CIF21 DIBBs: PD: Enhancing and Personalizing Educational Resources through Tools for Experimentation

该项目将自动化随机对照实验（RCE）的创建和数据分析。 RCE是在课堂环境中设计并交付给教师和学生的问题集，可用于比较替代教育策略。这项工作建立在现有的教育平台（ASSISTments）由首席研究员开发，并使用基于模板的方法来提高效率和进行教育研究的可靠性。 我们的目标是降低创造和学习随机对照实验的障碍。该项目有可能促进教育研究中的大规模学习，覆盖数百所学校和数千名学生。该项目建立在该团队先前的两项开发之上。 - ASSISTments是一个在线学习平台，最初旨在为学生提供帮助，为教师提供评估（建立绰号）。 该系统主要用作初中或中学教育的在线辅导系统，支持课堂作业和家庭作业的交付，收集和评分，为学生提供即时反馈，并为教师提供明确的报告。 到目前为止，24个随机对照实验比较教育策略已经使用这个平台发表。 - 此外，AssistmentsTestBed.org是PI和他的团队在单独的NSF拨款（#1440753）下开发的测试平台，以确定教育中的最佳实践，并允许其他研究人员通过该测试平台提出和运行自己的研究，利用ASSISTments作为共享的科学工具。 受益者包括教育研究人员、教师和学生，现有的工具正在500多所学校和50 000多名学生的教育中使用。 目前的项目改进了基础设施的两个组成部分，这两个组成部分在以前的研究中一直是资源密集型的瓶颈。 其中一项任务是通过开发模板工具（在ASSISTments中启用研究），实现研究创建和数据分析过程的自动化。 第二项任务是使统计分析自动化，并提高现有数据报告工具（学习基础设施评估，或ALI）的可用性。 这些改进将部分通过应用教育数据挖掘算法（即，深度知识追踪）在实验之前、期间和之后收集的学生数据上。 这些分析将为研究人员提供协变量，这将显着改善个性化教育的议程。 由此产生的能力将有助于研究人员在课堂环境中设计和向教师和学生提供问题集，提高大规模进行教育研究的效率和可靠性，并简化研究流程。高级网络基础设施办公室的这一奖项由NSF教育和人力资源理事会，正式和非正式环境学习研究部共同支持。

This project would automate the creation and data analysis of randomized controlled experiments (RCEs). RCEs are question sets designed and delivered to teachers and students in a classroom setting, and can be used to compare alternative educational strategies. This effort builds on an existing educational platform (ASSISTments) developed by the Principal Investigator, and uses a template-based approach to increase the efficiency and reliability of conducting educational research. The goal is to lower the barriers to creating and learning from randomized controlled experiments. The project has the potential to facilitate large-scale learning in education research, reaching hundreds of schools and thousands of students.The project builds upon two prior developments by this team. - ASSISTments is an online learning platform originally designed to provide students with assistance and teachers with assessments (establishing the moniker).  The system is used primarily as an online tutoring system for middle or secondary education, supporting the delivery, collection, and grading of classwork and homework, providing immediate feedback for students and explicit reporting for teachers. To date, 24 randomized controlled experiments comparing educational strategies have been published using this platform. - In addition, AssistmentsTestBed.org is a testbed developed by the PI and his group under a separate NSF grant (#1440753), to identify best practices in education and allow other researchers to propose and run their own studies leveraging ASSISTments as a shared scientific instrument through this testbed. Beneficiaries include education researchers, teachers, and students, with the existing tool being used in over 500 schools and in the education of over 50,000 students. The current project improves two components of the infrastructure that have been resource-intensive bottlenecks in prior research. One task automates the process of study creation and data analysis, through development of a Template Tool that enables studies within ASSISTments. A second task automates statistical analyses and improves usability of the existing data reporting tool (Assessment of Learning Infrastructure, or ALI). The improvements will be achieved, in part, by applying educational data mining algorithms (i.e., deep knowledge tracing) on student data collected before, during, and after experimentation. These analytics will provide researchers with covariates that will significantly improve the agenda of personalizing education. The resulting capability will assist researchers as they design and deliver question sets to teachers and students in a classroom setting, increase the efficiency and reliability of conducting educational research at scale, and streamline the research processes.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the NSF Directorate for Education and Human Resources, Division of Research on Learning in Formal and Informal Settings.