Projects

STEM learning is a function of both student level and classroom level characteristics. Though research efforts often focus on the impacts of classrooms level features, much of the variation in student outcomes is at the student level. Hence it is critical to consider individual students and how their developmental systems (e.g., emotion, cognition, relational, attention, language) interact to influence learning in classroom settings. This is particularly important in developing effective models for personalized learning. To date, efforts to individualize curricula, differentiate instruction, or leverage formative assessment lack an evidence base to support innovation and impact. Tools are needed to describe individual-level learning processes and contexts that support them. The proposed network will incubate and pilot a laboratory classroom to produce real-time metrics on behavioral, neurological, physiological, cognitive, and physical data at individual student and teacher levels, reflecting the diverse dynamics of classroom experiences that co-regulate learning for all students.

Progress in science is motivated and directed by uncertainties. Yet even though uncertainty is a crucial fulcrum for scientific thought, school students are taught science within an overarching assumption that scientific knowledge is certain. This project explores the intellectual leverage of enabling middle school students to experience how scientific work grapples with uncertainty. The overall goal of this project is to understand how teachers can create equitable learning environments for culturally and linguistically diverse learners using Student Uncertainty for Productive Struggle as a pedagogical model in middle school science classrooms.

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.

Research has shown that when teachers have strong content and pedagogical content knowledge that they can provide better quality mathematics instruction to their students and improve student outcomes. The goal of this project is to enhance elementary school teachers’ capacity to improve students’ mathematics learning through a scaled professional development program that uses artificial intelligence (AI) to create a personalized, active learning environment for teachers.

Despite the importance of addressing climate change, existing K-12 curricula struggle to make the urgency of the situation personally relevant to students. This project seeks to address this challenge in climate change education by making the abstract, global, and seemingly intractable problem of climate change concrete, local, and actionable for young people. The goal of this project is to develop and test actLocal, an online platform for K–12 teachers, students, and the public to easily create localized climate change adaptation simulations for any location in the contiguous United States. These simulations will enable high school students and others to implement and evaluate strategies to address the impacts of climate change in their own communities.

Providing students with exposure to high quality computational thinking (CT) activities within science classes has the possibility to create transformative educational experiences that will prepare students to harness the power of CT for authentic problems. By building upon foundational research in human-AI partnership for classroom support and effective practices for integrating CT in science, this collaborative research project will advance understanding of how to empower teachers to lead computationally enriched science activities with adaptive pedagogical tools.

This partnership development project deepens an existing partnership between the researcher and leadership of an elementary school in central Texas that serves predominantly Black and Latine students. The project focuses on engaging community members, teachers, and learners at the school in conversation about how mathematics teaching and learning might be improved. This partnering is important because the relationship between schools and communities is often marked by one-way communication and decision-making without dialogue. By promoting dialogue, all members of this partnership can learn more about the mathematical storylines embedded into the community, that is, the stories that community members, teachers, and learners share about their personal relationship to mathematics teaching and learning. 

Providing students with exposure to high quality computational thinking (CT) activities within science classes has the possibility to create transformative educational experiences that will prepare students to harness the power of CT for authentic problems. By building upon foundational research in human-AI partnership for classroom support and effective practices for integrating CT in science, this collaborative research project will advance understanding of how to empower teachers to lead computationally enriched science activities with adaptive pedagogical tools.

Although there is a push to integrate artificial intelligence (AI) in K-12 education, the novelty of AI means that little is known about what schools, teachers, students, and parents know, need, and expect regarding AI in classrooms. The lack of access to AI knowledge and training is especially significant in rural high-needs communities where schools are under-resourced. This year-long partnership development project will seek to strengthen and expand existing research-practice partnerships (RPPs) with East Tennessee teachers and school leaders, develop new RPPs with parents and students enrolled in East Tennessee middle and high schools, and co-construct a shared vision for AI that aligns with the needs and assets of the partner community.

This project will improve STEM education by studying the various strategies taught to and used by students for solving multi-digit multiplication and division to develop a more cohesive understanding of children's multiplicative reasoning. The work will also support teachers’ ability to better support students’ multiplicative reasoning strategies via professional development videos that help them learn about students’ strategies.

High school and first-year college mathematics courses sometimes act as gatekeepers, ‘weeding out’ students who struggle with the subject matter and narrowing students’ opportunities for advanced STEM education and employment. Acknowledging opportunity gaps for students of color and those experiencing poverty, this partnership development project brings together Milwaukee Public Schools (MPS), Milwaukee Area Technical College (MATC), and WestEd to establish dual enrollment math courses that function as a lever for equity.

Tutoring programs that are jointly supported by schools and universities can offer benefits to both parties. The programs, however, are only helpful to the extent they respond to the needs and interests of the students and schools they serve. This project will establish a partnership between a large, urban university and a small, rural high school to collaboratively create a tutoring program to support the mathematics learning of students with learning disabilities.

This project examines student and teacher experiences with the de-tracking of math sequences in a public school district in Western Oregon. It examines how a district-wide cohort of middle school students, as individuals and in groups, identify with and define what it means to be good at math, and how these identities shift over time as they progress through math sequences. It also establishes a partnership between a mathematics education researcher and a school district (Research Practice Partnership) to study changes in pedagogy, define problems of teaching practice, and design solutions as the district transitions to de-tracked classes.

Effective “early” algebra interventions in elementary grades that can develop all students’ algebra readiness for later grades are needed. This study will use an experimental design to test the effectiveness of a Grades K–2 early algebra intervention when implemented in diverse classroom settings by elementary teachers. The broader impact of the study will be to deepen the role of algebra in elementary grades, provide much-needed curricular support for elementary teachers, and strengthen college and career readiness standards and practices.

Semiconductors are essential components of electronic devices, enabling advances in important applications and systems such as communication, healthcare, and national security. In order to sustain the U.S.'s global competitiveness in the semiconductor industry, there is a growing demand for a skilled semiconductor workforce. High schoolers are among the most frequent users of electronic devices. However, many do not know how these devices are designed and manufactured. To address the knowledge gaps and workforce needs equitably, this project will develop a semiconductor curriculum with high-school-aged students from diverse backgrounds, and with partners in higher education, K-12, and industries, enhanced with artificial intelligence (AI) and other innovative technologies.

This project will contribute knowledge about cultivating and strengthening productive mathematical identities of early childhood and elementary students. The project has the potential to improve kindergarten to third grade mathematics education for students from historically and persistently marginalized groups by intentionally leveraging (and confirming) resources for productive mathematical identity development. Further, this project will also equip educators to design number talks building upon students’ funds of knowledge and to also support their efforts to positively develop students’ mathematical identities.

The purpose of this project is to develop a home mathematics environment (HME) intervention for preschool-aged children with developmental delays (DD). The project includes caregivers of children with DD as collaborators in the iterative design process to develop feasible and sustainable HME intervention activities.

Understanding of algebra concepts is necessary for students to gain access to STEM pathways. However, recent efforts in education have failed to improve algebra outcomes for many students, especially those with learning disabilities and persistent difficulties in mathematics. The primary goal of this project is to develop a supplemental intervention that intentionally develops students' concept of variable as they learn to (a) interpret and evaluate expressions, (b) represent real-life mathematical word problems using algebraic notation, and (c) solve linear equations. A focus on clarifying common misconceptions about variables will be interwoven throughout the program.

This project is an innovative exploratory research study focused on developing a high school environmental engineering curriculum that addresses the challenges posed by climate change. The curriculum follows a model-validate-iterate design paradigm, where students model dynamic real-world systems, validate their models using data, and create multiple iterations to explore changes in the system over time. The project aims to cultivate a new generation of environmental engineers who possess the necessary skills to analyze complex systems, collaborate with diverse communities, and develop creative solutions.

This research study examines the potential of integrating student-driven descriptive investigations of complex multivariate civic datasets into middle school social studies classrooms. 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 sets, and 3) create learning experiences that connect data to local problems that have meaning for students and their communities.

This research synthesis systematically reviews and meta-analyzes the evidence on relationships between teacher support, student engagement, and mathematics achievement in the instructional–relational model framework. The researchers rigorously examine the consistency and variability of the relationships between the domains and constructs across studies.

This project will build an interactive and integrated curricular and professional development technological system: the Building Blocks Toolset (BBToolset). The BBToolset is designed to benefit all early childhood educators and their students. Young children will learn from engaging, effective digital educational games and face-to-face activities. Teachers will receive just-in-time professional development related to their students' development and guidance on curricular choices and culturally sensitive pedagogical strategies.

This project seeks to better understand how teachers' capacity and willingness to customize instructional approaches to meet standards and the needs of diverse student populations develops through initial practice and successive enactments of curriculum materials. This work will address current gaps in the literature and contribute to an overall understanding of how teachers develop the capacity to use curricula in ways that advance the goal of equitable science instruction.

There have been prominent and widespread calls for high school science students to work with data in more complex ways that better align with and support the work of professional scientists and engineers. However, high school students' analysis and interpretation of scientific data is often limited in scope, complexity, and authentic purpose. This project aims to support and advance students' work with ecological data in high school biology classrooms by embracing a new approach: Bayesian data analysis methods. Such methods involve expressing initial ideas or beliefs and updating them quantitatively with data that students access or record. This project will empower 20 high school teachers and their approximately 1,200 students to make sense of data within and beyond classroom contexts. It also will involve sharing research findings, an educational technology tool for Bayesian data analysis, and curricular resources in open and accessible ways.

This project aims to create and test an innovative educational approach for bringing STEM learning experiences to underserved youth. It will co-create and study an outdoor robotic-augmented playground called the “Smart Playground” and a corresponding series of classroom lessons. The Smart Playground will be co-designed with Latinx families and educators to engage children in developing computational thinking skills and learning about robotics in a physical environment using a culturally sustaining approach. Research and evaluation will examine whether exposure to the Smart Playground and corresponding classroom activities have an impact on the development of computational thinking in young children.