We host a 1-day workshop colocated with the ITS 2019 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. In the context of reinforcement learning, we want to learn a policy to administer exercises or resources to individual students.

Educational research communities have proposed models that predict mistakes and dropout, in order to detect students that need further instruction. Such models are usually calibrated on data collected in an offline scenario, and may not generalize well to new students. 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? When optimizing human learning, which metrics should be optimized? Learner progress? Learner retention? User addiction? The diversity or coverage of the proposed activities? What the issues inherent to adapting the learning process in online settings, in terms of privacy, fairness (disparate impact, inadvertent discrimination), and robustness to adversaries trying to game the system?

Student modeling for optimizing human learning is a rich and complex task that gathers methods from machine learning, cognitive science, educational data mining and psychometrics. This workshop welcomes researchers and practitioners in the following topics (this list is not exhaustive):


Time Event
9:30 AM Welcome and Introduction
9:45 AM Tutorial: Knowledge Tracing [code]
Jill-Jênn Vie (RIKEN AIP, Kyoto University & New York University)
10:30 AM Coffee Break
11:00 AM An Exploration of Disciplinary Literacy in Learners’ Short Answers
Jean-Philippe Corbeil, Amal Zouaq, and Michel Gagnon
11:30 AM Open Discussion
12:15 AM End of workshop


The Optimizing Human Learning workshop will be held in:

The University of West Indies
Mona, Kingston, Jamaica.

Register now!

Important Dates

April 16 – AoE

Deadline for paper submissions

April 23

Notification for acceptance

May 6

Camera-ready version due

June 4

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



To contact us, join our Google group: optimizing-human-learning

Workshop Chairs

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

Program Committee

François Bouchet, LIP6/Sorbonne Université, France Benoît Choffin, Didask, France
Fabrice Popineau, CentraleSupélec & LRI, France
Julien Seznec, lelivrescolaire.fr, France
Michal Valko, Inria Lille, France
Jill-Jênn Vie, RIKEN AIP, Japan