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: 2209821
• 负责人: Jeffrey Carver
• 金额: $ 40万
• 依托单位: University of Alabama Tuscaloosa
• 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=2209821&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.

人们一直在学习 - 无论是家庭作业问题和视频的正规教育，还是阅读Wikipedia等网站。该项目将实验作为服务基础架构（EASI）开发，该项目降低了进行随机实验的障碍，以比较设计数字学习经验的替代方式，并分析了从系统中得出的数据来快速改变未来人的收到的东西。它通过将多学科研究人员汇总到测试改进和个性化教育资源的共同问题的共同问题来实现这一目标。该研究还提高了学习和指导的科学； （2）分析复杂教育数据的方法，以及（3）使用数据来改善教育经验的机器学习算法。改善学习和教学可以提高人们的知识，并使他们能够解决他们关心的问题，推动其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了所有学生，从K12系统的实践问题到大学和社区大学在线课程的教程网页。当前的资源版本很少与替代资源进行比较，这可能提供更好的学习。考虑到这一点，该项目的目标是使用数据来测试有关最有帮助的学生的假设，然后使用该数据来改变未来学生的体验。实验-As-Service-Infrasture支持三种完整类型的多学科，协作研究。 A – DeSign：基础架构可帮助研究人员通过设计对现实世界数字教育资源组成部分的随机现场实验来研究学习理论，并发现如何改善教学。这为基于教育，心理学，政策和基于学科的教育研究的子领域提供了有关学习和教学的生态有效研究。 B - 分析：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实验的复杂分析，例如发现哪些干预措施对学生的不同亚组有效。这可以推动使用创新的数据密集型方法来获得教育，学习分析，教育数据挖掘和应用统计的可行知识。 C - 适应：基础架构通过为算法提供测试床的研究，从而可以对自适应实验进行研究，以动态分析实验的数据，从而通过向未来的学生展示资源的哪个版本（条件）来增强学习，或者通过为未来的学生提供最有效的资源的不同子组来个性化的学习，以实现他们的个性化学习。基础架构最终为测试台提供了一个测试床，工作对齐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，并通过为设计，数据，分析脚本和算法的协作和共享的协作提供广泛的支持，以促进培​​训和协作的培训和协作，以促进高级实验，以促进型号的实验，以促进培​​训，促进型号的实验，以促进培​​训，以促进高级实验。并使用基金会的智力优点和更广泛的影响审查标准通过评估来诚实地获得支持。