Projects

Familial presence in school supports children’s learning. However, few models exist that illustrate forms of familial presence in STEM learning that center familial cultural knowledge and practice. The project will produce a model for familial engagement in STEM along with instructional tools and illustrative case-studies that can be used by teachers and school districts nationally in support of increasing students’ STEM learning. This three-year study investigates new instructional practices that support rightful familial presence in STEM as a mechanism to address the continued racial and class gaps in STEM achievement for historically marginalized students.

This project addresses tools to support students in reading and evaluating a variety of sources to compare various claims addressing socioscientific issues. It draws on literacy concepts from science education and social studies to develop and implement scaffolding tools that can support students' understanding of the links among data, evidence, and claims while considering the trustworthiness and plausibility of sources. The project will design and test such instructional scaffolds with the goal of helping middle and high school science and social studies students to deepen their evaluation skills as they make reasoned evaluations as expected of citizens in a functional democratic society.

This project focuses on developing the Adapted Measure of Math Engagement (AM-ME), a new evidence-based measure for math teachers to assess students’ engagement. This project develops a mathematics engagement measure that reflects the experiences of Black and Latino students, providing valuable insights into improving mathematics learning environments and fostering increased student engagement.

This project focuses on developing anti-racist mathematics teaching and learning practices that have led to inequitable school experiences for Black, Indigenous, and Latinx students. This study is a partnership with school and central office leaders from one district and educational researchers from three universities with expertise in both educational leadership and mathematics education. Partnership activities include documenting how leaders learn and develop anti-racist leadership practices and then measuring the impact on teachers’ instruction and students’ experiences.

This project focuses on developing the Adapted Measure of Math Engagement (AM-ME), a new evidence-based measure for math teachers to assess students’ engagement. This project develops a mathematics engagement measure that reflects the experiences of Black and Latino students, providing valuable insights into improving mathematics learning environments and fostering increased student engagement.

This project will design instructional assessment materials by using an innovative and unique design approach that brings together the coherent and systematic design elements of evidence-centered design, an equity and inclusion framework for the design of science materials, and inclusive design principles for language-diverse learners. Using this three-pronged approach, this project will develop a suite of NGSS aligned formative assessment tasks for first-grade science and a set of instructional materials to support teachers as they administer the formative assessments to students with diverse language skills and capacities.

This project uses neural and behavioral measures of learning as a basis for making improvements to an immersive high school course that trains students in flexible spatial cognition and data analysis. Tracking students into college, the project measures long-term effects of improved spatial cognition resulting from the modified geospatial course curriculum.

The focus of this project is the design of learning experiences in different high school science courses to help students gain experience in computational thinking. The project uses a partnership between two universities and school district to develop and refine the units as a collaboration between researchers, teachers, and school leaders. The goal is to help all students have opportunities to learn about computational thinking in multiple science courses.

This project aims to deepen understanding of how to support and develop early childhood science learning by articulating science and engineering practices observed in children’s play. It also aims to develop early childhood educators’ abilities to identify and support nascent science and engineering practices with young children. Through this project early childhood educators will engage in professional learning using a refined version of the Science and Engineering Practices Observation Protocol (SciEPOP), an observation tool that allows researchers to identify and describe high-quality play-based engagement with science and engineering practices. Through video-rich professional learning along with peer-based coaching, early childhood educators will grow in their ability to prepare play environments, identify nascent science and engineering practices, enhance and extend investigations through play, and record and reflect upon this learning.

The project will develop a teacher professional learning (PL) model that focuses on middle-school biological sciences in addressing real world problems. Systems thinking is central to understanding biology systems. Game design has been shown to help develop systems thinking in teachers and students. Students will participate in PL to illustrate the value of distributed expertise by sharing their knowledge of computer. Teachers will adapt their existing curriculum and will co-design games with students to experience participatory practices.

The focus of this project is the design of learning experiences in different high school science courses to help students gain experience in computational thinking. The project uses a partnership between two universities and school district to develop and refine the units as a collaboration between researchers, teachers, and school leaders. The goal is to help all students have opportunities to learn about computational thinking in multiple science courses.

This project aims to deepen understanding of how to support and develop early childhood science learning by articulating science and engineering practices observed in children’s play. It also aims to develop early childhood educators’ abilities to identify and support nascent science and engineering practices with young children. Through this project early childhood educators will engage in professional learning using a refined version of the Science and Engineering Practices Observation Protocol (SciEPOP), an observation tool that allows researchers to identify and describe high-quality play-based engagement with science and engineering practices. Through video-rich professional learning along with peer-based coaching, early childhood educators will grow in their ability to prepare play environments, identify nascent science and engineering practices, enhance and extend investigations through play, and record and reflect upon this learning.

Teacher professional learning is a critical part of the mathematics education landscape. For decades, professional learning has been the primary strategy for developing the skills of the teaching workforce and changing how teachers interact with students in classrooms around academic content. Professional learning also can be expensive for districts, both financially and in terms of teacher time. Given these investments, most school leaders wish to spend their professional development dollars efficiently, making decisions about professional learning design that maximize teacher and student learning. However, despite more than two decades of rigorous research on professional learning programs, practitioners have little causal evidence on which professional learning design features work to accelerate teacher learning. This project seeks to identify features of teacher professional learning experiences that lead to better mathematics outcomes for both teachers and students.

The focus of this project is the design of learning experiences in different high school science courses to help students gain experience in computational thinking. The project uses a partnership between two universities and school district to develop and refine the units as a collaboration between researchers, teachers, and school leaders. The goal is to help all students have opportunities to learn about computational thinking in multiple science courses.

The project will design and research the Cultural Connections Process Model (CCPM), a place-based, culturally sustaining STEM educational resources and model that will engage Alaska Native and other high school students in STEM. The project approach is strongly informed by Indigenous knowledge systems (i.e., knowledge embedded in the cultural traditions of regional, Indigenous or local communities) and incorporates relevant arctic scientific research.

This study will investigate factors influencing teacher change after professional learning (PL) experiences and will examine the extent to which modest supports for science and engineering teaching in grades 3-5 sustain PL outcomes over the long term, such as increases in instructional time devoted to science, teacher self-efficacy in science, and teacher use of reform-oriented instructional strategies aligned with the Next Generation Science Standards.

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.

The project will design, develop, and test a research-based professional development (PD) approach that will ensure that teachers, and ultimately their middle-school students, have the knowledge to act in a way that promotes zero net loss of biodiversity in their communities. Through their participation in the PD, teachers will be equipped to plan for and implement NGSS-aligned instruction, facilitate student identification and understanding of biodiversity and environmental justice issues in their local community, and foster student capacity to take action. Students will come to understand that biodiversity is a global issue that they can influence at the local level, and will become empowered, in both their knowledge and their agency, to be leaders in solving biodiversity problems in their communities.

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.

This project seeks to understand how children learn about place value by studying different representations of multi-digit numbers (written number symbols, heard number names) and how prior knowledge of number influences children’s’ learning. Knowing more about multi-digit number learning will help to create teaching and curriculum resources that better support children’s learning.

This project aims to meet this need by developing PreK-5, equity-oriented, field-based, interdisciplinary curricular materials that support students' socioecological reasoning and sustainable decision making. The science learning experiences will be integrated across disciplines from literacy to civic and social studies lessons. The curricular materials will be part of a science education model that facilitates family engagement in ways that transform relations between educators, families, and students' science learning. The curricular activities will be co-designed with teachers while using local nature and culture as a resource.

This project builds on a successful introductory computer science curriculum, called Scratch Encore, to explore ways to support teachers in bringing together—or harmonizing—existing Scratch Encore instructional materials with themes that reflect the interests, cultures, and experiences of their students, schools, and communities. In designing these harmonized lessons, teachers create customized activities that resonate with their students while retaining the structure and content of the original Scratch Encore lesson.

This project aims to create and study an Equitable and Interactive Mathematical Modeling (EIM2) program that positions students as decision makers in their own learning. Despite the value of connecting students’ life experiences with their mathematical learning, the practical implementation of this strategy has proven challenging in a classroom setting. EIM2 addresses this issue by supporting students to engage in equitable mathematical modeling, a process of using mathematics to analyze and quantify scenarios through a lens of equity.