How to teach Bayesian reasoning: An empirical study comparing four different probability training courses

IF 4.7 1区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Learning and Instruction Pub Date : 2024-11-01 DOI:10.1016/j.learninstruc.2024.102032

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引用次数: 0

Abstract

Background

Bayesian reasoning is understood as the updating of hypotheses based on new evidence (e.g., the likelihood of an infection based on medical test results). As experts and students alike often struggle with Bayesian reasoning, previous research has emphasised the importance of identifying supportive strategies for instruction.

Aims

This study examines the learning of Bayesian reasoning by comparing five experimental conditions: two “level-2” training courses (double tree and unit square, each based on natural frequencies), two “level-1” training courses (natural frequencies only and a school-specific visualisation “probability tree”), and a “level-0” control group (no training course). Ultimately, the aim is to enable experts to make the right decision in high-stake situations.

Sample

N = 515 students (in law or medicine)

Method

In a pre-post-follow-up training study, participants’ judgments regarding Bayesian reasoning were investigated in five experimental conditions. Furthermore, prior mathematical achievement was used for predicting Bayesian reasoning skills with a linear mixed model.

Results

All training courses increase Bayesian reasoning, yet learning with the double tree shows best results. Interactions with prior mathematical achievement generally imply that students with higher prior mathematical achievement learn more, yet with notable differences: instruction with the unit square is better suited for high achievers than for low achievers, while the double tree training course is the only one equally suited to all levels of prior mathematical achievement.

Conclusion

The best learning of Bayesian reasoning occurs with strategies not yet commonly used in school.

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如何教授贝叶斯推理：比较四种不同概率培训课程的实证研究

背景贝叶斯推理被理解为根据新的证据更新假设（例如，根据医学测试结果更新感染的可能性）。本研究通过比较五种实验条件来考察贝叶斯推理的学习情况：两种 "2 级 "培训课程（双树和单位方阵，每种都基于自然频率）、两种 "1 级 "培训课程（仅自然频率和学校特定的可视化 "概率树"）以及 "0 级 "对照组（无培训课程）。样本N = 515 名学生（法律或医学专业）方法在一项前后跟进培训研究中，参与者在五种实验条件下对贝叶斯推理的判断进行了调查。结果所有培训课程都能提高贝叶斯推理能力，但双树学习效果最好。与先前数学成绩的交互作用一般意味着先前数学成绩较高的学生学习得更多，但也存在明显差异：单位平方教学更适合成绩好的学生，而不适合成绩差的学生，而双树训练课程是唯一一个同样适合所有先前数学成绩水平的课程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Learning and Instruction Multiple-

CiteScore

11.30

自引率

4.80%

发文量

109

期刊介绍： As an international, multi-disciplinary, peer-refereed journal, Learning and Instruction provides a platform for the publication of the most advanced scientific research in the areas of learning, development, instruction and teaching. The journal welcomes original empirical investigations. The papers may represent a variety of theoretical perspectives and different methodological approaches. They may refer to any age level, from infants to adults and to a diversity of learning and instructional settings, from laboratory experiments to field studies. The major criteria in the review and the selection process concern the significance of the contribution to the area of learning and instruction, and the rigor of the study.