Addressing Preparation Gaps and Promoting Culturally Relevant Teaching to Support Diverse Groups in Computing Courses

解决准备差距并促进文化相关的教学，以支持不同群体的计算机课程

• 批准号: 2142381
• 负责人: Kalpathi Subramanian
• 金额: $ 46.59万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2142381&HistoricalAwards=false
• 关键词: Addressing; Preparation; Gaps; Promoting; Culturally

This project aims to serve the national interest by preparing a large, modern, and diverse computing workforce. The project team will investigate how its learning materials (known as a BRIDGES system) contribute to closing the preparation cap of college computing students. The BRIDGES system aims to improve engagement and motivation of first and second-year computer science students in foundational course. However, because studies have shown that students from underrepresented groups derive fewer benefits from the BRIDGES system, the system will be extended in this project to serve a broader and more diverse population. The project will develop learning materials to bridge preparation gaps among students and adapt to the skills, interests, and values of all learners. Additionally, the project will expand the use of the BRIDGES system through outreach, enhanced instructor support, and a cloud-based solution to ease adoption. The project aims to have a significant impact on data structures and algorithm analysis courses, which are foundational courses that underlie most college computer science degree programs.The project will develop evidence-based guidelines to customize learning materials for students from underrepresented groups in STEM to improve student engagement. Outcomes will be explored across gender, ethnicity, and institution types. The project team will investigate how to design a collection of assignments that match the same learning outcomes, while being rooted in different contexts, values, and applications. Surveys will be administered to understand the effectiveness of these approaches and assess the resulting student outcomes. Regression analyses will be used to determine if the pretest scores or the gains in scores from pretest to post-test predict responses to the BRIDGES assignments, survey, and project or course grades. Chi square tests will be used for items with response choices while regression analysis will be used with scale scores. 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.

该项目旨在通过准备一个大型，现代化和多样化的计算劳动力来服务于国家利益。项目团队将调查其学习材料（称为BRIDGES系统）如何有助于关闭大学计算机学生的准备上限。BRIDGES系统旨在提高一年级和二年级计算机科学学生在基础课程中的参与度和积极性。然而，由于研究表明，来自代表性不足群体的学生从BRIDGES系统中获得的好处较少，因此该系统将在本项目中得到扩展，以服务于更广泛和更多样化的人口。该项目将开发学习材料，以弥合学生之间的准备差距，并适应所有学习者的技能，兴趣和价值观。此外，该项目还将通过推广、增强教师支持和基于云的解决方案来扩大BRIDGES系统的使用，以便于采用。该项目旨在对大多数大学计算机科学学位课程的基础课程数据结构和算法分析课程产生重大影响。该项目将制定基于证据的指导方针，为STEM中代表性不足的学生定制学习材料，以提高学生的参与度。结果将探讨跨性别，种族和机构类型。项目团队将研究如何设计一系列与相同学习成果相匹配的作业，同时植根于不同的背景，价值观和应用程序。将进行调查，以了解这些方法的有效性，并评估由此产生的学生成果。回归分析将被用来确定预测试分数或从预测试到后测试的分数增益是否预测对BRIDGES作业、调查和项目或课程成绩的反应。卡方检验将用于回答选择的项目，而回归分析将用于量表评分。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。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。

项目成果

Location Based Assignments in Early CS Courses Using BRIDGES Engages Students

早期计算机科学课程中使用 BRIDGES 进行基于位置的作业吸引学生

• 发表时间: 2023
• 期刊: Journal of computing sciences in colleges
• 作者: Mcquaigue Matthew;Strahler, Jay;Subramanian, Kalpathi;Saule, Erik;Payton, Jamie
• 通讯作者: Payton, Jamie

BRIDGES: Real world data, assignments and visualizations to engage and motivate CS majors

BRIDGES：真实世界的数据、作业和可视化，以吸引和激励计算机专业的学生

• DOI: 10.1007/s10639-023-11958-4
• 发表时间: 2023
• 期刊: Education and Information Technologies
• 影响因子: 5.5
• 作者: Burlinson, David;Mcquaigue, Matthew;Goncharow, Alec;Subramanian, Kalpathi;Saule, Erik;Payton, Jamie;Goolkasian, Paula
• 通讯作者: Goolkasian, Paula