解决中学数据科学教育中的确认偏误

IF 1.7 Q2 MATHEMATICS, APPLIED
S. Hedges, Kim Given
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引用次数: 1

摘要

需要对中学生参与统计问题解决的过程进行更多的研究,特别是开始的过程步骤:制定问题和制定收集数据/考虑数据的计划。此外,增加大规模电子数据集的可用性是一个尚未开发的研究领域。本解释性研究考察了中学生对统计概念的理解,这些概念涉及到使用互联网上可用的数据在社会问题背景下收集数据以回答统计问题的计划。学生的手工制品,研究人员的笔记,以及来自郊区一所中学的两个资优教育退出班的9组20名七年级学生的音频和视频记录被用来回答研究问题。使用先前开发的框架中的先验代码分析数据,并使用归纳方法找到主题。与确认偏差相关的数据中出现了三个主题。一些中学生对他们选择研究的社会问题有先入为主的观念,这对他们的统计问题有偏见。这反过来又影响了学生用来回答问题的数据来源。确认偏误是一个严重的问题,由于无穷无尽的电子数据来源而加剧。我们认为,这种类型的偏见应该在学生的教育经历的早期解决。基于本研究的发现,我们提出了未来研究的建议以及对统计和数据科学教育的启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Addressing confirmation bias in middle school data science education
More research is needed involving middle school students' engagement in the statistical problem-solving process, particularly the beginning process steps: formulate a question and make a plan to collect data/consider the data. Further, the increased availability of large-scale electronically accessible data sets is an untapped area of study. This interpretive study examined middle school students' understanding of statistical concepts involved in making a plan to collect data to answer a statistical question within a social issue context using data available on the internet. Student artifacts, researcher notes, and audio and video recordings from nine groups of 20 seventh-grade students in two gifted education pull-out classes at a suburban middle school were used to answer the study research questions. Data were analyzed using a priori codes from previously developed frameworks and by using an inductive approach to find themes.Three themes that emerged from data related to confirmation bias. Some middle school students held preconceptions about the social issues they chose to study that biased their statistical questions. This in turn influenced the sources of data students used to answer their questions. Confirmation bias is a serious issue that is exacerbated due to endless sources of data electronically available. We argue that this type of bias should be addressed early in students' educational experiences. Based on the findings from this study, we offer recommendations for future research and implications for statistics and data science education.
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CiteScore
3.30
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