Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1)
  • Open Access


    Generating Intelligent Remedial Materials with Genetic Algorithms and Concept Maps

    Che-Chern Lin*, Chien-Chun Pan

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1333-1349, 2022, DOI:10.32604/iasc.2022.025387

    Abstract This study proposes an intelligent remedial learning framework to improve students’ learning effectiveness. Basically, this framework combines a genetic algorithm with a concept map in order to select a set of remedial learning units according to students’ weaknesses of learning concepts. In the proposed algorithm, a concept map serves to represent the knowledge structure of learning concepts, and a genetic algorithm performs an iteratively evolutionary procedure in order to establish remedial learning materials based on students’ understanding of these learning concepts. This study also conducted simulations in order to validate the proposed framework using artificially More >

Displaying 1-10 on page 1 of 1. Per Page