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 | |
Yuncheng Jiang, South China Normal University, China | |
Secretary | Ziwei Hou, Deakin University, Australia |
Chairs
Gang Li
Chair
Deakin University, Australia
Ly Tran
Vice Chair
Deakin University, Australia
Christos Douligeris
Vice Chair
University of Piraeus, Greece
Aswani Kumar Cherukuri
Vice Chair
Vellore Institute of Technology, Vellore, India
Xiaoliang Fan
Vice Chair
Xiamen University, China
Yuncheng Jiang
Vice Chair
South China Normal University, China