TY  - EJOU
AU  - Lin, Che-Chern 
AU  - Pan, Chien-Chun 

TI  - Generating Intelligent Remedial Materials with Genetic Algorithms and Concept Maps
T2  - Intelligent Automation \& Soft Computing

PY  - 2022
VL  - 34
IS  - 2
SN  - 2326-005X

AB  - 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 issues regarding generalizing the special case of the proposed framework are further discussed. The proposed algorithm can be generally-employed in e-learning, providing a framework for generating remedial learning materials for all kinds of learning fields.
KW  - Genetic algorithms; concept maps; remedial learning materials; intelligent learning systems

DO  - 10.32604/iasc.2022.025387