Collaborative Research: Frameworks for Intelligent Adaptive Experimentation: Enhancing and Tailoring Digital Education

合作研究：智能自适应实验框架：增强和定制数字教育

• 批准号: 2209819
• 负责人: John Stamper
• 金额: $ 220万
• 依托单位: Carnegie-Mellon University
• 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: 2022-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209819&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Intelligent; Adaptive

People are constantly learning – whether formal education of homework problems & videos, or reading websites like Wikipedia. This project develops the Experiments As a Service Infrastructure (EASI), which lowers the barriers to conducting randomized experiments that compare alternative ways of designing digital learning experiences, as well as analyzing the data derived from the systems to rapidly change what future people receive. It does this by bringing together multidisciplinary researchers around the shared problem of testing ideas for improving and personalizing educational resources. The research also advances (1) the science of learning and instruction; (2) methods for analyzing complex educational data, and (3) machine learning algorithms that use data to improve educational experiences. Improving learning and teaching increases people's knowledge and gives them the ability to solve problems they care about, driving their personal and career success and increasing society's human capital.Instructional decisions about digital educational resources impact all students, from practice problems in K12 systems to tutorial webpages in university and community college online courses. The current versions of resources are too infrequently compared against alternative resources, which may provide better learning. With this in mind, the project has the goal of using data to test hypotheses about what is most helpful to students, and then use that data to change the experience for future students. The Experiments-As-a-Service-Infrastructure supports three complementary types of multi-disciplinary, collaborative research. A–Design: the infrastructure helps researchers investigate theories of learning and discover how to improve instruction by designing randomized field experiments on components of real-world digital educational resources. This provides more ecologically valid research on learning and instruction, in subfields of education, psychology, policy and discipline-based education research. B–Analysis: the infrastructure facilitates sophisticated analysis of experiments in the context of large-scale data about student profiles, such as to discover which interventions are effective for different subgroups of students. This can advance the use of innovative data-intensive methods for gaining actionable knowledge in education, learning analytics, educational data mining, and applied statistics. C–Adaptation: the infrastructure enables research into adaptive experimentation by providing a testbed for algorithms that dynamically analyze data from experiments, to enhance learning by presenting future students with whichever version of a resource (condition) is more effective, or to personalize learning by presenting different subgroups of future students with the version of a resource that is most effective for their subgroup. The infrastructure provides a testbed for empirical evaluation of which algorithms enact effective adaptive experimentation in education to inspire the development of new algorithms. Finally, the work aligns many educational communities around the shared problem of enhancing and personalizing education through experimentation and spurs multidisciplinary research by providing extensive support for collaboration and sharing of designs, data, analysis scripts and algorithms while fostering an online community for training and collaborations, to promote high-quality, innovative, impactful experiments.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.

人们总是在不断地学习——无论是家庭作业问题和视频的正规教育，还是阅读维基百科这样的网站。该项目开发了实验即服务基础设施（EASI），它降低了进行随机实验的障碍，这些实验比较了设计数字学习体验的替代方法，并分析了来自系统的数据，以快速改变未来人们接受的内容。它通过将多学科研究人员聚集在一起，共同解决测试改进和个性化教育资源的想法的问题。该研究还促进了(1)学习和教学的科学；(2)分析复杂教育数据的方法；(3)利用数据改善教育体验的机器学习算法。改善学习和教学增加了人们的知识，使他们有能力解决他们关心的问题，推动他们的个人和事业成功，增加社会的人力资本。关于数字教育资源的教学决策影响着所有学生，从K12系统的实践问题到大学和社区学院在线课程的教程网页。当前版本的资源很少与可能提供更好学习的替代资源进行比较。考虑到这一点，该项目的目标是使用数据来测试对学生最有帮助的假设，然后使用这些数据来改变未来学生的体验。实验即服务基础设施支持三种互补类型的多学科合作研究。A-Design：基础设施帮助研究人员调查学习理论，并发现如何通过在现实世界数字教育资源的组成部分上设计随机现场实验来改进教学。这为教育、心理学、政策和基于学科的教育研究的子领域提供了更多生态学上有效的学习和教学研究。b -分析：基础设施有助于在学生档案的大规模数据背景下对实验进行复杂的分析，例如发现哪些干预措施对不同的学生群体有效。这可以促进创新数据密集型方法的使用，以获得教育、学习分析、教育数据挖掘和应用统计方面的可操作知识。c -适应性：基础设施通过为动态分析实验数据的算法提供测试平台，从而实现对适应性实验的研究，通过向未来的学生提供更有效的资源（条件）版本来增强学习，或者通过向未来的学生的不同子组提供对其子组最有效的资源版本来个性化学习。该基础设施为经验评估提供了一个测试平台，以评估哪些算法在教育中实施有效的自适应实验，以激发新算法的开发。最后，这项工作使许多教育社区围绕通过实验加强和个性化教育的共同问题保持一致，并通过为设计、数据、分析脚本和算法的协作和共享提供广泛支持来促进多学科研究，同时培养一个培训和协作的在线社区，以促进高质量、创新和有影响力的实验。该奖项反映了美国国家科学基金会的法定使命，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。