Creating Data Science Pathways for STEM Student Success

为 STEM 学生的成功打造数据科学途径

• 批准号: 2135596
• 负责人: Oleg Muzician
• 金额: $ 74.41万
• 依托单位: CUNY Borough of Manhattan Community College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135596&HistoricalAwards=false
• 关键词: Creating; Data; Science; Pathways; STEM

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This project aims to serve the national interest by developing and implementing a data science program through a multilayered approach that can be used as a model for other postsecondary institutions. The model will contextualize mathematics course content, reduce attrition in lower-division gateway courses, and facilitate senior college transfer. The resulting data science program will address the educational achievement gap among groups traditionally underrepresented in STEM. The new data science program will promote structural systemic reforms through the integration of support services within a sustainable, institutional framework. An additional focus will be on curricular reforms through the development of data science content. Curriculum development will involve the infusion of data science concepts into five existing mathematics courses and the creation of two new/redesigned data science courses. These efforts aim to contribute to the development of students’ data literacy by enhancing their conceptual understanding, integrating the use of real-life data, and fostering active learning. This project will address the problems of loss of interest, culturally unresponsive instruction, insufficient career information, and anxiety about mathematics and related subjects. This work will generate evidence that will improve the understanding of how the use of a guided pathways model can help students successfully transition from lower-division to upper-division coursework in data science. Qualitative and quantitative data will be collected from multiple sources to respond to the evaluation questions. A review of relevant program documentation will provide detailed information regarding the fidelity of the implementation of programmatic activities. In addition to program records, observations of course sessions and interviews with program staff will provide insight into the quality of the project activities. Descriptive statistics, such as frequencies and means, will be reported for various demographic and participant profile data. Cross-tabulations and inferential statistics will be used to analyze program perception data. An external evaluator will provide an ongoing formative evaluation that will enable the project team to monitor and improve project activities. The NSF program description on Advancing Innovation and Impact in Undergraduate STEM Education at Two-year Institutions of Higher Education supports projects that advance STEM education initiatives at two-year colleges. The program description promotes innovative and evidence-based practices in undergraduate STEM education at two-year colleges.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.

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。该项目旨在通过多层次的方法开发和实施数据科学计划，以服务于国家利益，该方法可用作其他中学后机构的模式。该模型将情境化数学课程内容，减少低年级网关课程的损耗，并促进高中转学。由此产生的数据科学计划将解决传统上在STEM中代表性不足的群体之间的教育成就差距。新的数据科学计划将通过在可持续的制度框架内整合支持服务来促进结构性系统改革。另一个重点将是通过开发数据科学内容进行课程改革。课程开发将涉及将数据科学概念注入五门现有的数学课程，并创建两门新的/重新设计的数据科学课程。这些工作旨在通过加强学生对概念的理解、整合使用真实数据和促进主动学习，促进学生的数据素养发展。该项目将解决对数学和相关科目失去兴趣、文化上不适应的教学、职业信息不足以及焦虑等问题。这项工作将产生证据，将提高如何使用引导路径模型可以帮助学生成功地从低年级过渡到高年级的数据科学课程的理解。将从多个来源收集定性和定量数据，以回答评价问题。对相关方案文件的审查将提供关于方案活动执行的忠实性的详细信息。除了项目记录之外，对课程的观察和对项目工作人员的采访将有助于深入了解项目活动的质量。将报告各种人口统计学和受试者特征数据的描述性统计量，如频率和平均值。交叉表和推理统计将用于分析程序感知数据。一名外部评价员将提供持续的形成性评价，使项目小组能够监测和改进项目活动。NSF关于在两年制高等教育机构中推进本科STEM教育的创新和影响的项目描述支持在两年制大学中推进STEM教育计划的项目。该项目旨在促进两年制大学本科STEM教育的创新和循证实践。该奖项反映了NSF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。