W25, August 11, 2019

AIMA4EDU Workshop in IJCAI 2019

AI-based Multimodal Analytics
for Understanding Human Learning
in Real-world Educational Contexts

IJCAI 2019 Macao WorkShop (W25)

We invite submissions in the following categories:

Full Paper

Short Paper

Demo/Analytics Paper

4-6 pages

Full papers should present original research work.

2-3 pages

Short papers can be position or early results papers.

2-3 pages

Demo papers should describe a demonstration.

till AIMA4EDU Workshop inaugurates


Human learning is a complex interactive and iterative process that takes place at a very finegrained level. However, our ability to understand this fascinating latent learning process is often limited by what we can perceive and how we can measure. Recent advances of sensing technology and accompanying techniques for processing multimodal data, which manifest the
psychological as well physiological processes during the human learning process, give us a new opportunity to look at this classical problem with a new pair of lens. The emerging new type of data includes, but not limited to, student’s physiological signals such as EKG or EEG waveforms, students’ speech, facial expressions and postures, within the context of particular learning activities. We are particularly interested in those data gathered from the real-world educational activities versus those from the controlled lab environment.


AGENDA (August 11) /

Welcome from Organizers


Invited Talk - Zachary Pardos (UC Berkeley)
Knowledge Estimation from Clickstream and Beyond


Morning Tea


Best Paper


Lunch Break


Invited Talk - Frank Andrasik (The University of Memphis)
Neurofeedback for Autism Spectrum Disorders: Methodological Musings


Invited Talk - Richard Tong (Squirrel AI Learning)


Afternoon Tea


Best Paper Award


Best Student Paper


Closing Speech



Organizing Committee


University of Technology Sydney


Carnegie Mellon University


SRI International




(In Alphabetical Order)

Jing Jiang (University of Technology Sydney)
KP Thai (Squirrel AI Learning)
Kang Lee (University of Toronto)
Lina Yao (University of New South Wales)
Lujie Karen Chen (Carnegie Mellon University)
Shirui Pan (Monash University)
Sen Wang (University of Queensland)
YK Wang (University of Technology Sydney)