Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content

Avi Segal, Yossi Ben David, Joseph Jay Williams, Yakov Gal, Yaar Shalom

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As e-learning systems become more prevalent, there is a growing need for them to accommodate individual differences between students. This paper addresses the problem of how to personalize educational content to students in order to maximize their learning gains over time. We present a new computational approach to this problem called MAPLE (Multi-Armed Bandits based Personalization for Learning Environments) that combines difficulty ranking with multiarmed bandits. Given a set of target questions MAPLE estimates the expected learning gains for each question and uses an exploration-exploitation strategy to choose the next question to pose to the student. It maintains a personalized ranking over the difficulties of question in the target set which is used in two ways: First, to obtain initial estimates over the learning gains for the set of questions. Second, to update the estimates over time based on the students responses. We show in simulations that MAPLE was able to improve students’ learning gains compared to approaches that sequence questions in increasing level of difficulty, or rely on content experts. When implemented in a live e-learning system in the wild, MAPLE showed promising results. This work demonstrates the efficacy of using stochastic approaches to the sequencing problem when augmented with information about question difficulty.
Original languageEnglish
Title of host publication19th International Conference on Artificial Intelligence in Education 2018
Place of PublicationLondon, UK
PublisherSpringer, Cham
Pages317-321
Number of pages5
ISBN (Electronic)978-3-319-93846-2
ISBN (Print)978-3-319-93845-5
DOIs
Publication statusPublished - 27 Jun 2018
Event19th International Conference on Artificial Intelligence in Education - London, United Kingdom
Duration: 27 Jun 201830 Jun 2018
https://aied2018.utscic.edu.au/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
Volume10948
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Artificial Intelligence in Education
Abbreviated titleAIED 2018
CountryUnited Kingdom
CityLondon
Period27/06/1830/06/18
Internet address

Fingerprint

Dive into the research topics of 'Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content'. Together they form a unique fingerprint.

Cite this