Synthesis and Design Workshop: Digital Science and Data Analytic Learning Environments at Small Liberal Arts Institutions

本次研讨会由“亲爱的同事来信：数字科学、技术、工程和数学(STEM)学习环境的设计原则”(NSF 18-017)资助。在快速技术变革的推动下，美国面临着科学、技术、工程和数学(STEM)专业的严重劳动力短缺。此外，所有领域的雇主都在越来越多地寻找拥有计算思维、数据科学和分析技能的员工。全国80%的高校被归类为小型院校，招生人数不超过5000人。许多这样的小型学院和大学都以文科为基础，并有在其通识教育课程中灌输个性化学习和跨学科的共同思想的传统。这使得它们特别适合培养学生的人文素养、技术素养和数据素养。由于计算思维、数据科学和分析工作通常是在数字环境中进行的，因此学生必须在数字环境中学习这些技能，以便为他们在工作场所遇到的情况做好准备。学生将使用包含五个领域(互操作性；个性化；分析、建议和学习评估；协作；可访问性和通用学习设计)的下一代数字学习环境来学习数据科学和分析技能。小型学院和大学往往缺乏财力来建设必要的人力和技术基础设施，以支持数字学习。该研讨会将设计数字学习环境，以满足对受过数据科学和分析教育的专业人员的实质性需求，促进学习公平，并帮助小型文科学院和大学为这些努力的早期发展做出贡献。专家将建议团队设计数字科学和分析课程，这些课程将被纳入满足学习者和劳动力需求的下一代数字学习环境的蓝图设计中。该项目的目标是召开一次研讨会，以产生下一代数字学习环境的蓝图设计，以回答以下问题-如何设计科学、技术和数学数字学习环境，以增强小型文科高等教育机构学习者的数字科学和数据分析技能能力？研究问题包括：1)创新的数字学习环境将如何为需要数据科学和分析的学生的就业做好准备？2)数据科学和分析数字学习环境的设计将如何考虑学习者的多样性？3)如何收集数据和评估学习环境以衡量学生的数据科学和分析能力？4)国家联盟将如何形成和发挥作用，以维持和扩大研讨会的成果？基于小型文科院校提供个性化学习的传统，将强调针对技术的通用设计学习，并作为支持STEM中更多代表不足的学生坚持到毕业的典范。数据科学和分析技能的复杂性要求评估研讨会参与者在实现计划结果方面的进展，以及关于个人动机的教育，这两者都是通过团队科学完成的。团队科学也被创新性地融入到数据科学和分析本科课程设计中，以教育学生他们的技能如何能够为更大的产品做出贡献，这是多学科团队集体贡献的结果。该项目利用了国家专家的专门知识，他们提供了小型文科学院和大学没有的内容知识。小型文科学院和大学数字学习国家联盟的成立将维持和扩大讲习班的成果。它将通过课程创新向学生介绍计算思维、数据科学和分析技能，提供真实的动手数字学习研究体验。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This workshop is funded through the "Dear Colleague Letter: Principles for the Design of Digital Science, Technology, Engineering, and Mathematics (STEM) Learning Environments (NSF 18-017)." Driven by rapid technological change, the United States is facing a critical workforce shortage in science, technology, engineering, and mathematics (STEM) professions. Furthermore, employers across all fields are increasingly seeking employees who possess computational thinking and data science and analytics skill sets. Eighty percent of the nation's colleges and universities are classified as small institutions and enroll 5,000 or fewer students. Many of these small colleges and universities are grounded in the liberal arts and have a tradition of infusing personalized learning and common thought across disciplines within their general education curricula. This makes them particularly well-suited for preparing students in human literacy, technological literacy, and data literacy. Because computational thinking and data science and analytics work is most often conducted digitally, it is imperative that students learn these skills in digital environments to prepare them for what they will encounter in the workplace. The next generation of digital learning environments encompassing five areas (interoperability; personalization; analytics, advising, and learning assessment; collaboration; and accessibility and universal learning design) will be used by students to learn data science and analytics skill sets. Small colleges and universities often lack the financial resources to build the necessary human and technological infrastructure to support digital learning. This workshop will design digital learning environments that will meet the substantial need for data science and analytics-educated professionals, promote equity in learning, and assist small liberal arts colleges and universities to contribute to the early development of these efforts. Experts will advise teams to design digital science and analytics curricula that will be incorporated in blueprint designs for next generation of digital learning environments meeting the needs of learners and the workforce. The goal of the project is to convene a workshop that results in blueprint designs of next generation of digital learning environments to answer the question - How can science, technology, and mathematics digital learning environments be designed to enhance the digital science and data analytic skill competencies of learners at small liberal arts institutions of higher education? The research questions include: 1) How will the innovative digital learning environments prepare students for employment that requires data science and analytics? 2) How will the design of data science and analytics digital learning environments account for the variability of learners? 3) How will data be collected and learning environments assessed to measure students' data science and analytics competency? 4) How will a national consortium form and function to sustain and expand the workshop outcomes? Based on the tradition in small liberal arts colleges and universities of offering personalized learning, universal design learning for technology will be emphasized and serve as a model for supporting more underrepresented students in STEM to persist to graduation. The complexity of data sciences and analytics skill sets require an assessment of the workshop participants' progress toward meeting the planned outcomes, and education on individual motivations, both which are done through team science. Team science is also innovatively incorporated into the data science and analytics undergraduate curriculum design to educate students how their skills can contribute to a greater product that is the result of the collective contribution of the multidisciplinary team. The project draws upon the expertise of national experts who provide content knowledge not otherwise available to small liberal arts colleges and universities. The forming of a national consortium for digital learning at small liberal arts colleges and universities will sustain and expand the workshop outcomes. It will provide authentic hands-on digital learning research experience through curriculum innovations that introduce students to computational thinking, data science, and analytic skills. The result will be prepared students who fit the workforce need in this area.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.