Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses
The increasing reliance on online examination systems in tax law education necessitates the development of intelligent frameworks that ensure fairness, syllabus coverage, and difficulty balance. Traditional exam construction methods, such as manual question selection and heuristic approaches, often...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11058934/ |
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| description | The increasing reliance on online examination systems in tax law education necessitates the development of intelligent frameworks that ensure fairness, syllabus coverage, and difficulty balance. Traditional exam construction methods, such as manual question selection and heuristic approaches, often result in inconsistencies, bias, and suboptimal difficulty distribution. This study proposes a simulated annealing (SA)-based optimization model for dynamically generating balanced online examinations in tax law courses. The SA algorithm efficiently selects exam questions while minimizing Exam Difficulty Variance (EDV), maximizing Syllabus Coverage Ratio (SCR), and reducing computational overhead. Experimental results demonstrate that SA achieves superior performance compared to genetic algorithms (GA), greedy approaches, ant colony optimization (ACO), and particle swarm optimization (PSO). Specifically, SA attained the lowest EDV (0.038), the highest SCR (94%), and the fastest execution time (ET) (1.5s), outperforming GA (5.2s) and ACO (3.8s). These findings highlight the effectiveness of SA in generating equitable and adaptive exams, providing a scalable and automated solution for tax law education. Future enhancements include integrating machine learning techniques to refine SA’s parameter tuning and further improving exam personalization. |
| format | Article |
| id | doaj-art-cf1025de0877458fb16afebe6a7c2c62 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-cf1025de0877458fb16afebe6a7c2c622025-08-20T03:50:45ZengIEEEIEEE Access2169-35362025-01-011311906311907610.1109/ACCESS.2025.358421411058934Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Coursesda Pan0https://orcid.org/0009-0008-1173-6335Jilin Business and Technology College, Changchun, Jilin, ChinaThe increasing reliance on online examination systems in tax law education necessitates the development of intelligent frameworks that ensure fairness, syllabus coverage, and difficulty balance. Traditional exam construction methods, such as manual question selection and heuristic approaches, often result in inconsistencies, bias, and suboptimal difficulty distribution. This study proposes a simulated annealing (SA)-based optimization model for dynamically generating balanced online examinations in tax law courses. The SA algorithm efficiently selects exam questions while minimizing Exam Difficulty Variance (EDV), maximizing Syllabus Coverage Ratio (SCR), and reducing computational overhead. Experimental results demonstrate that SA achieves superior performance compared to genetic algorithms (GA), greedy approaches, ant colony optimization (ACO), and particle swarm optimization (PSO). Specifically, SA attained the lowest EDV (0.038), the highest SCR (94%), and the fastest execution time (ET) (1.5s), outperforming GA (5.2s) and ACO (3.8s). These findings highlight the effectiveness of SA in generating equitable and adaptive exams, providing a scalable and automated solution for tax law education. Future enhancements include integrating machine learning techniques to refine SA’s parameter tuning and further improving exam personalization.https://ieeexplore.ieee.org/document/11058934/Simulated annealing (SA)online examination systemtax law educationheuristic optimizationexam question selectioncomputational efficiency |
| spellingShingle | da Pan Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses IEEE Access Simulated annealing (SA) online examination system tax law education heuristic optimization exam question selection computational efficiency |
| title | Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses |
| title_full | Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses |
| title_fullStr | Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses |
| title_full_unstemmed | Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses |
| title_short | Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses |
| title_sort | application of simulated annealing algorithm in the construction of online examination system for tax law courses |
| topic | Simulated annealing (SA) online examination system tax law education heuristic optimization exam question selection computational efficiency |
| url | https://ieeexplore.ieee.org/document/11058934/ |
| work_keys_str_mv | AT dapan applicationofsimulatedannealingalgorithmintheconstructionofonlineexaminationsystemfortaxlawcourses |