Data Path: Creating a New STEM Pathway for Undergraduates from Statistics Into Data Science

该项目旨在通过增加STEM学生的数量和多样性来服务于国家利益。为此，它将通过统计和数据科学为一所服务于西班牙裔的两年制大学的学生开辟一条进入STEM专业和职业的新途径。这条新道路有望改善STEM的学习和教学，并增加追求STEM的学生的多样性。该课程的第一步将是重新设计的统计学导论课程，纳入基于项目的学习。这一变化有望吸引更多的学生，激发他们追求STEM道路的兴趣。该课程的下一步将是数据科学课程的新入门，该课程将满足转学数学的要求。数据科学课程预计将为那些不一定认为自己从事STEM职业的学生提供进入STEM的入口。本项目将积极招收新统计课程的学生到新数据科学课程中，从而使统计课程从终端数学课程转变为STEM人才管道。数据科学课程将通过一个新的数据学者计划招收更多的学生，该计划将为最初可能对STEM专业不感兴趣的学生提供一个支持性的STEM社区。最后，本计划将提供数学教师实施专案式课程教学法的专业发展。在统计学导论课程中，将采用准实验设计来衡量基于项目的学习的整体有效性。在新的基于项目的学习统计课程中，学生将与使用传统教学法的统计课程的学生进行比较。方差分析将用于检验两类课程在学生经历、学生出勤率、课程成功、学位完成和四年制大学转学率等方面的差异。此外，定性方法，如焦点小组和开放式调查问题，将用于评估实施基于项目的学习模式的保真度。这个项目项目可以作为其他社区学院寻求建立数据科学项目和进入STEM的新途径的典范。将通过会议演讲和出版物分享关于不断变化的教学法如何影响学生对STEM的兴趣和成功的经验教训和见解。项目期间的预期成果包括提高学生在基于项目的统计学课程中的成功率，增加数据科学课程的入学率，以及增加数据学者计划的参与度。NSF IUSE: EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to serve the national interest by increasing the number and diversity of STEM students. To do so, it will establish a new path into STEM majors and careers through statistics and data science for students at a Hispanic-serving two-year college. This new path is expected to improve STEM learning and teaching and increase the diversity of students pursuing STEM. The first step in the pathway will be a redesigned Introduction to Statistics course that incorporates project-based learning. This change is expected to engage more students and spark their interest in pursuing a STEM pathway. The next step in the pathway will be a new introduction to Data Science course, which will meet a transfer math requirement The Data Science course is expected to serve as an onramp into STEM for students who do not necessarily see themselves in a STEM career. The project will actively recruit students from the new statistics course into the new Data Science course, thus transforming the statistics course into a STEM talent pipeline instead of a terminal math course. Additional students will be recruited into the Data Science course via a new Data Scholars Program, which will provide a supportive STEM community for students who may not be initially interested in STEM majors. Finally, the project will provide professional development to mathematics faculty on implementing project-based curriculum pedagogy. A quasi-experimental design will be used to measure the overall effectiveness of project-based learning in the introductory statistics course. Students in the new project-based learning statistics course will be compared with students in statistics courses using traditional pedagogy. Analysis of variance will be used to examine differences between the two types of courses on measures of student experience, student attendance, course success, degree completion, and transfer rate to four-year universities. Furthermore, qualitative methods, such as focus groups and open-ended survey questions, will be used to evaluate the fidelity of implementing the project-based learning model. This project project may serve as a model for other community colleges looking to build a data science program and a new pathway into STEM. Lessons learned and insights gained into how changing pedagogy impacts student interest and success in STEM will be shared through conference presentations and publications. Anticipated outcomes during the project period include increased student success rates in the project-based statistics course, increased enrollment in the data science course, and increased participation in the Data Scholars program. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.