TY - JOUR
T1 - A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system
AU - Chang, Ting Yi
AU - Ke, Yan Ru
N1 - Funding Information:
We would like to thank the referees for many valuable comments and suggestions which have resulted in several improvements of the presentation of the paper. This research was partially supported by the National Science Council, Taiwan, ROC , under contract no.: NSC100-2622-E-018-004-CC3 and NSC100-2622-E-241-006-CC3 .
PY - 2013/1
Y1 - 2013/1
N2 - 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, GA is able to enhance the quality of personalized e-course compositions in adaptive learning environments.
AB - 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, GA is able to enhance the quality of personalized e-course compositions in adaptive learning environments.
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U2 - 10.1016/j.jnca.2012.04.002
DO - 10.1016/j.jnca.2012.04.002
M3 - Article
AN - SCOPUS:84870682610
VL - 36
SP - 533
EP - 542
JO - Journal of Network and Computer Applications
JF - Journal of Network and Computer Applications
SN - 1084-8045
IS - 1
ER -