Collaborative Research: Diversifying Human-Centered Data Science through the Research and Design of Ethical Games

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。解决数据科学和机器学习中的压倒性问题需要新的方法来培训和结合社会科学，以人为本的设计，信息研究，统计学，计算机科学和相关领域的专业知识。我们邀请跨学科感兴趣的本科生合作研究和设计一个关于数据伦理问题的严肃游戏。在这种情况下，我们的项目研究了具有冲突的方法文化和不同背景的同龄人群体中的协作学习。参与的学生将来自美国不同地区的两个大学校园，组成一个由不同个人组成的团队，在虚拟环境中合作。通过研究学生在整个项目中的经验，我们将增加我们对学习的理解，通过合作随着时间的推移，并与他们的社会文化历史。这个为期两年的跨学科项目的另一个工件将是一个教育游戏的原型，针对代表性不足的社区和广大公众，这将使更多的人参与到关于人工智能和数据科学在我们社会中的作用的关键对话中。我们研究的主要目的是在面对不同背景的协作学习中产生学习科学的新理论，文化规范学生们如何在数据伦理中单独和集体地定位自己，这些观点是否会随着时间的推移而改变？学生如何在一个设计项目的设计中协调冲突的规范？伦理模拟策略类游戏的协同设计是否促进了学生多样化的学习体验？我们的工作将作为一个民族志案例研究进行，分析大量的社会文化历史理论。在项目过程中将收集多个数据流，包括“实地”观察，即团队会议（包括视听记录和研究人员的实地记录），与参与者的非正式对话，团队创建的产品（如论文，图像，原型等），简短的调查和参与者访谈。这项工作旨在为学习科学的研究机构提供关于协作学习的新理论，并为未来位于文化规范冲突的学科之间的教育项目提供案例研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力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 award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Tackling the overwhelming issues in data science and machine learning require new ways of training and combining expertise from social science, human-centered design, information studies, statistics, computer science, and related fields. We invite interested undergraduates across disciplines to collaborate on the research and design of a serious game on data ethics issues. In this context, our project studies collaborative learning in groups of peers with conflicting methodological cultures and diverse backgrounds. Participating students will come from two university campuses in different regions of the United States to form a single team of diverse individuals collaborating in a virtual setting. By studying students’ experiences throughout the project, we will increase our understanding of learning through collaboration over time and in relation to their sociocultural histories. An additional artifact of this two-year, interdisciplinary project will be a prototype of an educational game, targeted towards the underrepresented communities and the greater public, that will bring more people into critical conversations about the roles of AI and data science in our society.The primary aim of our research is the production of novel theories in learning sciences regarding collaborative learning in the face of diverse backgrounds and cultural norms. How do students individually and collectively position themselves within data ethics and do these views change over time with increased exposure to technical concepts and norms? How do students reconcile conflicting norms in the design of a single design project? Does the collaborative design of an ethical simulation game facilitate diverse student learning experiences? Our work will be conducted as an ethnographic case study with analysis heavily informed by sociocultural-historical theory. Multiple streams of data will be collected over the course of the project, including observations in the “field,” i.e. of the team meetings (consisting of both audiovisual recordings and researchers’ field notes), informal conversations with participants, products created by the team (such as papers, images, prototypes, etc), brief surveys, and participant interviews. This work aims to contribute new theories on collaborative learning to the body of research in learning sciences as well as serve as a case study for future educational projects situated between disciplines with conflicting cultural norms.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.