Nicole Steib , Theresa Büchter , Andreas Eichler , Karin Binder , Stefan Krauss , Katharina Böcherer-Linder , Markus Vogel , Sven Hilbert
{"title":"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","authors":"Nicole Steib , Theresa Büchter , Andreas Eichler , Karin Binder , Stefan Krauss , Katharina Böcherer-Linder , Markus Vogel , Sven Hilbert","doi":"10.1016/j.learninstruc.2024.102032","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Aims</h3><div>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.</div></div><div><h3>Sample</h3><div><em>N</em> = 515 students (in law or medicine)</div></div><div><h3>Method</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>The best learning of Bayesian reasoning occurs with strategies not yet commonly used in school.</div></div>","PeriodicalId":48357,"journal":{"name":"Learning and Instruction","volume":"95 ","pages":"Article 102032"},"PeriodicalIF":4.7000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How to teach Bayesian reasoning: An empirical study comparing four different probability training courses\",\"authors\":\"Nicole Steib , Theresa Büchter , Andreas Eichler , Karin Binder , Stefan Krauss , Katharina Böcherer-Linder , Markus Vogel , Sven Hilbert\",\"doi\":\"10.1016/j.learninstruc.2024.102032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>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.</div></div><div><h3>Aims</h3><div>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.</div></div><div><h3>Sample</h3><div><em>N</em> = 515 students (in law or medicine)</div></div><div><h3>Method</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>The best learning of Bayesian reasoning occurs with strategies not yet commonly used in school.</div></div>\",\"PeriodicalId\":48357,\"journal\":{\"name\":\"Learning and Instruction\",\"volume\":\"95 \",\"pages\":\"Article 102032\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Learning and Instruction\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0959475224001592\",\"RegionNum\":1,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Learning and Instruction","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959475224001592","RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
How to teach Bayesian reasoning: An empirical study comparing four different probability training courses
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.
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.