Facilitating Authentic Practice for Early Undergraduate Statistics Students

In current curricula, authentic statistical practice generally only occurs in capstone projects undertaken by advanced undergraduate and Master’s students. We argue that deferring practice is a mistake: undergraduate students should achieve experience via repeated practice from their first years onward, to achieve heightened levels of confidence and competence prior to graduation. However, statistical practice is not a “one size fits all” enterprise: for instance, elements of a capstone experience, such as extensive data preprocessing, may be out of place in earlier practice settings due to less-experienced students’ relative lack of coding skill. We describe a course we have implemented at Carnegie Mellon University, currently open to second-year students, that provides a circumscribed opportunity for statistical practice that limits coding breadth, uses fully curated data, treats statistical learning models as “gray boxes” to be understood qualitatively, and provides open-ended semester-long projects that students pursue outside of class. We show how pre- and post-course assessment tests and retrospective surveys indicate clear gains in the students’ knowledge of, and attitudes toward, statistical practice. Given its clear benefits, we feel that statistics and data science programs should offer a course like the one we describe to all undergraduate students pursuing statistics and data science degrees.

在当前的课程体系中，真实的统计实践通常仅在高年级本科生与硕士研究生的顶石项目（capstone project）中开展。我们认为，推迟统计实践的安排实为不当之举：本科生应当从大一学年起便通过反复实践积累经验，以便在毕业前显著提升自身的专业信心与能力水平。然而，统计实践并非“一刀切”的标准化流程：举例而言，顶石项目中的部分环节（如大规模数据预处理），由于经验尚浅的学生相对缺乏编码技能，并不适合放在早期实践场景中开展。我们介绍了一门在卡内基梅隆大学（Carnegie Mellon University）开设的实践课程，目前面向二年级学生开放。该课程为学生提供了受限范围的统计实践契机：限定了编码任务的广度，使用经过全面整理的精选数据集（curated data），将统计学习模型视作需通过定性方式理解的灰箱模型，并设置了为期一学期的开放式课外项目供学生自主完成。我们通过课程前后的测评考试与回顾性调查问卷，证实学生在统计实践相关知识储备与实践态度层面均取得了显著提升。鉴于该课程的显著优势，我们认为统计学与数据科学专业的培养方案，应当为所有攻读统计学与数据科学学位的本科生开设此类实践课程。