We host a fully virtual half-day workshop colocated with the AIED 2020 conference to discuss specific algorithmic and machine learning issues for optimizing human learning.

Motivation

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):

Program

The conference starts on Monday July 6 at 1 PM UTC+1 (see your local time here).

Pacific Eastern Morocco Central Europe Japan  
UTC-7 UTC-4 UTC+1 UTC+2 UTC+9  
5:00 AM 8:00 AM 1:00 PM 2:00 PM 9:00 PM Welcome and Introduction
5:30 AM 8:30 AM 1:30 PM 2:30 PM 9:30 PM Invited talk: Teaching Categories to Human Learners with Visual Explanations [intro video] [slides]
Oisin Mac Aodha, University of Edinburgh
6:00 AM 9:00 AM 2:00 PM 3:00 PM 10:00 PM Non-compensatory Knowledge Tracing with Local Variational Approximation [slides]
Hiroshi Tamano, NEC & The Graduate University for Advanced Studies, Japan
          15 min break
6:45 AM 9:45 AM 2:45 PM 3:45 PM 10:45 PM Invited talk: Teaching Multiple Concepts to a Forgetful Learner
Yuxin Chen, University of Chicago [slides]
7:15 AM 10:15 AM 3:15 PM 4:15 PM 11:15 PM Adaptive Quiz Generation Using Thompson Sampling
Fuhua Lin, Athabasca University, Canada [slides]
          15 min break
8:00 AM 11:00 AM 4:00 PM 5:00 PM 12:00 AM Open Discussion
8:30 AM 11:30 AM 4:30 PM 5:30 PM 12:30 AM End

Registration

The Optimizing Human Learning workshop will be held online via BigBlueButton.

Register now by selecting Workshop Four.

Price is 11.25 GBP for students (tax included), 21.50 GBP for others. It includes a small registration fee.

Important Dates

June 20 – AoE

Deadline for paper submissions - CFP on EasyChair

June 27

Notification for acceptance

July 5

Camera-ready version due

July 6 1pm-5pm (GMT+1) (see your local time here)

Optimizing Human Learning Workshop

Call for Papers

Short papers

Between 2 and 3 pages

Full papers

Between 4 and 6 pages

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

Workshop Topics

Datasets

Organizers

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

Workshop Chairs

Jill-Jênn Vie, Inria Lille, France
Fabrice Popineau, CentraleSupélec & LRI, France
Hisashi Kashima, Kyoto University, Japan
Benoît Choffin, CentraleSupélec & LRI, France