Collaborative Research: Frameworks: Cyber Infrastructure for Shared Algorithmic and Experimental Research in Online Learning

这个名为RAILKaM的项目将创造新技术，使20名研究人员能够在资助期间进行大规模的实地实验，在K-12数学学习和大学大规模在线开放课程（MOOCs）的背景下研究教育和教育心理学的基本原理。这些实验将通过嵌入学习系统的自适应学习技术进行，这些系统已经被每年超过10万名K-12学生和数十万名MOOC学习者使用。RAILKaM还将支持75名数据科学家在事后对学生数据进行分析，使用精心编辑的数据集来保护学生的隐私。在促进高功率、可复制的实验与不同的学生群体和广泛的测量中，该基础设施提高了在在线学习环境中进行高质量教育研究的效率和便利性，将21世纪的研究方法引入教育，以长期改善学习者的成果。这个名为RAILKaM的项目将支持研究人员更容易地在K-12和大学大规模在线开放课程（MOOCs）中进行大规模、高仪器化的教育和教育心理学研究。RAILKaM将利用ASSISTments，这是一个用于中学数学作业和课堂作业的在线学习平台，每年有超过10万名学生使用。此外，RAILKaM将在ASSISTments平台之上构建功能，以便将涉及脚手架解决问题的教育实验轻松构建到MOOC课程中。ASSISTments将使用开源api与宾夕法尼亚大学提供的MOOC集成，将调查能力扩展到高等教育，同时实现比MOOC课程更丰富的学生互动和数据收集。这些能力将使研究人员能够运行在线现场实验，以测试旨在提高学生学习和参与的干预措施，重点是如何优化适应性学习经验。这些实验将通过对学习者的丰富数据收集，扩展MOOC日志数据和ASSISTments数据，其中包含以前无法用于大规模研究的学习和参与的几个指标。该项目将开发进行实验和收集丰富数据所需的软件基础设施，以及选择和完善学习想法所需的社会基础设施，同时保持教师对学生体验的活动的控制。软件和社会基础设施的结合将使我们能够与对这些问题感兴趣的研究人员接触，但他们目前缺乏基础设施、技术能力或接触到进行高强度或复杂随机对照试验所必需的学习者。这个基础设施将帮助这些研究人员提高对人类学习原理的科学理解，为学习科学家提供一个独特的共享资源，这将有相当大的潜力产生更广泛的影响。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。

This project, RAILKaM, will create new technology that will enable twenty researchers during the grant period to run large-scale field experiments where they study basic principles in education and educational psychology in the context of both K-12 mathematics learning and university Massive Online Open Courses (MOOCs). The experiments will be delivered through adaptive learning technology embedded in learning systems already being used by over 100,000 K-12 students and hundreds of thousands of MOOC learners each year. RAILKaM will also support 75 data scientists in conducting analyses on student data after the fact, using carefully redacted datasets that protect student privacy. In facilitating high-power, replicable experiments with diverse student populations and extensive measurement, this infrastructure increases the efficiency and ease of conducting high-quality educational research in online learning environments, bringing 21st-century research methods to education for the long-term betterment of learner outcomes.This project, RAILKaM, will support researchers in more easily running scaled, highly instrumented studies on education and educational psychology, both in K-12 and university Massive Online Open Courses (MOOCs). RAILKaM will leverage ASSISTments, an online learning platform for middle school mathematics homework and classwork used by more than 100,000 students each year. In addition, RAILKaM will build functionality atop the ASSISTments platform so that educational experiments involving scaffolded problem-solving can be easily built into MOOC courses. ASSISTments will use open source APIs to integrate with MOOCs offered by the University of Pennsylvania, branching capacity for investigation to higher education while enabling richer student interactions and data collection than is typically feasible in MOOC courses. These capacities will enable researchers to run online field experiments to test interventions designed to increase student learning and engagement with a focus on how adaptive learning experiences can be optimized. These experiments will be augmented by rich data collection on learners, extending MOOC log data and ASSISTments data with several indicators of learning and engagement not previously available for research at scale. This project will develop the software infrastructure necessary to conduct experiments and collect enriched data, as well as the social infrastructure necessary to select and refine study ideas while maintaining instructor control over the activities that students experience. The combined software and social infrastructure will enable us to engage with researchers who are interested in these issues but who currently lack the infrastructure, technical capacity, or access to learners necessary to conduct high-powered or complex randomized controlled trials. This infrastructure will help these researchers to improve scientific understanding of the principles of human learning, providing a unique shared resource for learning scientists that will have considerable potential for broader impact.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.