Combining difficulty ranking with multi-armed bandits to sequence educational content

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We address 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 multi-armed 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 and updates it in real-time according to students’ progress. 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 initial results.

Original languageAmerican English
Title of host publicationArtificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
EditorsRose Luckin, Kaska Porayska-Pomsta, Benedict du Boulay, Manolis Mavrikis, Carolyn Penstein Rosé, Bruce McLaren, Roberto Martinez-Maldonado, H. Ulrich Hoppe
PublisherSpringer Verlag
Pages317-321
Number of pages5
ISBN (Print)9783319938455
DOIs
StatePublished - 1 Jan 2018
Event19th International Conference on Artificial Intelligence in Education, AIED 2018 - London, United Kingdom
Duration: 27 Jun 201830 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10948 LNAI

Conference

Conference19th International Conference on Artificial Intelligence in Education, AIED 2018
Country/TerritoryUnited Kingdom
CityLondon
Period27/06/1830/06/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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