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 generated data sets, and problematic… More >
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