Adaptive Curriculum Sequencing and Education Management System via Group-Theoretic Particle Swarm Optimization
The Curriculum Sequencing (CS) problem is a challenging task to tackle in the field of online teaching and learning system development. The current methods of education management might still possess certain drawbacks that would cause ineffectiveness and incompatibility of the whole system. A soluti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/11/1/34 |
| _version_ | 1797436733569105920 |
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| author | Xiaojing Sheng Kun Lan Xiaoliang Jiang Jie Yang |
| author_facet | Xiaojing Sheng Kun Lan Xiaoliang Jiang Jie Yang |
| author_sort | Xiaojing Sheng |
| collection | DOAJ |
| description | The Curriculum Sequencing (CS) problem is a challenging task to tackle in the field of online teaching and learning system development. The current methods of education management might still possess certain drawbacks that would cause ineffectiveness and incompatibility of the whole system. A solution for achieving better user satisfaction would be to treat users individually and to offer educational materials in a customized way. Adaptive Curriculum Sequencing (ACS) plays an important role in education management system, for it helps finding the optimal sequence of a curriculum among various possible solutions, which is a typical NP-hard combinatorial optimization problem. Therefore, this paper proposes a novel metaheuristic algorithm named Group-Theoretic Particle Swarm Optimization (GT-PSO) to tackle the ACS problem. GT-PSO would rebuild the search paradigm adaptively based on the solid mathematical foundation of symmetric group through encoding the solution candidates, decomposing the search space, guiding neighborhood movements, and updating the swarm topology. The objective function is the learning goal, with additional intrinsic and extrinsic information from those users. Experimental results show that GT-PSO has outperformed most other methods in real-life scenarios, and the insights provided by our proposed method further indicate the theoretical and practical value of an effective and robust education management system. |
| first_indexed | 2024-03-09T11:06:49Z |
| format | Article |
| id | doaj.art-25705c3a89f14fe8af2e64bc51106e06 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-09T11:06:49Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-25705c3a89f14fe8af2e64bc51106e062023-12-01T00:54:42ZengMDPI AGSystems2079-89542023-01-011113410.3390/systems11010034Adaptive Curriculum Sequencing and Education Management System via Group-Theoretic Particle Swarm OptimizationXiaojing Sheng0Kun Lan1Xiaoliang Jiang2Jie Yang3College of Teacher Education, Quzhou University, Quzhou 324099, ChinaCollege of Mechanical Engineering, Quzhou University, Quzhou 324099, ChinaCollege of Mechanical Engineering, Quzhou University, Quzhou 324099, ChinaCollege of Artificial Intelligence, Chongqing Industry and Trade Polytechnic, Chongqing 408099, ChinaThe Curriculum Sequencing (CS) problem is a challenging task to tackle in the field of online teaching and learning system development. The current methods of education management might still possess certain drawbacks that would cause ineffectiveness and incompatibility of the whole system. A solution for achieving better user satisfaction would be to treat users individually and to offer educational materials in a customized way. Adaptive Curriculum Sequencing (ACS) plays an important role in education management system, for it helps finding the optimal sequence of a curriculum among various possible solutions, which is a typical NP-hard combinatorial optimization problem. Therefore, this paper proposes a novel metaheuristic algorithm named Group-Theoretic Particle Swarm Optimization (GT-PSO) to tackle the ACS problem. GT-PSO would rebuild the search paradigm adaptively based on the solid mathematical foundation of symmetric group through encoding the solution candidates, decomposing the search space, guiding neighborhood movements, and updating the swarm topology. The objective function is the learning goal, with additional intrinsic and extrinsic information from those users. Experimental results show that GT-PSO has outperformed most other methods in real-life scenarios, and the insights provided by our proposed method further indicate the theoretical and practical value of an effective and robust education management system.https://www.mdpi.com/2079-8954/11/1/34curriculum sequencingadaptive educationintelligent management systemgroup theorymetaheuristiccombinatorial optimization |
| spellingShingle | Xiaojing Sheng Kun Lan Xiaoliang Jiang Jie Yang Adaptive Curriculum Sequencing and Education Management System via Group-Theoretic Particle Swarm Optimization Systems curriculum sequencing adaptive education intelligent management system group theory metaheuristic combinatorial optimization |
| title | Adaptive Curriculum Sequencing and Education Management System via Group-Theoretic Particle Swarm Optimization |
| title_full | Adaptive Curriculum Sequencing and Education Management System via Group-Theoretic Particle Swarm Optimization |
| title_fullStr | Adaptive Curriculum Sequencing and Education Management System via Group-Theoretic Particle Swarm Optimization |
| title_full_unstemmed | Adaptive Curriculum Sequencing and Education Management System via Group-Theoretic Particle Swarm Optimization |
| title_short | Adaptive Curriculum Sequencing and Education Management System via Group-Theoretic Particle Swarm Optimization |
| title_sort | adaptive curriculum sequencing and education management system via group theoretic particle swarm optimization |
| topic | curriculum sequencing adaptive education intelligent management system group theory metaheuristic combinatorial optimization |
| url | https://www.mdpi.com/2079-8954/11/1/34 |
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