Home > Topics > Topic: Educational Data Mining (EDM) and Learning Analytics >

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

Make a new submission to the Topic: Educational Data Mining (EDM) and Learning Analytics (LA) section.

List of Publications


General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Jon-Chao Hong
  • Managing Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2023: 2.8), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net