About Us

Welcome to the IEEE Task Force on Educational Data Mining, affiliated with the Data Mining and Big Data Analytics Technical Committee of the IEEE Computational Intelligence Society.

This EDM task force promotes an emerging research area, namely Educational Data Mining, which applies Data Mining (DM) and Machine Learning on longitudinal educational data to investigate scientific questions within teaching and learning. The most significant difference between EDM and other methods from the broader DM literature is the explicit exploitation of the multiple levels of meaningful hierarchy and progression in educational processes and interactions. Educational data is fine grained and longitudinal, recorded from "click stream" student interaction and system responses in online courses, online assessment, intelligent tutoring systems, virtual labs, simulations and other forms of educational technology.

The TFEDM promotes the research and development on student analytics for better student care, teaching and learning performance, early intervention of risks and issues in teaching and learning progression.

IEEE Task Force on Educational Data Mining

Chair Gang Li, Deakin University, Australia
Vice Chair Ly Tran, Deakin University, Australia
Christos Douligeris, University of Piraeus, Greece
Aswani Kumar Cherukuri, Vellore Institute of Technology, Vellore, India
Xiaoliang Fan, Xiamen University, China
Former Chair Guandong Xu, University of Technology Sydney, Australia
Secretary Ziwei Hou, Deakin University, Australia



More information will come soon.