UofM Professor Chairs International Conferences on Artificial Intelligence in Education and Educational Data Mining

July 21, 2017 - Dr. Xiangen Hu of the University of Memphis was the local arrangement chair for the 18th International Conference on Artificial Intelligence in Education (AIED) and the conference chair for the 10th International Educational Data Mining (EDM) conferences, which were both held in Wuhan, China, recently. Hu is a professor in the UofM Departments of Psychology, Computer Science and Electrical and Computer Engineering and the Institute for Intelligent Systems, and at Central China Normal University.

The AIED conference featured 36 revised full papers presented together with four keynotes, 37 poster presentations, four doctoral consortium papers, five industry papers, four workshop abstracts and two tutorial abstracts, which were selected from 159 submissions. The conference provided opportunities for the cross-fertilization of approaches, techniques and ideas from the many fields that comprise AIED, including computer science, cognitive and learning sciences, education, game design, psychology, sociology and linguistics, as well as many domain-specific areas.

Two of Hu's students, Jun Xie and Keith Shubeck, won the Best Poster award at the AIED meeting for their entry "Learning from Errors: Identifying Strategies in a Math Tutoring System."

The EDM conference is the leading international forum for high-quality research that leverages educational data, learning analytics and machine learning to answer research questions that shed light on the learning processes. Educational data may come from traces that students leave when they interact with learning management systems, interactive learning environments, intelligent tutoring systems, educational games or when they participate in other data-rich learning contexts.

The types of data range from raw log files to data captured by eye-tracking devices or other kinds of sensors. The methods used by EDM researchers include analytics, data science, data mining and machine learning, as well as social network analysis, graph mining, recommender systems and model building.

This year's conference received more than 120 submissions but accepted only 18 full papers and had 32 short papers, two invited speakers, 39 poster presentations, three demonstrations, six doctoral consortium presentations, three workshops and two tutorials, including "Why Data Standards are Critical for EDM and AIED" by Hu, Robby Robson and Avron Barr. This tutorial included discussions of the U.S. Department of Defense's Total Learning Architecture (TLA) and its efforts to incorporate standards.

Copies of the proceedings from both of these events are available at the following link: http://educationaldatamining.org/EDM2017/proceedings-full/.


Gabrielle Maxey