在入门统计学中教授建模：公式语法与 tidyverse 语法的比较
Teaching modeling in introductory statistics: A comparison of formula and tidyverse syntaxes
AI总结：
本文通过对比入门统计学实验课中 R 的公式语法与 tidyverse 语法，发现两者在学生体验上差异极小，但在视频观看、计算时间、材料长度和函数使用上存在不同，为教师选择教学语法提供了依据。
摘要：
将编程纳入统计学课程涉及许多教学考量。当使用 R 编程语言时，一个考量是所使用的特定 R 语法。本文报告了在一对入门统计学实验课中进行的直接对比，其中一节完全使用公式语法（formula syntax）进行，另一节使用 tidyverse。对课前和课后调查数据的分析显示，两个实验课之间差异极小，无论哪个班级，学生都报告了积极的体验。对来自 YouTube 和 RStudio Cloud 的数据分析显示出有趣的差异。公式语法班级似乎观看了更大比例的课前 YouTube 视频，但在 RStudio Cloud 上花费的计算时间更少。相反，tidyverse 班级观看视频的比例更小，而花费的计算时间更多。对实验材料的分析表明，就所提供的 RMarkdown 材料的行数以及相关 YouTube 视频的分钟数而言，tidyverse 实验课往往略长。tidyverse 实验课让学生接触到更多不同的 R 函数，但更频繁地重复使用函数。两个实验课都依赖一个相对较小的、一致的函数词汇表，这可以为有兴趣用 R 教授入门统计学的教师提供一个起点。教师在两种语法下的教学体验主要在讨论分类变量之间的关系以及处理数值变量的汇总统计量时出现分歧。这项工作为希望在入门统计学教学中选择语法的教师提供了额外的证据。
英文摘要：
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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