A personalized self-learning system based on knowledge graph and differential evolution algorithm

Feng Wang, Lingling Zhang, Xingchen Chen, Ziming Wang, Xin Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Discovering the most adaptive learning path and content is an urgent issue for nowadays e-learning environment, for achieving learning goals efficiently and effectively. The main challenge of building this system is to provide appropriate educational guide and resource for different learners with respective interests and knowledge base. In order to reduce people's cognitive overload and fulfill their self-learning requirements, this article proposes a framework for a self-learning system. The system is design to be closed and updated automatically, in which learning path is discovered based on differential evolution (DE) algorithm and knowledge graph. The output of the system includes: (1) the personalized learning path adapted to learner's specific needs; (2) learning resource recommendation matching the learning path; (3) test results of learners' learning effect after following the learning path and resources recommendation; (4) revised learning path and resources recommendation according to learner's evaluation. Experimental results show that the system based on DE algorithm and disciplinary knowledge graph is feasible in optimal learning path discovery and further learning resources recommendation.

Original languageEnglish
Article numbere6190
JournalConcurrency and Computation: Practice and Experience
Volume34
Issue number8
DOIs
Publication statusPublished - 10 Apr 2022

Keywords

  • differential evolution algorithm
  • knowledge graph
  • learning path
  • self-learning system

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics

Cite this