A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system
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In [33], genetic algorithm with forcing legality and particle swarm optimization were used to create personalized e-courses by selecting appropriate e-learning materials from a database.
“The algorithm is utilized for creating personalized e-courses in adaptive learning systems.The goal is to efficiently select appropriate e-learning materials from a database tailored to individual learners.”
From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [33]
Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192 · see citing works
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101 · see citing works
A discrete binary version of the particle swarm algorithm
Kennedy, J., & Eberhart, R. C. (2002) · 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation · doi:10.1109/icsmc.1997.637339 · Cited by 4,767 · see citing works
Personalized e-learning system using Item Response Theory
Chen, C., Lee, H.-M., & Chen, Y. (2004) · Computers & Education · doi:10.1016/j.compedu.2004.01.006 · Cited by 489 · see citing works
Web-based education for all: a tool for development adaptive courseware
Brusilovsky, P. L., Eklund, J., & Schwarz, E. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00082-8 · Cited by 473 · see citing works
Adaptive and Intelligent Technologies for Web-based Eduction.
Brusilovsky, P. L. (1999) · Künstliche Intell. · Cited by 450 · see citing works
From adaptive hypermedia to the adaptive web
Brusilovsky, P. L., & Maybury, M. T. (2002) · Communications of the ACM · doi:10.1145/506218.506239 · Cited by 415 · see citing works
A Computational Study of Search Strategies for Mixed Integer Programming
Linderoth, J., & Savelsbergh, M. W. P. (1999) · INFORMS journal on computing · doi:10.1287/ijoc.11.2.173 · Cited by 366 · see citing works
HUANG, M., HUANG, H., & CHEN, M. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.05.019 · Cited by 222 · see citing works
Charsky, D., & Ressler, W. H. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.10.001 · Cited by 213 · see citing works
Effects of teaching and learning styles on students’ reflection levels for ubiquitous learning
Hsieh, S.-W., Jang, Y.-R., Hwang, G., & Chen, N. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.01.004 · Cited by 201 · see citing works
A learning style classification mechanism for e-learning
Chang, Y.-C., Kao, W.-Y., Chu, C., & Chiu, C. (2009) · Computers & Education · doi:10.1016/j.compedu.2009.02.008 · Cited by 164 · see citing works
Wang, T. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.01.003 · Cited by 151 · see citing works
Hwang, G., Chu, H., Lin, Y.-S., & Tsai, C. (2011) · Computers & Education · doi:10.1016/j.compedu.2010.12.013 · Cited by 138 · see citing works
An adaptive testing system for supporting versatile educational assessment
Huang, Y., Lin, Y.-T., & Cheng, S.-C. (2008) · Computers & Education · doi:10.1016/j.compedu.2008.06.007 · Cited by 121 · see citing works
Profiling students' adaptation styles in Web-based learning
Lee, M.-G. (2001) · Computers & Education · doi:10.1016/s0360-1315(00)00046-4 · Cited by 116 · see citing works
Yin, P.-Y., Chang, K.-C., Hwang, G., Hwang, G., & Chan, Y. (2006) · Cited by 64 · see citing works
Chang, T., & Chen, Y. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.10.050 · Cited by 60 · see citing works
Self-citationPC2PSO: personalized e-course composition based on Particle Swarm Optimization
Chu, C., Chang, Y.-C., & Tsai, C. (2009) · Applied Intelligence · doi:10.1007/s10489-009-0186-7 · Cited by 54 · see citing works
QoL Guaranteed Adaptation and Personalization in E-Learning Systems
Liu, H.-I., & Yang, M.-N. (2005) · IEEE Transactions on Education · doi:10.1109/te.2005.858398 · Cited by 51 · see citing works
An enhanced genetic approach to optimizing auto-reply accuracy of an e-learning system
Hwang, G., Hwang, G., Yin, P.-Y., et al. (2007) · Computers & Education · doi:10.1016/j.compedu.2007.05.014 · Cited by 47 · see citing works
A Tabu Search Approach to Generating Test Sheets for Multiple Assessment Criteria
Hwang, G., Yin, P.-Y., & Yeh, S.-H. (2006) · IEEE Transactions on Education · doi:10.1109/te.2002.858405 · Cited by 47 · see citing works
Performance evaluation of an online argumentation learning assistance agent
Huang, C., Wang, Y., Huang, T.-H., Chen, Y.-C., Chen, H.-M., & Chang, S.-C. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.01.013 · Cited by 43 · see citing works
Dynamic question generation system for web-based testing using particle swarm optimization
Cheng, S.-C., Lin, Y.-T., & Huang, Y. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.09.064 · Cited by 42 · see citing works
Huang, T., Huang, Y., & Cheng, S.-C. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.09.039 · Cited by 37 · see citing works
Visualized online simple sequencing authoring tool for SCORM-compliant content package
Yang, J., Chiu, C., Tsai, C., et al. (2004) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2004.1357487 · Cited by 30 · see citing works
Simultaneously construct IRT-based parallel tests based on an adapted CLONALG algorithm
Chang, T., & Shiu, Y.-F. (2011) · Applied Intelligence · doi:10.1007/s10489-011-0308-x · Cited by 24 · see citing works
Self-citationA Hybrid PSO/GA Algorithm for Job Shop Scheduling Problem
Tang, J., Zhang, G., Lin, B., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13495-1_69 · Cited by 21 · see citing works
Test-Sheet Composition Using Immune Algorithm for E-Learning Application
Lee, C., Huang, C.-H., & Lin, C. (2007) · Springer eBooks · doi:10.1007/978-3-540-73325-6_82 · Cited by 20 · see citing works
Dheeban, S. G., Deepak, V., Dhamodharan, L., et al. (2010) · International Journal of Computer Applications · doi:10.5120/134-252 · Cited by 18 · see citing works
Chang, Y.-C., Huang, Y.-C., & Chu, C. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.04.044 · Cited by 14 · see citing works
A Novel Online Test-Sheet Composition Approach Using Genetic Algorithm
Wang, F., Wang, W., Pan, Q.-K., et al. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04843-2_1 · Cited by 4 · see citing works
A New Algorithm for Motion Control of Leader-Follower Formation of Mobile Autonomous Agents
Atta, D., & Subudhi, B. (2010) · International Journal of Computer Applications · doi:10.5120/527-689 · Cited by 3 · see citing works
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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980 · see citing works
Ouyang, F., Zheng, L., & Jiao, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10925-9 · Cited by 831 · see citing works
Birjali, M., Beni-Hssane, A., & Erritali, M. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.04.030 · Cited by 68 · see citing works
Gonaygunta, H., Nadella, G. S., Pramod Pawar, P., & Kumar, D. (2024) · 2024 Systems and Information Engineering Design Symposium (SIEDS) · doi:10.1109/sieds61124.2024.10534661 · Cited by 39 · see citing works
- Authors
- Chang, T.-Y., & Ke, Y.-R.
- Affiliations
- National Changhua University of Education (TW)
- Corresponding author
- Chang, Ting-Yi
- Venue
- Journal of Network and Computer Applications
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 36
- Issue
- 1
- Pages
- 533-542
- ISSN
- 1084-8045 (ISSN-L)
- OpenAlex
- W1983930826
- Cited by
- 40
- References
- 33
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 4 · 2022: 5 · 2021: 4 · 2020: 2 · 2019: 5 · 2018: 2 · 2017: 3 · 2016: 2 · 2015: 4 · 2013: 1
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Network and Computer Applications (publishedVersion)
- Funders
- National Science Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- adaptive learning systems, genetic algorithm, particle swarm optimization, improved particle swarm optimization, search space reduction
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