论文标题
消除自我选择：在第一年的入门课程中使用数据科学进行真实的本科研究
Eliminating self-selection: Using data science for authentic undergraduate research in a first-year introductory course
论文作者
论文摘要
研究经验和指导已被确定为增加学生在STEM领域的参与和保留率的有效干预措施，对不当人群的学生产生了很大影响。但是，一对一的指导受到可用教师数量的限制，在某些情况下，也受津贴的资金可用性。一对一的指导进一步受到学生的选择和自我选择的限制。由于研究职位通常具有竞争力，因此通常是由表现最好的学生夺取的。更重要的是，许多不认为自己是班级顶级学生的学生，或者不认为自己是研究人员可能不适用，并且自我选择对非传统学生的影响最大。为了解决可伸缩性，选择和自我选择的障碍，我们为本科生设计了一项数据科学研究经验，作为入门计算机科学课程的一部分。通过干预，学生早在第一学期就接受了真实的研究。干预措施是包容性的，因为所有学生都注册了该课程，而没有选择或自我选择的过程。该研究的重点是大型文本数据库的分析。使用增强发现的软件工具，学生们分析了国会演讲的语料库，并确定民主演讲和共和党演讲之间的差异模式，对某些法案的演讲和反对某些法案之间的差异，以及关于通过的账单和未通过的账单的演讲之间的差异。在研究经验的开头，所有学生都遵循相同的协议并使用相同的数据，然后每组学生都在自己的研究项目中工作，作为课程最终项目的一部分。
Research experience and mentoring has been identified as an effective intervention for increasing student engagement and retention in the STEM fields, with high impact on students from undeserved populations. However, one-on-one mentoring is limited by the number of available faculty, and in certain cases also by the availability of funding for stipend. One-on-one mentoring is further limited by the selection and self-selection of students. Since research positions are often competitive, they are often taken by the best-performing students. More importantly, many students who do not see themselves as the top students of their class, or do not identify themselves as researchers might not apply, and that self selection can have the highest impact on non-traditional students. To address the obstacles of scalability, selection, and self-selection, we designed a data science research experience for undergraduates as part of an introductory computer science course. Through the intervention, the students are exposed to authentic research as early as their first semester. The intervention is inclusive in the sense that all students registered to the course participate in the research, with no process of selection or self-selection. The research is focused on analytics of large text databases. Using discovery-enabling software tools, the students analyze a corpus of congressional speeches, and identify patterns of differences between democratic speeches and republican speeches, differences between speeches for and against certain bills, and differences between speeches about bills that passed and bills that did not pass. In the beginning of the research experience all student follow the same protocol and use the same data, and then each group of students work on their own research project as part of their final project of the course.
