Two efficient selection methods for high-dimensional CD-CAT utilizing max-marginals factor from MAP query and ensemble learning approach

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Fen Luo, Xiaoqing Wang, Yan Cai, Dongbo Tu
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引用次数: 0

Abstract

Computerized adaptive testing for cognitive diagnosis (CD-CAT) needs to be efficient and responsive in real time to meet practical applications' requirements. For high-dimensional data, the number of categories to be recognized in a test grows exponentially as the number of attributes increases, which can easily cause system reaction time to be too long such that it adversely affects the examinees and thus seriously impacts the measurement efficiency. More importantly, the long-time CPU operations and memory usage of item selection in CD-CAT due to intensive computation are impractical and cannot wholly meet practice needs. This paper proposed two new efficient selection strategies (HIA and CEL) for high-dimensional CD-CAT to address this issue by incorporating the max-marginals from the maximum a posteriori query and integrating the ensemble learning approach into the previous efficient selection methods, respectively. The performance of the proposed selection method was compared with the conventional selection method using simulated and real item pools. The results showed that the proposed methods could significantly improve the measurement efficiency with about 1/2–1/200 of the conventional methods' computation time while retaining similar measurement accuracy. With increasing number of attributes and size of the item pool, the computation time advantage of the proposed methods becomes more significant.

利用MAP查询中的最大边际因子和集成学习方法对高维CD-CAT进行高效选择
计算机自适应认知诊断测试(CD-CAT)需要高效、实时响应才能满足实际应用的要求。对于高维数据,一次测试中需要识别的类别数量随着属性数量的增加呈指数增长,这很容易导致系统反应时间过长,从而对考生产生不利影响,严重影响测量效率。更重要的是,CD-CAT中由于密集的计算导致的长时间的CPU操作和内存占用是不切实际的,不能完全满足实际需要。为了解决这一问题,本文提出了两种新的高维CD-CAT高效选择策略(HIA和CEL),分别将最大后验查询的最大边际和集成学习方法集成到以前的高效选择方法中。通过模拟和真实项目池,将所提出的选择方法与传统的选择方法进行了性能比较。结果表明,该方法在保持测量精度的前提下,可以显著提高测量效率,计算时间约为传统方法的1/2-1/200。随着属性数量和项目池规模的增加,所提方法的计算时间优势越来越明显。
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来源期刊
CiteScore
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
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