Projects

The Data Games project has developed software and curriculum materials in which data generated by students playing computer games form the raw material for mathematics classroom activities. Students play a short video game, analyze the game data, develop improved strategies, and test their strategies in another round of the game.

This project is developing software and curriculum materials in which data generated by students playing computer games form the raw material for mathematics classroom activities. Students play a short video game, analyze the game data, conjecture improved strategies, and test their strategies in another round of the game.

This project will develop an integrated, justice-oriented curriculum and a digital platform for teaching secondary students about data science in science and social studies classrooms. The platform will help students learn about data science using real-world data sets and problems. This interdisciplinary project will also help students meaningfully analyze real-world data sets, interpret social phenomena, and engage in social change.

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

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.

The Common Core State Standards for Mathematics (CCSSM) problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests.

The Common Core State Standards for Mathematics (CCSSM) problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests.

The goal of this project is to study the design and development of community-centered, job-embedded professional development for classroom teachers that supports bias reduction. The project team will partner with three school districts serving racially, ethnically, linguistically, and socio-economically diverse communities, for a two-year professional development program. The aim is to reduce bias through: analyzing and designing mathematics teaching with colleagues, students, and families to create classrooms and schools based on community-centered mathematics; engaging in anti-bias teaching routines; and building relationships with parents, caretakers, and community members.

This research study examines the potential of integrating student-driven explorations of multivariate civic datasets with middle school social studies content. It uses a collaborative co-design process to develop data-rich experiences for the social studies classroom crafted to 1) deepen students’ data literacy, 2) develop students’ sense of efficacy in working with civic data, and 3) create data experiences that are meaningful and relevant to students and their communities.

Visit our project website to learn more about the project and access resources, readings, and opportunities to get involved: https://www.terc.edu/civic-data-project/about-the-project/.

This project uses new psychometric techniques to create a technological tool that could evaluate how well students in the 4th-8th mathematics and science classrooms respond to complex performance tasks. The purpose of this tool is to improve the instruction of teachers in mathematics and science. It will produce real-time individualized diagnoses of instructional needs to help teachers plan instruction that specifically addresses the learning needs of each student in that class.

This project addresses a major educational barrier, namely that rural students are less likely to choose a major in STEM and have far less access to advanced STEM courses taught by highly qualified teachers. The LogicDataScience (LogicDS) curriculum and virtual delivery are expected to relieve the resource constraints significantly and thus reach rural students. The strategy behind this curriculum development for data science explores the utility of emphasizing how the foundations of data science in computing, mathematics, and statistics are unified by mathematical logic. The project is studying the impacts of the new curriculum on students’ learning of computing, mathematics, and statistics.

This project addresses a major educational barrier, namely that rural students are less likely to choose a major in STEM and have far less access to advanced STEM courses taught by highly qualified teachers. The LogicDataScience (LogicDS) curriculum and virtual delivery are expected to relieve the resource constraints significantly and thus reach rural students. The strategy behind this curriculum development for data science explores the utility of emphasizing how the foundations of data science in computing, mathematics, and statistics are unified by mathematical logic. The project is studying the impacts of the new curriculum on students’ learning of computing, mathematics, and statistics.

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 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 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.

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 is developing curricular materials that utilize best teaching practices in improving student understanding of statistics and data science for use in high school Algebra I, Algebra II, and Geometry courses.  Although teachers are encouraged to integrate statistics and data science in these kinds of high school courses, teachers do not have sufficient access to resources to accomplish this effectively. The distinctive feature of these curricular materials is the use of simulation-based inference methods, data visualization, and the entire statistical investigation process to improve students’ understanding of the relevance and power of statistics because these approaches are central to statistical thinking and practice.

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 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 teaching modules that engage high school students in learning and using mathematics. Using geo-spatial technologies, students explore their city with the purpose of collecting data they bring back to the formal classroom and use as part of their mathematics lessons. This place-based orientation helps students connect their everyday and school mathematical thinking. Researchers are investigating the impact of place-based learning on students' attitudes, beliefs, and self-concepts about mathematics in urban schools.

Understanding probability is essential for daily life. Probabilistic reasoning is critical in decision making not only for people but also for artificial intelligence (AI). AI sets a modern context to connect probability concepts to real-life situations. It also provides unique opportunities for reciprocal learning that can advance student understanding of both AI systems and probabilistic reasoning. This project aims to improve the current practice of high school probability education and to design AI problem-solving to connect probability and AI concepts. Set in a game-based environment, students learn and practice applying probability theory while exploring the world of probability-based AI algorithms to solve problems that are meaningful and relevant to them.

Understanding probability is essential for daily life. Probabilistic reasoning is critical in decision making not only for people but also for artificial intelligence (AI). AI sets a modern context to connect probability concepts to real-life situations. It also provides unique opportunities for reciprocal learning that can advance student understanding of both AI systems and probabilistic reasoning. This project aims to improve the current practice of high school probability education and to design AI problem-solving to connect probability and AI concepts. Set in a game-based environment, students learn and practice applying probability theory while exploring the world of probability-based AI algorithms to solve problems that are meaningful and relevant to them.

LOCUS (Levels of Conceptual Understanding in Statistics) is an NSF Funded DRK12 project (NSF#118618) focused on developing assessments of statistical understanding. These assessments will measure students’ understanding across levels of development as identified in the Guidelines for Assessment and Instruction in Statistics Education (GAISE). The intent of these assessments is to provide teachers and researchers with a valid and reliable assessment of conceptual understanding in statistics consistent with the Common Core State Standards (CCSS).

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.