A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system

被引:27
作者
Chang, Ting-Yi [1 ]
Ke, Yan-Ru [1 ]
机构
[1] Natl Changhua Univ Educ, Grad Inst E Learning, Changhua 500, Taiwan
关键词
Adaptive learning; Personalized learning; Artificial intelligence; Evolutionary algorithms; Genetic algorithm; Particle swarm optimization; TEST SHEETS;
D O I
10.1016/j.jnca.2012.04.002
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a personalized e-course composition based on a genetic algorithm with forcing legality (called GA*) in adaptive learning systems, which efficiently and accurately finds appropriate e-learning materials in the database for individual learners. The forcing legality operation 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. In serial experiments, the forcing legality operation is applied in Chu et al.'s the particle swarm optimization (called PSO*) and Dheeban et al.'s the improved particle swarm optimization (called RPSO*) to show the forcing legality can speed up the computational time and reduce the computational complexity of algorithm. Furthermore, GA* 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 PSO* and RPSO*. For the experiment increasing the number of students to 1200, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, stability, and execution time are above 96%, 79%, 90%, and 10%, respectively. For the experiment increasing the number of materials to 500 and the execution time set to the shortest execution time of RPSO*, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, and stability are above 97%, 51%, and 80%, respectively. Therefore, GAS is able to enhance the quality of personalized e-course compositions in adaptive learning environments. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:533 / 542
页数:10
相关论文
共 33 条
[1]  
[Anonymous], 2004, LEARN RES MET DAT IN
[2]   Web-based education for all: a tool for development adaptive courseware [J].
Brusilovsky, P ;
Eklund, J ;
Schwarz, E .
COMPUTER NETWORKS AND ISDN SYSTEMS, 1998, 30 (1-7) :291-300
[3]  
Brusilovsky P, 2002, COMMUN ACM, V45, P30
[4]  
Brusilovsky P., 1999, SPECIAL ISSUE INTELL, V4, P19
[5]   Simultaneously construct IRT-based parallel tests based on an adapted CLONALG algorithm [J].
Chang, Ting-Yi ;
Shiu, You-Fu .
APPLIED INTELLIGENCE, 2012, 36 (04) :979-994
[6]   Cooperative learning in E-learning: A peer assessment of student-centered using consistent fuzzy preference [J].
Chang, Ting-Yi ;
Chen, Yi-Ting .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (04) :8342-8349
[7]  
Chang TY, 2011, INT C E COMM E ADM E, P18
[8]   B2 model: A browsing behavior model based on High-Level Petri Nets to generate behavioral patterns for e-learning [J].
Chang, Yi-Chun ;
Huang, Ying-Chia ;
Chu, Chih-Ping .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (10) :12423-12440
[9]   A learning style classification mechanism for e-learning [J].
Chang, Yi-Chun ;
Kao, Wen-Yan ;
Chu, Chih-Ping ;
Chiu, Chiung-Hui .
COMPUTERS & EDUCATION, 2009, 53 (02) :273-285
[10]   "Games are made for fun": Lessons on the effects of concept maps in the classroom use of computer games [J].
Charsky, Dennis ;
Ressler, William .
COMPUTERS & EDUCATION, 2011, 56 (03) :604-615