Areas of Work

Defining the new way to work. These major areas of work are integrated into all our projects to create new knowledge and advance theories with long-term impact on educational quality.


We believe assessment design and practice are the critical linchpins to understanding and improving the quality of education and training. Our priority is assessing the current quality, while gaining strong evidence for how to improve.

We measure goals and competencies that are needed for future success by developing a deep understanding of content through communication, collaboration, and innovation. We ensure our measures fully represent intended goals to determine where training, teaching, and learning systems align.

We exert intellectual leadership in the design, analyses, and use of assessment and assessment systems, as we formulate performance-based assessments and create general models. Our work consistently results in cognitively complex measures of high technical quality, allowing us to apply accurate assessment knowledge to solve real-world problems.


Evaluation is the vital key for increasing and improving learning and effectiveness in evidence-based practice. We use state-of-the-art technology to expand and refine our evaluation practice, incorporating new methods and delivering existing methods more efficiently. We use innovative designs and instrumentation to produce the best evidence about what programs are working, why they are succeeding, and how to improve them even further.

We bring together the practicalities of teaching and training, and the experts in technical methodology and engineering to yield valid data and actionable results. Our current work combines new measures of program quality, innovative approaches for detecting complex performance outcomes, and the synthesis of existing research to inform instructional design.


We are redefining psychometric, statistical, and research design approaches to meet today’s demands for individualized learning, dynamic assessment contexts and just-in-time data and results that better serve the information needs at multiple levels of policy and practice.

We implement techniques such as Bayesian inference, neural networks, data mining, and machine learning. Our researchers have expertise in computer adaptive testing, item response theory, covariance structure analysis, meta-analyses, hierarchical modeling, causal analysis, computational statistics, and natural language processing (NLP).

Our methodological contributions are routinely featured in leading journals and used by statisticians and psychometricians around the world.


We push forward on the frontiers of learning and assessment by using technology to capture data in new and unobtrusive ways. We capitalize and build on the possibilities of crowdsourcing and data mining to make current assessment and evaluation options more efficient and cost-effective. And we apply emerging technologies and explore their potential in the administration and interpretation of assessments, in the dissemination of our products, and in stimulating communication with policymakers, practitioners, and the public. With innovative games and simulations that support future learning, we are making significant contributions to the national research and development agenda.

Learning Design

The key to using assessment and evaluation to improve learning and educational effectiveness is grounding them in a comprehensive understanding of learning, as well as how it is expected to develop. Detailed learning models and expected learning progressions provide the foundation for all of our design, development and validation efforts.

In pursuing learning-based designs, we also advance our understanding of how learning develops in a domain and how to best support its advancement. The design of our instruction, assessments, games, and simulations focuses on deep understanding and problem solving.

Because our designs are sensitive to the relationship between classroom practice, policy, and targeted learning outcomes, we can successfully scale up our learning model-based research to create innovative assessment, learning, and evaluation systems for every age and type of program.

Data Use

We are turning data into actionable information that provides solutions to the most pressing problems in education. Of course, data doesn’t accomplish it alone—people are an integral component. Our work in this area marshals technical data expertise with communication, collaboration, and organizational development. We’ve applied our knowledge to a broad range of projects, focusing on the implementation, evaluation, and improvement of accountability systems that support state and district efforts to improve student learning at all levels.