Projects

This project will support a conference series, including an in-person gathering and virtual follow-up meetings, that will bring together teachers, researchers, education leaders, and instructional material designers to build a shared understanding of how to integrate the use of high-quality instructional materials with the benefits of localizing these materials to better address students’ contexts and backgrounds. By fostering dialogue, sharing models, and setting priorities for future research and design, the project seeks to build knowledge about inclusive, effective, and culturally responsive approaches to science instruction that will advance equitable science education in K–12 classrooms.

This project will examine middle school students’ learning of earth and physical sciences and their functional understanding of engineering design as they engage in newly developed environmental justice-oriented curriculum units in community-based service projects. In collaboration with middle school teachers and their students, two STEM units that integrate science inquiry, engineering design, and community-based service projects will be co-designed, implemented, and refined while examining students’ science and engineering learning and their development of science/STEM interest and agency.

This project will develop a technology platform that can streamline lesson planning and allow teachers to adapt resources to their students' needs. The project will design and investigate an AI-powered lesson plan tool for middle-grades mathematics teaching called Colleague. Using existing, open-access lesson plans that have been vetted in prior work, the project would refine the tool for generating math lesson plans and supporting teachers to iteratively improve their instruction. Streamlining lesson planning would open more time for teacher creativity and reduce job stress. The study would explore how teachers use Colleague to plan and adapt lessons, the influence on teaching, and the students' learning.

This project will examine middle school students’ learning of earth and physical sciences and their functional understanding of engineering design as they engage in newly developed environmental justice-oriented curriculum units in community-based service projects. In collaboration with middle school teachers and their students, two STEM units that integrate science inquiry, engineering design, and community-based service projects will be co-designed, implemented, and refined while examining students’ science and engineering learning and their development of science/STEM interest and agency.

Artificial intelligence (AI) is transforming numerous industries and catalyzing scientific discoveries and engineering innovations. To prepare for an AI-ready workforce, young people must be introduced to core AI concepts and practices early to develop fundamental understandings and productive attitudes. Neural networks, a key approach in AI development, have been introduced to secondary students using various approaches. However, more work is needed to address the interpretability of neural networks and human-machine collaboration in the development process. This exploratory project will develop and test a digital learning tool for secondary students to learn how to interpret neural networks and collaborate with the algorithm to improve AI systems. The learning tool will allow students to interact with complex concepts visually and dynamically. It will also leverage students’ knowledge and intuition of natural languages by contextualizing neural networks in natural language processing systems.

Scientific literacy is an important educational goal, and the way scientists communicate is key to how science, as an institution, succeeds in its work. Conveniently, the recent and rapid rise of preprint publication platforms means that the public now has greater access to scientific communication and dialogue that occurs through open peer review. This is driving the need to educate students on, and engage them in, the evolving ways in which scientists construct and communicate knowledge. The goal of this project is to engage students in authentic science communication innovations through the implementation of a preprint and peer-review platform specifically designed for high school students.

Society has grown to rely on smart, embedded, and interconnected systems. This has created a great need for well-qualified and motivated engineers, scientists, and technicians who can design, develop, and deploy innovative microelectronics and Artificial Intelligence (AI) technologies, which drive these systems. This project will address the need for a more robust computer science and engineering workforce by broadening access to microelectronics and AI education leveraging the cutting-edge technologies of Tiny Machine Learning and low-cost microcontroller systems in diverse high schools. The goal of this project is to engage high-school students and teachers from underresourced communities in the design and creative application of AI-enabled smart, embedded technologies, while supporting their engineering identity development and preparing them for the STEM jobs of tomorrow.

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.

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.

Society has grown to rely on smart, embedded, and interconnected systems. This has created a great need for well-qualified and motivated engineers, scientists, and technicians who can design, develop, and deploy innovative microelectronics and Artificial Intelligence (AI) technologies, which drive these systems. This project will address the need for a more robust computer science and engineering workforce by broadening access to microelectronics and AI education leveraging the cutting-edge technologies of Tiny Machine Learning and low-cost microcontroller systems in diverse high schools. The goal of this project is to engage high-school students and teachers from underresourced communities in the design and creative application of AI-enabled smart, embedded technologies, while supporting their engineering identity development and preparing them for the STEM jobs of tomorrow.

Mathematics education research has emphasized instruction that asks teachers to use approaches that center students’ mathematical thinking. A significant part of this is how teachers notice, or focus on, analyze, and decide how to respond to, mathematics thinking. One common professional development method is to use videos of mathematics teaching to help teachers understand what is possible for students' learning. This exploratory project aims to understand how facilitators of video-based teacher professional development learn to help mathematics teachers of middle and high school students notice student mathematical thinking.

To provide equitable mathematics instruction to their students, middle grades mathematics teachers need easily accessible professional learning (PL), including opportunities to participate in discussions about both mathematics content and equity-based teaching practices. This project will help address this need by producing a refined version of the existing Video in the Middle design and development prototype. The team will also produce an asynchronous, collaborative online PL course comprising ten 2.5-hour sessions. These sessions interweave equitable mathematics teaching by incorporating the use of positioning into the classroom learning environment so that students' mathematical competence can be recognized and valued.

Exemplary teaching in STEM fields encourages students from diverse backgrounds to pursue further education and careers in science, technology, engineering and mathematics. Improving teaching, however, first requires an understanding of the current landscape of STEM instruction. The 2027 National Survey of Science and Mathematics Education (NSSME+), the seventh iteration of the study, will continue monitoring the status of science, mathematics, and computer science education in the U.S. The study will examine policies and practices related to STEM education, including the extent to which instruction currently models effective, evidence-based teaching practices, and factors that influence teachers’ decisions about content and pedagogy. It will also attend to factors that contribute to the underrepresentation of some groups in STEM, further adding to general knowledge about ways to broaden participation.

Society has grown to rely on smart, embedded, and interconnected systems. This has created a great need for well-qualified and motivated engineers, scientists, and technicians who can design, develop, and deploy innovative microelectronics and Artificial Intelligence (AI) technologies, which drive these systems. This project will address the need for a more robust computer science and engineering workforce by broadening access to microelectronics and AI education leveraging the cutting-edge technologies of Tiny Machine Learning and low-cost microcontroller systems in diverse high schools. The goal of this project is to engage high-school students and teachers from underresourced communities in the design and creative application of AI-enabled smart, embedded technologies, while supporting their engineering identity development and preparing them for the STEM jobs of tomorrow.

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.

While research has identified some features of professional development that impact teacher and student outcomes, there is still much mathematics education researchers do not know regarding which design features are most impactful to learning and how specific features of professional development connect to teacher learning. This project will investigate six prior NSF-funded professional development projects looking for features of the professional development associated with teacher uptake and learning, such as how the establishment of community or norms of collaboration support teachers’ long-term classroom practice.

Expectations and opportunities for student learning in science are expanding to involve students in making sense of and addressing real questions and problems in the world around them. At the same time, school districts are seeking innovative ways to support teachers to provide instruction that takes into account students’ perspectives and uses those perspectives to teach science. This project seeks to understand how a large, urban school district implements a practice-based professional learning program for teachers that employs performance assessments as a lever for instructional improvement by eliciting, centering, and advancing students’ thinking in middle school science classrooms.

Transdisciplinary science integrates knowledge across STEM disciplines to research complex challenges such as climate science, genetic engineering, or ecology. In this project, teachers and students will design smart greenhouses by connecting electronic sensors that can detect light or other environmental data to microcontrollers that can activate devices that water plants and regulate other environmental factors such as temperature or light. This activity brings together engineering, computer science, and horticulture. Working across urban and rural contexts, the project will engage teachers in professional development as they adopt and adapt instructional materials to support their students in learning across disciplines as they build smart greenhouses.

Staying up to date on new research findings is an increasingly daunting task for researchers, with scientific literature doubling roughly every 15 to 20 years. Synthesis researchers, too, face growing resource constraints as the size of extant literatures grow. To help mitigate associated challenges, this project will build the foundation and collaborations for using the latest advances in Artificial Intelligence (AI) to transform research synthesis in STEM education. This infrastructure will transform the speed and scale of research syntheses, while also democratizing access to the resources needed to conduct high-quality syntheses and spurring advances in broader researcher ecosystems.

Mathematical Opportunities in Student Thinking (MOSTs) are high-leverage instances of student mathematical thinking that emerge in whole-class discussions. The challenge for teachers is to build on these opportunities to help the whole class understand the mathematics underlying these student contributions. To help teachers learn how to build on MOSTs, there is a need for professional development resources and tools that facilitators can use. There is also a need for research about how teachers use what they learn in professional development in their teaching. This project is developing a teacher learning sequence that will support teachers in learning to productively use student thinking that surfaces in-the-moment during their instruction—that is, in learning to build on MOSTs.

With recent advances in artificial intelligence (AI), the United States needs to develop a diverse workforce with strong computational skills and the knowledge and capability to work with AI. Recent studies have raised questions about the extent to which youth are aware of AI and its application in industries of the future that may limit their interest in pursuing learning that lead toward careers in these industries. To address this challenge, learning trajectories (LTs) will be developed and researched for AI concepts that are challenging for middle and high school students. The project will design and pilot test learning activities and assessments targeting these concepts based on the LTs, offer teacher professional development on the LTs and related activities, and research the effectiveness of the LT-based activities when implemented by teachers during the regular school day.

Transdisciplinary science integrates knowledge across STEM disciplines to research complex challenges such as climate science, genetic engineering, or ecology. In this project, teachers and students will design smart greenhouses by connecting electronic sensors that can detect light or other environmental data to microcontrollers that can activate devices that water plants and regulate other environmental factors such as temperature or light. This activity brings together engineering, computer science, and horticulture. Working across urban and rural contexts, the project will engage teachers in professional development as they adopt and adapt instructional materials to support their students in learning across disciplines as they build smart greenhouses.

An exit ticket is a recommended and widely used way to end a lesson. The most common purpose of exit tickets is to provide formative feedback to teachers about whether students have met the objectives of a given lesson. However, the psychology of learning literature suggests that there is an untapped potential for exit tickets to also benefit students’ learning directly. This project explores two potential enhancements to exit tickets, with the goal of improving high-school students’ mathematics knowledge and ability to regulate their own learning processes.

Mathematical Opportunities in Student Thinking (MOSTs) are high-leverage instances of student mathematical thinking that emerge in whole-class discussions. The challenge for teachers is to build on these opportunities to help the whole class understand the mathematics underlying these student contributions. To help teachers learn how to build on MOSTs, there is a need for professional development resources and tools that facilitators can use. There is also a need for research about how teachers use what they learn in professional development in their teaching. This project is developing a teacher learning sequence that will support teachers in learning to productively use student thinking that surfaces in-the-moment during their instruction—that is, in learning to build on MOSTs.

Professional learning communities (PLCs) are one common model for teachers to collaborate and learn from one another. The goal of this study is to understand how teachers' expertise is positioned in a PLC and the larger system of the school and district to inform mathematics teaching and learning. This should help schools and districts understand the features of PLCs that are important for supporting teachers as they collaborate and learn.