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.

人们不断学习——无论是家庭作业问题和视频的正规教育，还是阅读维基百科等网站，该项目开发了实验即服务基础设施 (EASI)，这降低了进行随机实验的障碍，这些实验比较设计数字学习体验的替代方法。 ，以及分析从系统中获得的数据，以快速改变未来人们所接受的内容。它通过将多学科研究人员聚集在一起，围绕测试改进和个性化教育资源的共同问题来实现这一点。该研究还推动了（1）科学的发展。 (2) 方法分析复杂的教育数据，以及（3）使用数据改善学习和教学的机器学习算法增加人们的知识，使他们有能力解决他们关心的问题，推动他们的个人和职业成功并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 系统中的练习问题到大学和社区学院在线课程中的教程网页，当前版本的资源很少与替代资源进行比较，而替代资源可能会提供更好的学习效果。心灵，那个项目的目标是使用数据来测试对学生最有帮助的假设，然后使用该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、 A-设计：该基础设施帮助研究人员研究学习理论，并通过对现实世界数字教育资源的组成部分设计随机现场实验来探索如何改进教学，这为学习和教学的子领域提供了更生态有效的研究。教育、心理学、政策和学科教育B-分析：该基础设施有助于在有关学生概况的大规模数据的背景下进行复杂的实验分析，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。用于获得教育、学习分析、教育数据挖掘和应用统计方面的可操作知识：该基础设施通过提供动态分析实验数据的算法测试平台来支持适应性实验的研究，通过向未来的学生展示增强学习。无论资源的哪个版本（条件）更有效，或者通过向未来学生的不同子群体提供对其子群体最有效的资源版本来个性化学习。该基础设施提供了一个测试平台，用于实证评估哪些算法可以在教育中实施有效的自适应实验。最后，这项工作使许多教育社区围绕通过实验增强和个性化教育的共同问题进行协调，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，同时培育在线培训社区，促进多学科研究该奖项反映了 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。