We host a 1-day workshop colocated with the ITS 2018 conference to discuss specific algorithmic and machine learning issues for optimizing human learning.


What should we learn next? In this current era where digital access to knowledge is cheap and user attention is expensive, a number of online applications have been developed for learning. These platforms collect a massive amount of data over various profiles, that can be used to improve learning experience: intelligent tutoring systems can infer what activities worked for different types of students in the past, and apply this knowledge to instruct new students. In order to learn effectively and efficiently, the experience should be adaptive: the sequence of activities should be tailored to the abilities and needs of each learner, in order to keep them stimulated and avoid boredom, confusion and dropout.

Educational research communities have proposed models that predict mistakes and dropout, in order to detect students that need further instruction. There is now a need to design online systems that continuously learn as data flows, and self-assess their strategies when interacting with new learners. These models have been already deployed in online commercial applications (ex. streaming, advertising, social networks) for optimizing interaction, click-through-rate, or profit. Can we use similar methods to enhance the performance of teaching in order to promote lifetime success?


Tuesday, June 12, 2018

Opening keynote

Masato Hagiwara

Important Dates

April 8, AoE

Deadline for paper submissions

April 16

Notification for acceptance

June 12

Optimizing Human Learning Workshop

Call for Papers

Short papers

Between 2 and 3 pages

Full papers

Between 4 and 6 pages

Submissions can be made through EasyChair and should follow the LNCS format.

Workshop Topics



Contact us: [email protected].

Workshop Chairs

Fabrice Popineau, CentraleSupélec & LRI, France
Michal Valko, Inria Lille, France
Jill-Jênn Vie, RIKEN AIP, Japan

Program Committee

Fabrice Popineau, CentraleSupélec & LRI, France
Arnaud Riegert, Didask, France
Julien Seznec, lelivrescolaire.fr, France
Michal Valko, Inria Lille, France
Jill-Jênn Vie, RIKEN AIP, Japan