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.
