ABSTRACT

Data preparation (also called “wrangling” or “cleaning”) – the evaluation and manipulation of data prior to formal analysis – is often dismissed as a precursor to meaningful engagement with a dataset. Here, we re-envision data preparation in light of calls to prepare students for a data-rich world. Traditionally, curricular statistics explorations involve data that are derived from observations that students record themselves or that reflect familiar, relatively closed systems. In contrast, pre-constructed public datasets are much larger in scope and involve temporal, geographic, and other dimensions that complicate inference and blur boundaries between “signal” and “noise.” As a result, students have fewer opportunities to consider sources of variability in such datasets. Due to these constraints, we argue that data preparation becomes an important site for students to reason about variability with public data. Through analyses of repeated task-based interviews with five pairs of adolescent participants, we find that specific actions during data preparation, such as filtering data or calculating new measures, presented opportunities to engage leaners with variability as they prepared and analyzed several public socioscientific datasets. More broadly, our study highlights some changes to theory and curriculum in statistics education that are necessitated by a focus on “big data literacy”.

摘要

数据准备（也称为“争吵”或“清理”）——在正式分析之前对数据的评估和操作——通常被认为是有意义地参与数据集的前兆。在这里，我们根据呼吁让学生为数据丰富的世界做好准备，重新设想数据准备。传统上，课程统计探索涉及的数据来自学生自己记录的观察结果或反映熟悉的、相对封闭的系统的数据。相比之下，预先构建的公共数据集的范围要大得多，并且涉及时间、地理和其他维度，这会使推理复杂化并模糊“信号”和“噪声”之间的界限。因此，学生很少有机会考虑此类数据集中的可变性来源。由于这些限制，我们认为，数据准备成为学生推理公共数据可变性的重要场所。通过对五对青少年参与者的重复任务型访谈的分析，我们发现数据准备过程中的具体行动，例如过滤数据或计算新措施，为在准备和分析几个公共社会科学数据集时让具有可变性的学习者参与提供了机会。更广泛地说，我们的研究强调了统计教育理论和课程的一些变化，这些变化是关注“大数据素养”所必需的。例如过滤数据或计算新的措施，在准备和分析几个公共社会科学数据集时，提供了让学习者参与可变性的机会。更广泛地说，我们的研究强调了统计教育理论和课程的一些变化，这些变化是关注“大数据素养”所必需的。例如过滤数据或计算新的措施，在准备和分析几个公共社会科学数据集时，提供了让学习者参与可变性的机会。更广泛地说，我们的研究强调了统计教育理论和课程的一些变化，这些变化是关注“大数据素养”所必需的。