With the growing interest in Big Data, researchers are calling for a reframing of key questions about what constitutes knowledge found in very large data settings (Boyd & Crawford, 2012). Many researchers are developing new ways to access and organize Big Data, and many academic institutions are offering new courses that focus on analyzing Big Data. Many Big Data course syllabi require statistics and programming as prerequisites (Friedman, 2017). It is common for instructors to report resistance from students to learning statistics (Schoenfeld, 1992). There are a variety of reasons for this resistance—for example, students may feel that statistics lack vividness or the ability to engage their emotions (Hill, 2004). While there is already a large body of research on the benefits of visuals in education (Gilbert, 2008), a review of this literature could not produce a detailed model for a visualizationcentric statistics curriculum at the undergraduate level.

中文翻译：

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使用 Dick and Carey 模型开发大数据时代的可视化教育课程

随着对大数据的兴趣日益浓厚，研究人员呼吁重新定义关于在超大数据环境中发现的知识构成的关键问题（Boyd & Crawford，2012 年）。许多研究人员正在开发访问和组织大数据的新方法，许多学术机构正在提供专注于分析大数据的新课程。许多大数据课程大纲要求以统计学和编程为先决条件（弗里德曼，2017 年）。教师报告学生对学习统计的抵制是很常见的（Schoenfeld，1992）。这种抵制有多种原因——例如，学生可能觉得统计数据缺乏生动性或无法调动他们的情绪（Hill，2004）。虽然已经有大量关于视觉在教育中的好处的研究 (Gilbert, 2008)，