Update. The proceedings of WeASeL 2018 are available here, starting page 141.
About the next edition of our workshop, more information will be available in February 2019.
To stay updated, join our Google group: optimizing-human-learning!

We hosted 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?


Masato Hagiwara

Human Language

[html] [slides]

Carnegie Mellon University

Shayan Doroudi

Where's the Reward? A Review of Reinforcement Learning for Instructional Sequencing


Tuesday 12 June 2018

Proceedings are available on this page.

Time Event
8:45 AM Welcome and Introduction
9:00 AM Keynote: Optimizing Human Language Learning [html] [slides]
Masato Hagiwara, Duolingo
10:00 AM An Adaptive Tutor to Promote Learners’ Skills Acquisition during Procedural Learning [slides]
Joanna Taoum, Anaïs Raison, Elisabetta Bevacqua, Ronan Querrec
10:30 AM Coffee Break
11:00 AM SARLR: Self-adaptive Recommendation of Learning Resources [slides]
Liping Liu, Wenjun Wu, Jiankun Huang
11:30 AM Open Discussion
12:00 AM Lunch Break
1:30 PM Keynote: Where’s the Reward? A Review of Reinforcement Learning for Instructional Sequencing [slides]
Shayan Doroudi, Carnegie Mellon University
2:30 PM Optimizing Recommendation in Collaborative E-Learning by Exploring DBpedia and Association Rules [slides]
Samia Beldjoudi, Hassina Seridi
3:00 PM Coffee Break
3:30 PM Tutorial: Knowledge Tracing Machines: towards an unification of DKT, IRT & PFA [pdf] [code] [slides]
Jill-Jênn Vie, RIKEN AIP
4:30 PM Closing Discussion
5:00 PM End of Workshop


Optimizing Human Learning is denoted by W6 on the registration page.


The Optimizing Human Learning workshop was held at:

University du Québec à Montréal (UQAM)
Pavillon Sherbrooke
200 rue Sherbrooke Ouest
H2X 3P2

Important Dates

April 9 – AoE

Deadline for paper submissions

April 16

Notification for acceptance

May 7 – PST

End of early bird registration rates for ITS 2018

May 11 – PST

End of early bird registration rates for Optimizing Human Learning
Camera-ready version due

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].

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

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