Prerequisite Structure Discovery in Intelligent Tutoring Systems

Louis Annabi, Sao Mai Nguyen

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

Abstract

This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. The KS represents the relations between different Knowledge Components (KCs), while KT predicts a learner's success based on her past history. The contribution of this research includes proposing a KT model that incorporates the KS as a learnable parameter, enabling the discovery of the underlying KS from learner trajectories. The quality of the uncovered KS is assessed by using it to recommend content and evaluating the recommendation algorithm with simulated students.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Development and Learning, ICDL 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages176-181
Number of pages6
ISBN (Electronic)9781665470759
DOIs
Publication statusPublished - 1 Jan 2023
Externally publishedYes
Event2023 IEEE International Conference on Development and Learning, ICDL 2023 - Macau, China
Duration: 9 Nov 202311 Nov 2023

Publication series

Name2023 IEEE International Conference on Development and Learning, ICDL 2023

Conference

Conference2023 IEEE International Conference on Development and Learning, ICDL 2023
Country/TerritoryChina
CityMacau
Period9/11/2311/11/23

Keywords

  • Intelligent Tutoring Systems
  • Knowledge Structure Discovery
  • Knowledge Tracing

Fingerprint

Dive into the research topics of 'Prerequisite Structure Discovery in Intelligent Tutoring Systems'. Together they form a unique fingerprint.

Cite this