Overcoming Programming Barriers for Non-Computing Majors in Data Science

该项目旨在通过加强非计算学科学生的数据科学教育来满足国家对许多学科数据科学家的劳动力需求。对计算相关先决条件和编程任务的要求可能是具有其他强大数据技能的非计算专业学生进入数据科学劳动力的障碍。从以前的IUSE项目的结果表明，通过深入的动手实践，没有或很少涉及编码，基于网络的学习平台的支持下，提高学生的兴趣和学习成果的有效性。这个二级IUSE：EDU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之间的合作努力，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机专业学生的模块，评估平台和课程材料在三个地点的有效性，并促进其他机构采用该项目的产品。该项目的总体目标是提供有效的课程材料，以克服编程障碍，让学生接触各种数据科学主题，并教他们如何在自己的学科背景下解决数据问题。该项目将：（1）开发一个综合学习平台，以支持教与学;（2）开发一套涵盖重要数据科学主题的课程单元，并为不同学科设计实践作业;（3）在三所参与院校（包括HBCU和女子博雅学院）部署和评估平台和课程单元;以及（4）进行研究以调查所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先前的计算经验、学科、性别和人口统计。这些项目单元可以灵活地融入现有课程，也可以合并在一起作为常规课程提供，从而提高它们对其他机构的适应性。该项目将直接受益于超过1500名学生就读于目标19门课程，来自8个不同的专业在三个地点在项目周期。该项目中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专业发展机会（在线辅导、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STEM教育的有效性。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。

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.