SEERNet is a network of platform developers, researchers, and education stakeholders working together to create and expand the capacity of digital learning platforms (DLPs) to enable equity-focused and rigorous education research. SEERNet is funded by the Institute of Education Science, with a deliberate intention to fund both DLPs and researchers, as well as a growing network connecting DLPs, researchers, and practitioners. It is called “SEERNet” in reference to IES’s SEER principles, a set of aspirations for educational research.
In every school, dedicated teachers strive to support their students’ unique learning journeys. Imagine a classroom where potential challenges are quickly identified and met with precise interventions. Imagine a school or school district where the many potential ways to solve problems can be quickly tested, and the best solutions rapidly scaled up across the district. We explore how this vision can become a reality through the integration of Improvement Science with SEERNet’s data and research capabilities. Improvement Science offers a structured approach to identifying and solving problems. SEERNet—a network of digital learning platforms, researchers, and educators—provides a method to use evidence to compare alternative approaches to supporting students on the basis of detailed data from students’ experiences in digital learning platforms. However, this vision cannot be realized in isolation. Collaboration between researchers and practitioners is vital for improving student outcomes. Researchers contribute theoretical knowledge and empirical skills, while practitioners bring on-the-ground professional experience and knowledge about what works for their students. Working together, they can advance how educational technologies are used for student learning in ways that are research based, practical and relevant. This white paper explores how and why SEERNet could be combined with Improvement Science methodologies. We delve into the collaborative power of Networked Improvement Communities (NICs), a core method in Improvement Science. We then examine the dynamic interplay between SEERNet’s approach and Improvement Science. A scenario illustrates how a school district could use Terracotta, a platform that enables research within a popular LMS, to address reading comprehension barriers in STEM subjects for English learners and students with disabilities. Researchers and teachers collaborate to test assignment modifications, such as adding text-to-speech tools and steps to clarify questions. Using iterative Plan-Do-Study-Act (PDSA) cycles, they refine these strategies based on data, resulting in improved outcomes. The paper concludes with five recommendations: fostering collaboration, enhancing data sharing, leveraging root cause analysis, implementing iterative improvements, and scaling successful interventions.
One of SEERNet’s aims is to enable alignment of research on digital learning platforms to practitioner needs. Office Hours: A Conversational Series was SEERNet’s first initiative to convene practitioners and researchers in identifying problems of practice relevant to digital learning platforms (DLPs) and co-designing research questions. Participants discussed their experiences, goals, challenges, and vision related to DLP use. The conversations were synthesized and feedback from the participants led to refined research questions. In addition, we asked the participants to reflect on the value of the Office Hours process.
The goal of the Statistical and Research Methodology in Education (Stats/Methods) grant program (ALN 84.305D) is to provide a wide range of methodological and statistical tools that will better enable applied education scientists to conduct rigorous education research. Through this grant program, IES invites applications to develop new methodological approaches, to extend and improve existing methods, and to create other tools that can enhance the ability of researchers to conduct high quality, scientific education research.
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This project is supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305N210034 to Digital Promise. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.