This project is connecting mathematicians and mathematics teachers in middle schools by offering summer workshops and continued communication throughout the year. The workshops focus on mathematical problem solving and include activities that offer multiple entry points. The goal of the workshops is to increase teachers' knowledge of mathematics for teaching and to help teachers use their knowledge to improve student learning of mathematics.
This study examines ways that teacher-level factors (including teacher background variables and instructional practices) and student-level factors (such as self-rated mathematics interest and proficiency), and interactions among these factors, are associated with American Indian/Alaska native (AI/AN) student academic achievement in middle grades mathematics. The ultimate goal is to identify malleable factors that, if changed, could improve teachers' practices and AI/AN student achievement in mathematics.
In this project, the team will address questions about how collaborative problem solving, learning progressions, and facilitation interact in the development of students’ mathematical learning. The work affords an opportunity to advance equitable access to high-quality education for all students by enhancing the quality of instruction for students lacking opportunities to learn key concepts of mathematics because of the inequitable structures of education in the country.
The United States faces the critical need to prepare students and the future workforce for advances in Artificial Intelligence (AI). This project will develop curriculum that will engage middle-school students in learning science and basic AI concepts and in developing related career interests.
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.
We're happy to welcome these guests who will be presenting at the 2018 DRK-12 PI Meeting: View Bios.
This is a quasi-experimental study of the effects of attending an inclusive STEM high school in three key geographic regions and comparing outcomes for students in these schools with those of their counterparts attending other types of schools in the same states. The study's focus is on the extent to which inclusive STEM high schools contribute to improved academic outcomes, interests in STEM careers, and expectations for post secondary study.
The objective of this study is to examine the impact of ITEAMS intervention strategies on student persistence in high school STEM course-taking and career expectations, and the value that students place on STEM careers.
This project will address two obstacles that hinder elementary science instruction: (1) a lack of content-specific teaching knowledge (e.g., research on effective topic-specific instructional strategies); and (2) the knowledge that does exist is often not organized for use by teachers in their lesson planning and instruction. The project will collect existing empirical literature for two science topics and synthesize it with an often-overlooked resource -- practice-based knowledge.
This project provides a virtual environment in which high school physics students can engage in the cutting edge science of studying exoplanets. Using online telescopes and learning software, students gain a deeper understanding of science inquiry, including reasoning from models, gathering assessing, and interpreting authentic data, and drawing conclusions from multiple line of evidence. The research advances our understanding of ways to increase students' knowledge of data literacy.
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 exploratory study develops and pilot-tests a model for improving science teaching and learning with middle school ELLs. Study goals include: (1) clarifying pedagogical constructs of language-rich science inquiry and the academic language of science and their relationships across the learning contexts of middle school science classrooms, teacher professional development and family science workshops, (2) developing and refining instruments to study these constructs in context, and (3) conducting pilot tests of the model and instruments.
This is a large-scale, cross-sectional, and longitudinal study aimed at understanding and supporting the teaching of science and engineering practices and academic language development of middle and high school students (grades 7-10) with a special emphasis on English language learners (ELLs) and a focus on biotechnology.
This project will develop a modified virtual world and accompanying curriculum for middle school students to help them learn to more deeply understand ecosystems patterns and the strengths and limitations of experimentation in ecosystems science. The project will build upon a computer world called EcoMUVE, a Multi-User Virtual Environment or MUVE, and will develop ways for students to conduct experiments within the virtual world and to see the results of those experiments.
The COVID-19 pandemic has highlighted the need for supporting student learning about viral outbreaks and other complex societal issues. Given the complexity of issues like viral outbreaks, engaging learners with different types of models (e.g., mechanistic, computational and system models) is critical. However, there is little research available regarding how learners coordinate sense making across different models. This project will address the gap by studying student learning with different types of models and will use these findings to develop and study new curriculum materials that incorporate multiple models for teaching about viral epidemics in high school biology classes.
This project tests and refines a hypothetical learning trajectory and corresponding assessments, based on the collective work of 50 years of research in mathematics education and psychology, for improving students' ability to reason, prove, and argue mathematically in the context of algebra. The study produces an evidence-based learning trajectory and appropriate instruments for assessing it.
To prepare the country's youth more broadly for a globalized world, the U.S. National Commission on Mathematics Instruction (USNCMI) will engage with the international community and assist in improving the state of mathematics education in the country by implementing international education programs, participating in international benchmarking activities, and working closely with other countries and multilateral organizations.
The Learning by Evaluating (LbE) project will develop, refine, and test an educational innovation in which 9th grade students evaluate sample work as a starting point in engineering design cycles. Students will compare and discuss the quality and fit to context of completed design artifacts. Teachers will collaboratively review and refine the LbE approaches and map the LbE materials into the curriculum.
This project will develop and test two curriculum units on the topic of evolution for high school general biology courses, with one unit focusing primarily on human case studies to teach evolution and one unit focusing primarily on case studies of evolution in other species. The two units will be compared to examine how different approaches to teaching evolution affect students and teachers.
Data literacy is the ability to ask questions, analyze, interpret, and draw conclusions from data. As the world and the workplace become more data-driven, students need to have stronger data literacy across multiple disciplines, including science. This project uses an instructional framework, Data Puzzles, to investigate how to support middle grades teachers learning to include data literacy in their science teaching. Data Puzzles integrate mathematical and computational thinking with ambitious science teaching instructional practices and contemporary science topics. Students engaging with Data Puzzles resources can analyze real-world climate science data using web-based data analysis tools to make sense of science phenomena and develop data literacy.
