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一项关于使用树形图和2×2表格进行统计推理的眼动追踪研究。

An Eye-Tracking Study of Statistical Reasoning With Tree Diagrams and 2 × 2 Tables.

作者信息

Bruckmaier Georg, Binder Karin, Krauss Stefan, Kufner Han-Min

机构信息

Department of Secondary Education, University of Education, University of Applied Sciences and Arts Northwestern Switzerland, Windisch, Switzerland.

Mathematics Education, Faculty of Mathematics, University of Regensburg, Regensburg, Germany.

出版信息

Front Psychol. 2019 May 15;10:632. doi: 10.3389/fpsyg.2019.00632. eCollection 2019.

Abstract

Changing the information format from probabilities into frequencies as well as employing appropriate visualizations such as tree diagrams or 2 × 2 tables are important tools that can facilitate people's statistical reasoning. Previous studies have shown that despite their widespread use in statistical textbooks, both of those visualization types are only of restricted help when they are provided with probabilities, but that they can foster insight when presented with frequencies instead. In the present study, we attempt to replicate this effect and also examine, by the method of eye tracking, probabilistic 2 × 2 tables and tree diagrams do not facilitate reasoning with regard to Bayesian inferences (i.e., determining what errors occur and whether they can be explained by scan paths), and the same visualizations are of great help to an individual when they are combined with frequencies. All ten inferences of = 24 participants were based solely on tree diagrams or 2 × 2 tables that presented either the famous "mammography context" or an "economics context" (without additional textual wording). We first asked participants for marginal, conjoint, and (non-inverted) conditional probabilities (or frequencies), followed by related Bayesian tasks. While solution rates were higher for natural frequency questions as compared to probability versions, eye-tracking analyses indeed yielded noticeable differences regarding eye movements between correct and incorrect solutions. For instance, heat maps (aggregated scan paths) of distinct results differed remarkably, thereby making correct and faulty strategies visible in the line of theoretical classifications. Moreover, the inherent structure of 2 × 2 tables seems to help participants avoid certain Bayesian mistakes (e.g., "Fisherian" error) while tree diagrams seem to help steer them away from others (e.g., "joint occurrence"). We will discuss resulting educational consequences at the end of the paper.

摘要

将信息格式从概率转换为频率,以及采用树状图或2×2表格等适当的可视化方式,都是有助于人们进行统计推理的重要工具。先前的研究表明,尽管这两种可视化类型在统计教科书中被广泛使用,但当它们以概率形式呈现时,其帮助作用有限,而以频率形式呈现时则能促进理解。在本研究中,我们试图复制这种效应,并通过眼动追踪方法研究概率性2×2表格和树状图在贝叶斯推理方面(即确定会出现哪些错误以及这些错误是否可以通过扫描路径来解释)是否有助于推理,以及相同的可视化方式在与频率结合时对个体有很大帮助。24名参与者的所有十个推理都仅基于呈现著名的“乳房X光检查情境”或“经济情境”(无额外文字说明)的树状图或2×2表格。我们首先要求参与者给出边际概率、联合概率和(非反转的)条件概率(或频率),随后进行相关的贝叶斯任务。与概率版本相比,自然频率问题的解决率更高,而眼动追踪分析确实在正确和错误解决方案的眼动方面产生了显著差异。例如,不同结果的热图(汇总扫描路径)差异显著,从而使正确和错误策略在理论分类中清晰可见。此外,2×2表格的内在结构似乎有助于参与者避免某些贝叶斯错误(如“费舍尔错误”),而树状图似乎有助于引导他们避免其他错误(如“联合出现错误”)。我们将在论文结尾讨论由此产生的教育意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a843/6530428/7f7bfc24824d/fpsyg-10-00632-g001.jpg

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