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arXiv 2003.10821physics.ed-ph
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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

Lior Shamir

摘要:

研究经验和指导已被确定为一种有效的干预措施,以增加学生参与和保留在干领域,具有很高的影响,学生从不应得的人口。然而,一对一的指导受到可用教师数量的限制,在某些情况下还受到助学金资金的限制。一对一的辅导还受到学生选择和自我选择的限制。由于研究职位往往是竞争激烈的,他们往往是由表现最好的学生。更重要的是,许多学生谁不认为自己是班上的尖子生,或不认为自己是研究人员可能不会申请,自我选择可以有最大的影响,非传统的学生。为了解决可扩展性,选择和自我选择的障碍,我们为本科生设计了一个数据科学研究体验,作为计算机科学入门课程的一部分。通过干预,学生早在第一学期就接触到真实的研究。干预措施是包容性的,因为所有注册课程的学生都参加了研究,没有选择或自我选择的过程。研究重点是大型文本数据库的分析。使用发现使软件工具,学生们分析了国会演讲语料库,并确定民主党演讲和共和党演讲之间的差异模式,支持和反对某些法案的演讲之间的差异,以及关于通过和未通过法案的演讲之间的差异。在研究经验的开始,所有学生遵循相同的协议,使用相同的数据,然后每组学生对自己的研究项目的工作,作为他们的课程的最终项目的一部分。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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