Collaborative Research: Retaining and Engaging Computer Science majors By Solving and Visualizing Algorithmic Problems on Real-world Data Sets

协作研究：通过解决和可视化现实世界数据集上的算法问题来留住和吸引计算机科学专业的学生

• 批准号: 1726148
• 负责人: Jamie Payton
• 金额: $ 5.67万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1726148&HistoricalAwards=false
• 关键词: Collaborative; Research; Retaining; Engaging; Computer

The government demands greater quality in undergraduate education and industry demands more highly qualified graduates who can withstand rapidly changing qualifications. Higher education institutions strive to balance these demands by producing graduates with workforce-ready knowledge and skills. Unfortunately, government, industry, and higher education do not naturally come together often enough to achieve the needed student outcomes. Another force affecting both higher education and industry is ?big data,? the ability to capture vast amounts of information about anything that can be measured, including student learning. This project will explore the intersection of big data with educational opportunities across a learner?s life, the opportunities and challenges created by these intersections, and what roles different institutions (e.g. K-12, industry, universities, government) will or should play in improving undergraduate education. Through a series of workshops, this project aims to facilitate interactions among constituents from industry, the government, and higher education institutions. The goal is to identify the kinds of big data that can tell us how to expand learning opportunities across a student?s lifetime. Through these workshops, participants will be guided to use big data to investigate trends in undergraduate engineering education, including: redefining engineering education in relation to the skills needed to be a working engineer; exploring how real-time feedback can promote learning; increasing the efficiency at which engineers acquire new knowledge and skills; the increasing interconnections that require thinking about systems as well as individual components; and rethinking the shift towards more automation, which may radically change the skills needed for employability. Big data and other trends will fundamentally change how education is obtained, who has access to various forms of education (e.g., on-line vs in person courses), and the activities that define a high-quality engineering education. By exploring the implications of big data on education. this project has the potential to transform engineering education specifically and STEM education more generally.More specifically, this project will involve a series of four workshops that are designed to achieve the following: (1) map the engineering education ecosystem from the perspective of how the affordances created by big data will impact processes, cultures, and structures in higher education; (2) explore opportunities at productive interfaces within the ecosystem; (3) understand resource allocation and flow; and (4) develop heuristics to allow various stakeholders to navigate cultural and structural shifts. Additionally, the project will integrate data collection through ethnography and survey methodologies adapted for complex, dynamic systems to more broadly capture perspectives across engineering education and explicitly focuses on network building and disseminating materials to help various entities across engineering education navigate change. This project is designed to advance knowledge of how different sectors within the engineering education ecosystem capitalize on, adapt to, or are threatened by education and workforce trends broadly driven by big data. The workshops will draw a broad audience from many sectors which includes both industry and university representatives with broad expertise and networks in personalized learning. Advances in information technology and the ability to monitor and analyze the interactions of millions of learners with online content has the potential to lead to transformative shifts in education. While these technologies have the capacity to create more equity and access to education, without opportunities for the range of stakeholders to communicate and build robust networks, such a transformative outcome cannot be assured. The opportunities for networking and relationship building in this project are supported scenario analysis and systems thinking, in a framework that has been shown to affect participant narratives.

政府要求更高质量的本科教育，行业要求更多高素质的毕业生，他们能够经受住快速变化的资格。高等教育机构努力通过培养具有劳动力准备知识和技能的毕业生来平衡这些需求。不幸的是，政府、行业和高等教育并不能自然地结合在一起，以实现所需的学生成果。影响高等教育和行业的另一股力量是？大数据。捕捉任何可以测量的信息的能力，包括学生学习。该项目将探索大数据与整个学习者的教育机会的交叉-S的生活，这些交叉创造的机遇和挑战，以及不同的机构(例如K-12，行业，大学，政府)将或应该在改善本科教育方面发挥什么作用。通过一系列研讨会，该项目旨在促进来自行业、政府和高等教育机构的成员之间的互动。我们的目标是识别哪些类型的大数据可以告诉我们如何在一个学生的一生中扩大学习机会？S。通过这些研讨会，参与者将被引导使用大数据来调查本科工程教育的趋势，包括：重新定义工程教育与成为一名工作工程师所需技能的关系；探索实时反馈如何促进学习；提高工程师获取新知识和技能的效率；需要考虑系统和单个组件的日益增加的相互联系；以及重新思考向更多自动化的转变，这可能从根本上改变就业所需的技能。大数据和其他趋势将从根本上改变教育的获得方式、谁有机会接受各种形式的教育(例如，在线课程与面对面课程)，以及定义高质量工程教育的活动。通过探索大数据对教育的影响。这个项目有可能具体地改变工程教育，更广泛地改变STEM教育。更具体地说，这个项目将包括一系列四个研讨会，旨在实现以下目标：(1)从大数据创造的负担将如何影响高等教育的过程、文化和结构的角度绘制工程教育生态系统图；(2)探索生态系统内生产界面上的机会；(3)了解资源分配和流动；以及(4)开发启发式方法，使不同的利益相关者能够驾驭文化和结构变化。此外，该项目将通过人种学和调查方法整合数据收集，以适应复杂、动态的系统，以更广泛地捕捉工程教育的观点，并明确侧重于网络建设和传播材料，以帮助工程教育中的各种实体适应变化。该项目旨在提高对工程教育生态系统中不同部门如何利用、适应或受到大数据驱动的教育和劳动力趋势的威胁的知识。讲习班将吸引来自许多部门的广泛受众，其中包括在个性化学习方面具有广泛专门知识和网络的工业界和大学代表。信息技术的进步以及监测和分析数百万学习者与在线内容互动的能力有可能导致教育的变革。虽然这些技术有能力创造更多的公平和获得教育的机会，但如果利益攸关方没有机会进行交流和建立健全的网络，就不能保证取得这样的变革性结果。在这个项目中建立网络和建立关系的机会得到了情景分析和系统思维的支持，在一个已被证明影响参与者叙述的框架内。

项目成果

Bringing Real-World Data, Interactive Games and Visualizations into Early CS Courses

将真实世界数据、互动游戏和可视化引入早期计算机科学课程

• DOI: 10.1145/3328778.3367011
• 发表时间: 2020
• 期刊: SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education
• 作者: Subramanian, Kalpathi;Saule, Erik;Payton, Jamie
• 通讯作者: Payton, Jamie

Visualization, Assessment and Analytics in Data Structures Learning Modules

数据结构学习模块中的可视化、评估和分析

• DOI: 10.1145/3159450.3159460
• 发表时间: 2018
• 期刊: SIGCSE '18 Proceedings of the 49th ACM Technical Symposium on Computer Science Education
• 作者: Mcquaigue, Matthew;Burlinson, David;Subramanian, Kalpathi;Saule, Erik;Payton, Jamie
• 通讯作者: Payton, Jamie

Bringing Real-World Data and Visualizations of Student-Implemented Data Structures into Sophomore CS Courses Using BRIDGES

使用 BRIDGES 将真实世界数据和学生实现的数据结构的可视化引入大二计算机科学课程

• DOI: 10.1145/3287324.3287536
• 发表时间: 2019
• 期刊: Proceedings of the 50th ACM Technical Symposium on Computer Science Education
• 作者: Subramanian, Kalpathi;Payton, Jamie;Saule, Erik
• 通讯作者: Saule, Erik