基于能力的多元论知识结构理论

IF 2.2 4区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Mathematical Psychology Pub Date : 2023-08-01 DOI:10.1016/j.jmp.2023.102781
Luca Stefanutti , Andrea Spoto , Pasquale Anselmi , Debora de Chiusole
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引用次数: 0

摘要

本文提出了属性图的公理化理论基础,这是技能图在多元知识结构理论中的延伸。通过属性映射(attribute map)和项目响应函数(item - response function)两种映射关系,建立了可用属性与可观察项目响应之间的确定性关系。属性映射为每个项目-响应对分配一组属性,这些属性有助于观察对项目的特定响应。项目响应函数为每一组属性分配一组项目响应,根据属性映射,这些响应可以通过这些属性获得。所提出的方法被证明是相当通用的,并且能够处理在实践中可能遇到的大量多义项。提供的例子涵盖了对李克特量表的反应分析,给予部分学分的反应,以及对误解的调查。
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Towards a competence-based polytomous knowledge structure theory

The present article lays out the foundations of an axiomatic theory of attribute maps, an extension of skill maps to polytomous knowledge structure theory. A deterministic relationship between the available attributes and the observable item responses is established by means of two mappings denoted attribute map and item–response function. The attribute map assigns to each item–response pair the set of attributes that are instrumental for observing that particular response to the item. The item–response function assigns to each set of attributes the set of item responses that, according to the attribute map, can be obtained with those attributes. The proposed approach is shown to be rather general and capable of handling a multitude of polytomous items that can be encountered in practice. Examples are provided that cover the analysis of responses on Likert scales, responses awarded partial credits, and the investigation of misconceptions.

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来源期刊
Journal of Mathematical Psychology
Journal of Mathematical Psychology 医学-数学跨学科应用
CiteScore
3.70
自引率
11.10%
发文量
37
审稿时长
20.2 weeks
期刊介绍: The Journal of Mathematical Psychology includes articles, monographs and reviews, notes and commentaries, and book reviews in all areas of mathematical psychology. Empirical and theoretical contributions are equally welcome. Areas of special interest include, but are not limited to, fundamental measurement and psychological process models, such as those based upon neural network or information processing concepts. A partial listing of substantive areas covered include sensation and perception, psychophysics, learning and memory, problem solving, judgment and decision-making, and motivation. The Journal of Mathematical Psychology is affiliated with the Society for Mathematical Psychology. Research Areas include: • Models for sensation and perception, learning, memory and thinking • Fundamental measurement and scaling • Decision making • Neural modeling and networks • Psychophysics and signal detection • Neuropsychological theories • Psycholinguistics • Motivational dynamics • Animal behavior • Psychometric theory
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