论文标题

入门统计中的教学建模:公式和整形语法的比较

Teaching modeling in introductory statistics: A comparison of formula and tidyverse syntaxes

论文作者

McNamara, Amelia

论文摘要

将编程纳入统计课程中有许多教学方面的考虑因素。使用编程语言r时,一个考虑是将使用的特定R语法。本文报告了在一对介绍性统计实验室中进行的面对面比较,一个是在公式语法中进行的,另一个在整洁的语法中进行。对调查后数据的分析表明,这两个实验室之间的差异很小,而学生报告的积极经验无论有什么部分。来自YouTube和Rstudio Cloud的数据的分析显示出有趣的区别。公式部分似乎观看了更大比例的前LAB YouTube视频,但在Rstudio Cloud上花费更少的时间计算。相反,Tidyverse部分观看了较小比例的视频,并花费了更多的时间计算。对实验室材料的分析表明,在提供的RmarkDown材料和相关YouTube视频的分钟内,整形实验室往往略长。整洁的实验室使学生暴露于更不同的R功能,但重复使用的功能更频繁。这两个实验室都依赖于一致功能的相对较小的词汇,这可以为有兴趣教学的教师提供一个起点R。在两个语法中教学的教学经验主要是在讨论分类变量之间的关系时,以及在使用数字变量汇总统计数据时,主要是在讨论分类变量之间的关系时。这项工作为希望在概论统计教学的语法之间进行选择的讲师提供了其他证据。

There are many pedagogical considerations for incorporating programming into a statistics course. When using the programming language R, one consideration is the particular R syntax that will be used. This paper reports on a head-to-head comparison run in a pair of introductory statistics labs, one conducted fully in the formula syntax, the other in tidyverse. Analysis of pre- and post-survey data show minimal differences between the two labs, with students reporting a positive experience regardless of section. Analysis of data from YouTube and RStudio Cloud show interesting distinctions. The formula section appeared to watch a larger proportion of pre-lab YouTube videos, but spend less time computing on RStudio Cloud. Conversely, the tidyverse section watched a smaller proportion of the videos and spent more time computing. Analysis of lab materials showed tidyverse labs tended to be slightly longer in terms of lines in the provided RMarkdown materials and minutes of the associated YouTube videos. The tidyverse labs exposed students to more distinct R functions, but reused functions more frequently. Both labs relied on a relatively small vocabulary of consistent functions, which can provide a starting point for instructors interested in teaching introductory statistics in R. The instructor experience of teaching in the two syntaxes diverged primarily when discussing relationships between categorical variables, as well as when working with summary statistics for numeric variables. This work provides additional evidence for instructors looking to choose between syntaxes for introductory statistics teaching.

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