A Personalized e-Course Composition based on Genetic Algorithm with Forcing Legality in an Adaptive Learning System

• Main Authors: Yan-Ru Ke, 柯彥如
• Other Authors: Ting-Yi Chang
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/28504335775415228543

碩士 === 國立彰化師範大學 === 數位學習研究所 === 99 === This paper proposes a personalized e-course composition based on a genetic algorithm with forcing legality in adaptive learning systems, which efficiently and accurately finds appropriate e-learning materials in the database for individual learners. The algorithm not only reduces the search space size and increases search efficiency but also is more explicit in finding the best e-course composition in a legal solution space. The serial experiments indicate that the genetic algorithm with forcing legality regardless of the number of students or the number of materials in the database, to compose a personalized e-course within a limited time is much more efficient and accurate than the method based on the particle swarm optimization proposed by Chu et al. and the improved particle swarm optimization proposed by Dheeban et al. Therefore, the genetic algorithm with forcing legality is able to enhance the quality of personalized e-course compositions in adaptive learning environments.