Personalized adaptive learning using neural networks

Devendra Singh Chaplot, Eunhee Rhim, Jihie Kim

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

16 Scopus citations

Abstract

Adaptive learning is the core technology behind intelligent tutoring systems, which are responsible for estimating student knowledge and providing personalized instruction to students based on their skill level. In this paper, we present a new adaptive learning system architecture, which uses Artificial Neural Network to construct the Learner Model, which automatically models relationship between different concepts in the curriculum and beats Knowledge Tracing in predicting student performance. We also propose a novel method for selecting items of optimal difficulty, personalized to student's skill level and learning rate, which decreases their learning time by 26.5% as compared to standard pre-defined curriculum sequence item selection policy.

Original languageEnglish
Title of host publicationL@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale
PublisherAssociation for Computing Machinery, Inc
Pages165-168
Number of pages4
ISBN (Electronic)9781450337267
DOIs
StatePublished - 25 Apr 2016
Event3rd Annual ACM Conference on Learning at Scale, L@S 2016 - Edinburgh, United Kingdom
Duration: 25 Apr 201626 Apr 2016

Publication series

NameL@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale

Conference

Conference3rd Annual ACM Conference on Learning at Scale, L@S 2016
Country/TerritoryUnited Kingdom
CityEdinburgh
Period25/04/1626/04/16

Keywords

  • Adaptive learning
  • Instructional model
  • Learner Model
  • Neural networks
  • Personalized item selection
  • Student model

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