Overcoming Programming Barriers for Non-Computing Majors in Data Science

克服数据科学非计算专业的编程障碍

• 批准号: 2336929
• 负责人: Xumin Liu
• 金额: $ 74.99万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2336929&HistoricalAwards=false
• 关键词: Overcoming; Programming; Barriers; Non; Computing

This project aims to serve the national interest by strengthening data science education for students in non-computing disciplines in order to meet the national workforce demands for data scientists across many disciplines. The requirement for computing-related prerequisites and programming tasks can be a barrier for non-computing students with other strong data skills to enter the data science workforce. Results from a prior IUSE project indicate the effectiveness of increasing student interest and learning outcomes through in-depth hands-on practice with no or little coding involved, supported by a web-based learning platform. This Level II IUSE:EDU Engaged Student Learning track project is a collaborative effort among Rochester Institute of Technology, Howard University, and Bryn Mawr College that will upgrade the learning platform to provide comprehensive support for both teaching and learning. The project will also develop modules tailored to non-computing students, evaluate the effectiveness of the platform and the curricular materials at the three sites, and facilitate adoption of the project's products at other institutions. The overarching goal of this project is to provide effective curricular materials to overcome the programming barriers, expose students to various data science topics, and teach them how to solve data problems in the context of their own disciplines. The project will: (1) develop an integrated learning platform to support both teaching and learning; (2) develop a set of course modules covering important data science topics with hands-on assignments designed for different disciplines; (3) deploy and evaluate the platform and course modules at three participating institutions including an HBCU and a women’s liberal arts college; and (4) conduct a study to investigate if the impact of the developed platform and course modules on student learning is independent from students' prior computing experience, discipline, gender, and demographics. The project modules can be flexibly integrated into an existing course or be combined together and offered as a regular course, increasing their adaptability to other institutions. This project will directly benefit more than 1500 students enrolled in the targeted 19 courses from more than 8 different majors at the three sites during the project cycle. The study conducted in this project will provide insights on how to offer effective and inclusive data science education. The project will also provide several professional development opportunities (online tutorials, information sessions, regional and conference workshops) and project outcomes and materials will be widely disseminated via multiple channels. The NSF IUSE: EDU 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.

该项目旨在通过加强对非计算学科的学生的数据科学教育来满足国家利益，以满足许多学科的数据科学家的国家劳动力需求。与计算相关的先决条件和编程任务的要求可能是对具有其他强大数据技能的非计算学生进入数据科学工作人员的障碍。先前的IUSE项目的结果表明，通过基于Web的学习平台的支持，通过深入的动手实践来增加学生兴趣和学习成果的有效性。这二级IUSE：EDU订婚的学生学习轨道项目是罗切斯特理工学院，霍华德大学和Bryn Mawr学院的合作努力，该学院将升级学习平台，为教学和学习提供全面的支持。该项目还将开发针对非计算学生量身定制的模块，评估平台和三个站点的课程材料的有效性，并促进在其他机构中采用该项目的产品。该项目的总体目标是提供有效的课程材料来克服编程障碍，使学生了解各种数据科学主题，并教他们如何在自己的学科中解决数据问题。该项目将：（1）开发一个综合学习平台来支持教学和学习； （2）开发一组课程模块，该模块涵盖了重要的数据科学主题，该主题具有为不同学科设计的动手作业； （3）在包括HBCU和女子文科学院在内的三个参与机构中部署和评估平台和课程模块； （4）进行一项研究，以调查开发平台和课程模块对学生学习的影响是否与学生先前的计算经验，纪律，性别和人口统计学无关。可以将项目模块灵活地集成到现有课程中，也可以合并为常规课程，从而提高了对其他机构的适应性。该项目将直接受益于在项目周期中三个地点的8个不同专业的有针对性的19个课程中招收的1500多名学生。该项目进行的研究将提供有关如何提供有效和包容性数据科学教育的见解。该项目还将提供一些专业发展的机会（在线教程，信息会议，区域和会议研讨会），项目成果和材料将通过多个渠道广泛传播。 NSF IUSE：EDU计划支持研发项目，以提高所有学生STEM教育的有效性。通过参与的学生学习轨道，该计划支持了承诺实践和工具的创建，探索和实施。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估审查标准，被认为是珍贵的支持。