Topic: Educational Data Mining (EDM) and Learning Analytics (LA)
Educational data mining (EDM) and learning analytics (LA) are used to represent the application of data mining in higher education and other educational settings. They are fundamentally based on computational data analysis and can consecutively collect, process, report, and work on digital data to improve the educational process. EDM and LA are used to offer more personalized, adaptive, and interactive educational environments to enhance learning outcomes, teaching and learning effectiveness, and optimize institutional proficiency, contributing to both learning sciences and educational theory more broadly.
The topic seeks to connect learning analytics researchers, developers, and practitioners who share a common interest in computational approaches to educational data mining, to better understand and improve learning through the creation and implementation of new tools and techniques.
Specifically, it welcomes high-quality original work including but not limited to:
►Student modeling
►Social Network Analysis
►Analysis and Visualization of Data
►Prediction, Clustering, and Relationship Mining
►Developing concept maps
►Discovering or improving domain models
►Predicting students’ future learning behavior
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List of Publications
2025
Meriem Bentaleb, Hommane Boudine, Driss El Karfa, and Mohamed Tayebi
Transforming the Professional Development of Computer Science Teachers through Intelligent Technologies: Focus on Digital Competencies and Educational Outcomes
Zhandos Zulpykhar, Appak Yessirkep, and Sidi Fatimah
Leveraging Machine Learning to Forecast Candidate Selection Outcomes
Chaimae Ouhaddou, Asmaâ Retbi, and Samir Bennani
Enhancing the Preparation of Future Mathematics Teachers in a Digital Learning Environment
Alma E. Abylkassymova, Akmaral B. Duisebayeva, Yessenkeldy A. Tuyakov, Almagul K. Ardabayeva, and Bagdat M. Kossanov
Enhancing Student Performance Prediction in e-Learning Ecosystems Using Machine Learning Techniques
Fatima Ezzahraa EL Habti, Mustafa Hiri, Mohamed Chrayah, Abdelhamid Bouzidi, and Noura Aknin
2024
Bambang Riadi, Muhammad Fuad, Rangga Firdaus, and Dwi Yulianti
Understanding Student Performance in Foundation Year: Insights from Logistic Regression, Naïve Bayes, and Random Forest Models
Abdallah Bashir Musa
Predicting the Professional Field of Students Using Data Mining: A Case Study of an Autonomous University in Thailand
Phichayasini Kitwatthanathawon
Advancing Models Portability of Students’ Failure Prediction Using Ontology Modeling Approach
Mohamed Daoudi, El Miloud Smaili, Ilyas Alloug, Ilham Oumaira, and Moulay El Hassan Charaf
Optimizing Dropout Prediction in University Using Oversampling Techniques for Imbalanced Datasets
Analysis of Decision Support System for Character Assessment of Elementary School Students to Improve Teacher Assessment
Machine Learning Based Developmental Capability Prediction: A Diagnosis to the Learning Capacity Disorder for Specially-Abled Children
Use of the Naive Bayes Classifier Algorithm in Machine Learning for Student Performance Prediction