NRT-IGE: Information Infrastructure for Society: Integrating Data Science and Social Science in Graduate Education and Workforce Development

这个国家科学基金会研究培训（NRT）奖在研究生教育创新（IGE）轨道马里兰州大学将试点创新，跨学科的课程，整合数据科学与社会科学。该项目将通过将社会科学家开发的工具包和分析框架与计算机科学和统计领域开发的新型数据、模型和工具相结合，促进对人类行为动态的科学理解。 该课程提供了一种创新的方法来训练学生创造性和科学地使用越来越多的可用数据。 它涵盖了研究项目的所有阶段，解决社会问题，包括问题的制定，数据收集，操作，处理和分析。该项目试点了一个创新的模块化培训计划，该计划针对传统和非传统研究生的特定需求量身定制，并为未来的雇主和现有雇主提供直接途径。该项目促进了对如何最好地培养STEM领域（特别是社会科学）的学生和在职专业人士的理解。 该项目测试了在其他领域取得成功的教学方法，即模块化“纳米”课程，侧重于基于具体社会问题的体验式学习，并以社会科学和计算机科学人员之间的同行学习为特色。由于纳米课程中的项目和问题是与联邦、州和地方机构一起设计的，学生将有机会测试并向当前和未来的雇主展示他们的知识。通过分析参与者对所提供材料的反应、提高资格方面的学习成果、在各自工作中使用所学材料、对工作绩效的影响以及对回报率的估计来评估该方案的有效性。数据管理，管理，询问和集成工具被内置到一个数据设施系统中，在该系统中促进协作，并且可以被其他课程采用者复制和使用。 一系列的小型随机实验将允许学习模块的实证测试，并根据结果改进它们。 该项目的总体成果可以由美国的机构进行调整，与该机构的国际合作伙伴开展的外联活动计划将允许更广泛的转移。NSF研究培训计划（NRT）旨在鼓励开发和实施大胆的，新的潜在变革性的STEM研究生教育培训模式。研究生教育创新轨道专门致力于试点，测试和评估新颖，创新和潜在的变革性研究生教育方法。

This National Science Foundation Research Traineeship (NRT) award in the Innovations in Graduate Education (IGE) Track to the University of Maryland will pilot an innovative, cross-disciplinary curriculum that integrates Data Science with Social Science. The project will advance the scientific understanding of the dynamics of human behavior by integrating the toolkits and analytical frameworks developed by social scientists with the new types of data, models and tools developed in the fields of computer science and statistics. The curriculum provides an innovative approach to training students in the creative and scientific use of the growing number of available data. It covers all phases of a research project, addressing social issues including problem formulation, data collection, manipulation, processing, and analysis. The project pilots an innovative modular training program that is tailored to fill specific needs of both traditional and nontraditional graduate students, and provides direct pathways both to future employers and to advancement with current employers.The project advances the understanding of how best to train students and working professionals in the STEM fields, particularly social science. The project tests a pedagogical approach that has been successful in other areas, namely, modular "nano" classes that are focused on experiential learning based on specific social problems and feature peer-to-peer learning between persons in the social sciences and computer science. Because the projects and problems to be worked on within the nano classes are designed together with federal, state, and local agencies, students will have the opportunity test and showcase their knowledge to current and future employers. The effectiveness of the program is evaluated by analyzing the reactions of the participants to the material provided, learning outcomes in terms of improved qualifications, use of learned material in their respective jobs, effects upon job performance, and estimates of rate of return. The data curation, management, interrogation and integration tools are built into a data facility system, within which collaboration is fostered, and which can be replicated and used by other curriculum adopters. A series of small randomized experiments will allow the empirical test of learning modules and improve them based on the results. The overall result of the project can be adapted by institutions in the US, and a program of outreach activities with the institution's international partners will allow an even broader transfer.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The Innovations in Graduate Education Track is dedicated solely to piloting, testing, and evaluating novel, innovative, and potentially transformative approaches to graduate education.