对多约束认知诊断测试构建的美美学殖民地优化
Xi Cao1, Yong-Feng Ge2, Kate Wang3
1Department of Computer Science and Information Technology, La Trobe University, Melbourne, Victoria 3086 Australia.
Health information science and systems
|November 19, 2024
概括
这项研究引入了一种新的模拟群优化 (MACO) 算法,用于创建满足多个约束的认知诊断测试 (CDT). MACO提高了测试质量和诊断准确性,特别是在具有挑战性的项目库中.
科学领域:
- 心理测量 心理测量 心理测量
- 人工智能的人工智能
- 教育测量教育的测量
背景情况:
- 认知诊断测试 (CDT) 提供了对测试者掌握情况的详细见解.
- 传统的CDT构建算法面临着多个同时限制的局限性.
研究的目的:
- 开发一种元启发式算法,用于构建高质量的CDT,有效地处理多个约束.
- 通过解决更广泛的测试施工挑战来改进现有方法.
主要方法:
- 为了CDT的构建,开发了一种仿真殖民地优化 (MACO) 算法.
- MACO将项目质量和约束坚持整合到启发式信息中,使用激素轨迹和本地搜索策略.
- 测试组件根据诊断指数和约束满意度进行了评估.
主要成果:
- 元启发式算法在管理CDT的多个约束方面表现出强大的能力.
- MACO的表现优于标准的群优化,表现出更快的趋同和更高的表现,特别是在混合和低歧视项目银行.
- 模拟实验证实了MACO在各种条件下的有效性.
结论:
- MACO为多限制的CDT构建提供了有效的解决方案,特别是用于更短的测试和特定的项目库类型.
- 优化方法的最佳选择可能因项目库特征和测试长度而异.
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