Envisioning LASSO as National Infrastructure: Scaling Cognitive Diagnostics and Unlocking Equitable Learning Data

• In Event: Toward a National Infrastructure for Equitable, AI-Enhanced Learning Diagnostics in STEM Education

Abstract

1. Objectives or Purposes
This presentation envisions the LASSO platform as a national infrastructure that supports large-scale, equity-focused learning research and practice. LASSO has already become a widely used system for administering research-based assessments and gathering high-quality student learning data. Our vision expands this role, positioning LASSO as a national laboratory for educational research that (1) scales cognitive diagnostic (CD) assessments across disciplines, (2) provides timely, actionable data to instructors, and (3) supports research-practice partnerships with open, longitudinal, and intersectional data. Future work includes a Mid-Scale Research Infrastructure (MSRI) project to build STEM CDs across grades 9–14 and an NSF ASCEND project integrating AI-powered support selection and tutoring into the platform.

2. Perspective(s) or Theoretical Framework
The LASSO platform is grounded in evidence-centered design (ECD) and informed by frameworks that call for more equitable and actionable educational data. Our work reflects commitments to Critical Quantitative and intersectional research, emphasizing what is measured and how and for whom. We aim to create tools that align with classroom needs, empower instructors, and generate disaggregated data to inform equitable policy and instruction.

3. Methods, Techniques, or Modes of Inquiry
Rather than presenting findings from a single study, this session outlines a developmental mode of inquiry. We treat platform development as iterative design-based research, using data from classroom implementations to refine cognitive diagnostics, build learning trajectories, and identify instructional leverage points. LASSO serves as a testbed for innovative assessment systems that are both research-grade and classroom-ready.

4. Data Sources, Evidence, Objects, or Materials
Over 250 instructors across more than 150 institutions currently use the LASSO platform. It has supported over 20 peer-reviewed publications and received over $4.8M in NSF funding across four grants. Our Mechanics Cognitive Diagnostic (MCD) instrument leads this work, offering fine-grained skill mastery profiles based on weekly diagnostic testing. Instructors can access high-frequency formative data throughout the semester, while researchers gain access to large-scale, de-identified datasets that include demographic information sufficient to support intersectional analysis.

5. Results and/or Substantiated Conclusions or Warrants for Arguments/Point of View
Our central argument is that public research infrastructure is essential for building an equitable, data-informed future of education. LASSO is already fulfilling key components of this role, hosting diagnostic tools, returning real-time insights to instructors, and supporting dozens of published studies. We encourage researchers and instructors to adopt and expand this infrastructure. Future directions include new CDs across the STEM disciplines, longitudinal studies of learning trajectories, and embedding generative AI to provide personalized student support at scale.

6. Scientific or Scholarly Significance of the Study or Work
As educational systems face growing calls for personalized, inclusive, and transparent assessment, platforms like LASSO offer a model for what national infrastructure could look like: open, research-aligned, and classroom-responsive. By combining CD assessments, AI supports, and disaggregated learning data, LASSO opens new pathways for research into learning, equity, and instruction, especially when supported by sustained infrastructure investments.

Authors

• Ben Van Dusen, Iowa State University

• Jayson Nissen, Montana State University

• Jason W Morphew, Purdue University