Data science transfer pathways from associate's to bachelor's programs
Data science transfer pathways from associate's to bachelor's programs
复制标题
数据科学从副学士学位课程的转移途径
DOI：
10.1162/99608f92.e2720e81
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Horton, Nicholas Jon
中科院分区：
文献类型：
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作者：
Baumer, Benjamin S.;Horton, Nicholas Jon
在美国的公立大学完成大学教育的学生中，有相当一部分开始他们的旅程是在935所公立两年制大学之一。虽然提供数据科学学士学位的四年制大学数量继续增加，但许多两年制大学的数据科学教学却落后于人。一个主要的障碍是数据科学入门课程的相对缺乏，这些课程可以为多个学生受众提供服务，并且可以很容易地转移。此外，数据科学缺乏预定义的转移途径（或衔接协议），造成了越来越大的脱节，使想要学习数据科学的学生处于不利地位。我们描述了数据科学转移途径的机会和障碍。五点课程摩擦值得注意：1）数据科学的第一门课程，2）数据科学的第二门课程，3）科学计算，数据科学工作流和/或可再生计算的课程，4）实验室科学，以及5）在通识教育和文科课程映射的背景下导航通信，道德和应用领域的要求。我们对现有的转移途径进行了分类，努力使各机构的课程保持一致，用破坏性最小的解决方案克服障碍，以及培养这些途径的方法。这些领域的改进对于确保广泛多样的学生能够参与并成功完成本科数据科学课程至关重要。
A substantial fraction of students who complete their college education at a public university in the United States begin their journey at one of the 935 public two-year colleges. While the number of four-year colleges offering bachelor's degrees in data science continues to increase, data science instruction at many two-year colleges lags behind. A major impediment is the relative paucity of introductory data science courses that serve multiple student audiences and can easily transfer. In addition, the lack of pre-defined transfer pathways (or articulation agreements) for data science creates a growing disconnect that leaves students who want to study data science at a disadvantage. We describe opportunities and barriers to data science transfer pathways. Five points of curricular friction merit attention: 1) a first course in data science, 2) a second course in data science, 3) a course in scientific computing, data science workflow, and/or reproducible computing, 4) lab sciences, and 5) navigating communication, ethics, and application domain requirements in the context of general education and liberal arts course mappings. We catalog existing transfer pathways, efforts to align curricula across institutions, obstacles to overcome with minimally-disruptive solutions, and approaches to foster these pathways. Improvements in these areas are critically important to ensure that a broad and diverse set of students are able to engage and succeed in undergraduate data science programs.