At the core of SEERNet’s mission is the empowerment of researchers to test interventions in diverse digital environments at a larger scale. This approach aims to ensure that the interventions are responsive to the needs of learners from diverse backgrounds. To that end, our DLPs are constantly working to improve their platforms to ensure usability and accessibility for a wide-range of researchers and use scenarios. In this second blog post in a series focusing on updates to our DLPs, Danielle McNamara, Principal Investigator and Executive Director of the Learning Engineering Institute at Arizona State University, shares recent developments within the ASU/Learning@Scale digital learning platform.
Learning@Scale (L@S) is a project at Arizona State University that seeks to develop the foundational infrastructure and protocols that allow researchers within and beyond ASU to access a wide range of student data. Arizona State University is the largest university in the country, and serves a highly diverse population of students. The L@S platform was created with the goal of answering questions of how to enhance learning for every student at ASU, while at the same time providing access to data to researchers who traditionally are not able to access a broad array of data from a diverse student population.
Visiting the L@S website as a potential researcher, the website will allow you to make a data request, as well as access a data dictionary which describes available data. The L@S data warehouse compiles student data from multiple sources across ASU into tables that are readily accessible to support researchers’ data requests. Currently, there are 3 primary datasets within the ASU L@S data warehouse: 1) student profile – which provides person-level demographics, 2) student trajectory – organized by student over time, and 3) course profile – organized by class term.
In addition to the three primary datasets, there are two datasets that leverage natural language produced in various course contexts, such as: 1) discussion boards and 2) written assignments. The L@S team is currently working on a variety of natural language processing analyses to examine the extent to which they can share this data while maintaining the privacy of individuals. In particular, the team is working to ingest the discussion board posts and written assignments and analyze them to see if releasing just the linguistic and semantic features of the text is sufficient for potential data analyses.
Now in year three of the project, L@S has completed much towards accomplishing their goals:
In December 2023, ASU hosted the inaugural meeting of the Learning Engineering Research Network (LERN) to discuss L@S, as well as the Learning Engineering Institute. The Learning Engineering Institute is a new research institute that was launched at ASU in 2024, and draws on behavioral, design, computer, and data sciences to advance research and development around educational technology to develop data-informed, technologically enhanced innovations in learning and instruction. Developing L@S and building a robust data infrastructure was the necessary element before launching LEI, which now supports researchers in conducting A/B studies, and works with faculty and instructors in their classrooms.
Learning@Scale was recently awarded an IES Transformative Research in the Education Sciences grant, in collaboration with Terracotta, another SEERNet DLP. This new project aims to solve the challenge that higher education learners often need access to course materials and just-in-time learning activities while on the go. In response to this challenge, Active L@S will partner with a mobile technology app, INFLO, to allow learners access to course content and note taking while not at their computer. The INFLO app will link to Canvas LTI tool, Terracotta and Learning@Scale. The app will include active learning features and algorithms that assess the quality of summaries and self explanations as well as questions to support students’ engagement with content whether they are on the go or at their computer.
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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.
