Cao Xi, Ge Yong-Feng, Wang Kate, Lin Ying
Department of Computer Science and Information Technology, La Trobe University, Melbourne, Victoria 3086 Australia.
Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Victoria 3011 Australia.
Health Inf Sci Syst. 2024 Nov 16;12(1):56. doi: 10.1007/s13755-024-00314-6. eCollection 2024 Dec.
Cognitive diagnostic tests (CDTs) assess cognitive skills at a more granular level, providing detailed insights into the mastery profile of test-takers. Traditional algorithms for constructing CDTs have partially addressed these challenges, focusing on a limited number of constraints. This paper intends to utilize a meta-heuristic algorithm to produce high-quality tests and handle more constraints simultaneously.
This paper presents a memetic ant colony optimization (MACO) algorithm for constructing CDTs while considering multiple constraints. The MACO method utilizes pheromone trails to represent successful test constructions from the past. Additionally, it innovatively integrates item quality and constraint adherence into heuristic information to manage multiple constraints simultaneously. The method evaluates the assembled tests based on the diagnosis index and constraint satisfaction. Another innovation of MACO is the incorporation of a local search strategy to further enhance diagnostic accuracy by partially optimizing item selection. The optimal local search parameter settings are explored through a parameter investigation. A series of simulation experiments validate the effectiveness of MACO under various conditions.
The results demonstrate the great ability of meta-heuristic algorithms to handle multiple constraints and achieve high statistical performance. MACO exhibited superior performance in generating high-quality CDTs while meeting multiple constraints, particularly for mixed and low discrimination item banks. It achieved faster convergence than the ant colony optimization in most scenarios.
MACO provides an effective solution for multi-constrained CDT construction, especially for shorter tests and item banks with mixed or lower discrimination. The experimental results also suggest that the suitability of different optimization approaches may depend on specific test conditions, such as the characteristics of the item bank and the length of the test.
认知诊断测试（CDT）能在更细化的层面评估认知技能，深入洞察考生的掌握情况。传统的CDT构建算法部分解决了这些挑战，但仅关注有限的一些约束条件。本文旨在利用一种元启发式算法来生成高质量测试并同时处理更多约束条件。
本文提出一种用于构建CDT的混合蚁群优化（MACO）算法，该算法考虑了多个约束条件。MACO方法利用信息素轨迹来表示过去成功的测试构建。此外，它创新性地将项目质量和对约束条件的遵守整合到启发式信息中，以同时管理多个约束条件。该方法基于诊断指标和约束条件满意度来评估组合后的测试。MACO的另一项创新是纳入了局部搜索策略，通过部分优化项目选择来进一步提高诊断准确性。通过参数研究探索了最优的局部搜索参数设置。一系列模拟实验验证了MACO在各种条件下的有效性。
结果表明元启发式算法在处理多个约束条件和实现高统计性能方面具有强大能力。MACO在生成满足多个约束条件的高质量CDT方面表现出色，特别是对于混合和低区分度的项目库。在大多数情况下，它比蚁群优化算法收敛得更快。
MACO为多约束CDT构建提供了一种有效解决方案，尤其适用于较短的测试以及具有混合或较低区分度的项目库。实验结果还表明，不同优化方法的适用性可能取决于特定的测试条件，如项目库的特征和测试长度。
