Projects

This project seeks to strengthen statistics and data science instruction in grades 6–12 through the design and implementation of an online professional learning environment for teachers. In partnership with RTI International, the InSTEP project designed and launched instepwithdata.org, an online professional learning platform that supports in-service teachers in developing both deeper content knowledge in statistics and the pedagogical expertise needed to teach statistics and data science effectively in their classrooms.

InSTEP is intentionally designed as a flexible professional learning experience with no fixed sequence of completing activities and modules. Teachers can chart their own learning pathways, engaging with selected resources or the full collection of materials based on their interests and needs. This flexibility allows educators to work at their own pace while deepening their understanding of key aspects of classroom practice, including selecting meaningful data and statistics tasks, facilitating rich classroom discourse, and making thoughtful choices about technology tools. 

InSTEP provides two primary types of learning experiences:

Self-Paced Modules. These modules support focused exploration of 7 individual dimensions, helping teachers strengthen both their statistical content knowledge and instructional practice. Together the 7 interconnected dimensions characterize effective learning environments for teaching data science and statistics, as shown in the accompanying diagram. As of January 2026, the platform includes 15 modules spanning the seven dimensions.

Data Investigations. These inquiry-oriented experiences immerse teachers in working with real-world, multivariate datasets using data visualization tools. Each investigation is situated in an authentic context and engages teachers in core Data and Statistical Practices and Central Statistical Ideas. Investigations are organized around the Data Investigation Process (Lee et al., 2022), represented in the puzzle-piece figure. As of January 2026, there are six investigations available.

This project will develop a cloud-based platform that enables high school students, teachers, and scientists to conduct original neuroscience research in school classrooms.

The aim of this project is to enact and study a process in which middle school teachers of mathematics and visual arts co-design and teach activities that combine math and art to teach data science.

This project will provide structured and meaningful scaffolds for teachers in examining two research-based teaching strategies hypothesized to positively impact mathematics achievement in the areas of mathematical modeling and problem solving. The project investigates whether the order in which teachers apply these practices within the teaching of mathematics content has an impact on student learning.

This project aims to enact and study the co-design of classroom activities by mathematics and visual arts teachers to promote middle school students' data literacy.

The aim of this project is to enact and study a process in which middle school teachers of mathematics and visual arts co-design and teach activities that combine math and art to teach data science.

This project will address the pressing national need to generate shared, practice-based knowledge about how to implement freely available, high-quality instructional resources (mathematics formative assessment lessons) that have been shown to produce significant gains in student learning outcomes. It will expand a professional development model (Analyzing Instruction in Mathematics using the Teaching for Robust Understanding Framework (AIM-TRU)) that supports teacher learning about effective lesson implementation.

This project will investigate the professional development supports needed for teaching bioinformatics at the high school level. The project team will work with biology and mathematics teachers to co-design instructional modules to engage students with core bioinformatics concepts and computational literacies, by focusing on local community health issues supported through mobile learning activities. The overarching goal of the project is to help create an engage population of informatics-informed students who are capable of critically analyzing information and able to solve local problems related to their health and well-being.

The goal of this project is to formalize subjective ideas about the important concept of replication, provide statistical analyses for evaluating replication studies, provide properties for evaluating the conclusiveness of replication studies, and provide principles for designing conclusive and efficient programs of replication studies.

This project will examine the impact of a 12-year statewide science field trip program called LabVenture, a hands-on program in discovery and inquiry that brings middle school students and teachers across the state of Maine to the Gulf of Maine Research Institute (GMRI) to become fully immersed in explorations into the complexities of local marine science ecosystems.

This project is developing and studying high school curriculum modules that integrate social justice topics with statistical data investigations to promote skills and interest in data science among underrepresented groups in STEM.

This project builds upon the prior work by creating problem-solving measures for grades 3-5. The elementary assessments will be connected to the middle-grades assessments and will be available for use by school districts, researchers, and other education professionals seeking to effectively measure children's problem solving. The aims of the project are to (a) create three new mathematical problem-solving assessments and gather validity evidence for their use, (b) link the problem-solving measures (PSMs) with prior problem-solving measures (i.e., PSM6, PSM7, and PSM8), and (c) develop a meaningful reporting system for the PSMs.

This project will design, develop, and test a new curriculum unit for high school chemistry courses that is organized around the question, "How does chemistry shape where I live?" The new unit will integrate relevant Earth science data, scientific practices, and key urban environmental research findings with the chemistry curriculum to gain insights into factors that support the approach to teaching and learning advocated by current science curriculum standards.

This project explores how secondary mathematics teachers can plan and enact learning experiences that spur student curiosity, captivate students with complex mathematical content, and compel students to engage and persevere (referred to as "mathematically captivating learning experiences" or "MCLEs"). The study will examine how high school teachers can design lessons so that mathematical content itself is the source of student intrigue, pursuit, and passion. To do this, the content within mathematical lessons (both planned and enacted) is framed as mathematical stories and the felt tension between how information is revealed and withheld from students as the mathematical story unfolds is framed as its mathematical plot.

This project will build on prior funding to design a next generation diagnostic assessment using learning progressions and other learning sciences research to support middle grades mathematics teaching and learning. The project will contribute to the nationally supported move to create, use, and apply research based open educational resources at scale.

This project will address the need for high quality evidence-based models, practices, and tools for high school teachers and the development of students' problem solving and analytical skills by leveraging novel research and design approaches using digital tools and two well-established online instructional platforms: Zoom In and Common Online Data Analysis Platform.            

This project will develop a comprehensive framework to inform and guide the analytic design of teacher professional development studies in mathematics. An essential goal of the research is to advance a science of teaching and learning in ways that traverse both research and education.

This project will provide a virtual environment for completing the Food, Energy, and Water (FEW) graduate student experience. The proposed work facilitates a transition from interdisciplinary to transdisciplinary training of existing faculty and current graduate students through a virtual resource center to help develop systematic processes for interdisciplinary thinking about large societal problems, especially those at the nexus of food, energy, and water.

The production of news stories and student-oriented instruction in the classroom are designed to increase student learning of STEM content through student-centered inquiry and reflections on metacognition. This project scales up the PBS NewsHour Student Reporting Labs (SRL), a model that trains teens to produce video reports on important STEM issues from a youth perspective.

The goal of this research is to investigate whether the integration of real data from cutting-edge scientific research in grade 6-10 classrooms will increase students’ quantitative reasoning ability in the context of science. We will adapt the materials to address current science and mathematics standards, including key concepts from  develop a professional development program for teachers, and test the efficacy of the materials through a quasi-experiment.

The project will develop modules for grades 9-12 that integrate mathematics, computing and science in sustainability contexts. The project materials also include information about STEM careers in sustainability to increase the relevancy of the content for students and broaden their understanding of STEM workforce opportunities. It uses summer workshops to pilot test materials and online support and field testing in four states. 

Given the changes in instructional practices needed to support high quality mathematics teaching and learning based on college and career readiness standards, school districts need to provide professional learning opportunities for teachers that support those changes. The project is based on the TRUmath framework and will build a coherent and scalable plan for providing these opportunities in high school mathematics departments, a traditionally difficult unit of organizational change.

This project builds on prior efforts to create teaching resources for high-school Advanced Placement Statistics teachers to use an open source statistics programming language called "R" in their classrooms. The project brings together datasets from a variety of STEM domains, and will develop exercises and assessments to teach students how to program in R and learn the underlying statistics concepts.

The Graphing Research on Inquiry with Data in Science (GRIDS) project will investigate strategies to improve middle school students' science learning by focusing on student ability to interpret and use graphs. GRIDS will undertake a comprehensive program to address the need for improved graph comprehension. The project will create, study, and disseminate technology-based assessments, technologies that aid graph interpretation, instructional designs, professional development, and learning materials.

This project aims to engage students in meaningful scientific data collection, analysis, visualization, modeling, and interpretation. It targets grades 9-12 science instruction. The proposed research poses the question "How do learners conceive of and interact with empirical data, particularly when it has a hierarchical structure in which parameters and results are at one level and raw data at another?"