Assessment-based correct rates in learning spaces

IF 1.5 4区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Mathematical Psychology Pub Date : 2023-02-01 DOI:10.1016/j.jmp.2022.102740
Jürgen Heller
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引用次数: 1

Abstract

The basic local independence model (BLIM) is the standard probabilistic model in knowledge structure theory. It assumes that the probability of a correct response to a problem is constant for all individuals that master the problem, and accordingly, for all individuals that do not master it, irrespective of the mastering of other problems. Recently published data on the problem correct rate as inferred from a response-based assessment of the mastering of the problem seem to contradict this assumption. The analysis presented in this paper, however, reveals that deviations from constancy in the observed direction are to be expected under the BLIM. They are mainly due to the inaccuracy inherent in any response-based assessment. The implications of these results for the empirical validation of the BLIM are discussed.

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学习空间中基于评估的正确率
基本局部独立模型(BLIM)是知识结构理论中的标准概率模型。它假设,对一个问题做出正确反应的概率对于所有掌握了这个问题的人来说是恒定的,相应地,对于所有没有掌握这个问题的人来说也是如此,不管他们是否掌握了其他问题。最近公布的数据显示,从对问题掌握程度的基于回答的评估中推断出的问题正确率似乎与这一假设相矛盾。然而,本文的分析表明,在blm下,在观测方向上偏离恒定是可以预期的。它们主要是由于任何基于反应的评估固有的不准确性。讨论了这些结果对blm实证验证的意义。
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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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