Overcoming Programming Barriers for Non-Computing Majors in Data Science

该项目旨在通过加强对非计算学科学生的数据科学教育来服务于国家利益，以满足国家对多个学科的数据科学家的劳动力需求。对与计算相关的先决条件和编程任务的要求可能会成为拥有其他强大数据技能的非计算专业学生进入数据科学工作队伍的障碍。国际教育、科学及文化组织以前的一个项目的结果表明，在基于网络的学习平台的支持下，通过不涉及编码或几乎不涉及编码的深入实践练习，可以有效地提高学生的兴趣和学习成果。这一II级IUSE：EDU参与式学生学习跟踪项目是罗切斯特理工学院、霍华德大学和布林·莫尔学院共同努力的成果，该项目将升级学习平台，为教与学提供全面支持。该项目还将开发为非计算机专业学生量身定做的模块，评估该平台和三个地点的课程材料的有效性，并促进其他机构采用该项目的产品。这个项目的总体目标是提供有效的课程材料来克服编程障碍，让学生接触到各种数据科学主题，并教他们如何在自己学科的背景下解决数据问题。该项目将：(1)开发一个支持教与学的综合学习平台；(2)开发一套涵盖重要数据科学主题的课程模块，并为不同学科设计实践作业；(3)在三个参与机构(包括一所HBCU和一所女子文科学院)部署和评估该平台和课程模块；以及(4)进行一项研究，以调查开发的平台和课程模块对学生学习的影响是否独立于学生以前的计算经验、学科、性别和人口统计数据。项目单元可以灵活地整合到现有课程中，也可以结合在一起，作为常规课程提供，从而增加它们对其他机构的适应性。该项目将在项目周期内直接惠及来自三个地点8个不同专业的定向19门课程的1500多名学生。该项目进行的研究将为如何提供有效和包容性的数据科学教育提供见解。该项目还将提供若干职业发展机会(在线教程、信息会议、区域和会议讲习班)，并将通过多种渠道广泛传播项目成果和材料。NSF IUSE：EDU计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。通过参与的学生学习路径，该计划支持有前景的实践和工具的创建、探索和实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 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.