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 >

K. Abhirami^1,*, M. K. Kavitha Devi²

Computer Systems Science and Engineering, Vol.40, No.3, pp. 1127-1144, 2022, DOI:10.32604/csse.2022.020013

Abstract With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be… More >