the Special Issue and follow the journal's official author guidelines.
All submissions will undergo the journal's standard peer-review process.
Guest Editors
Dr. Adebukola Onashoga, Federal University of Agriculture, Nigeria
Dr. Adebayo Abayomi-Alli, INESC TEC, University of Porto, Portugal
Dr. Vivian Ogochukwu Nwaocha, National Open University of Nigeria
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