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.
Projects
To successfully understand and address complex and important questions in the field of environmental science, many kinds of communities’ knowledge about their local environment need to be engaged. This one-year partnership development project involves a collaboration to design an approach that would yield opportunities for K-12 students to learn about environmental science in ways that honor both traditional STEM knowledge and Native ways of knowing among the Pomo community in California.
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.
Providing computer science (CS) education to students prior to high school is critical for catalyzing their interest in CS and closing achievement and development gaps. However, the retention rate for underrepresented group participants in middle school CS teacher preparation programs is lower than that for their peers. The resulting lack of diversity in CS teachers contributes to students’ inequitable access to quality middle school CS education. In this project will investigate effective design and implementation strategies of CS teacher preparation programs aimed to increase the number of middle school CS teachers from underrepresented groups.
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.
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.
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.
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.
National frameworks for science education in the United States advocate for bringing science, technology, engineering, mathematics, and computer science (STEM+CS) disciplines together in K-12 classrooms. Although curricular materials are emerging to support STEM+CS integration, research demonstrates that teachers need support to engage students in authentic STEM+CS practices that leverage and sustain student and community assets. This project aims to support middle school teachers in their enactment of an integrated science, engineering, and computational modeling curriculum unit and understand how teachers customize computationally rich, Next Generation Science Standards (NGSS)-aligned curricular materials to their own schools and classrooms.
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.
National frameworks for science education in the United States advocate for bringing science, technology, engineering, mathematics, and computer science (STEM+CS) disciplines together in K-12 classrooms. Although curricular materials are emerging to support STEM+CS integration, research demonstrates that teachers need support to engage students in authentic STEM+CS practices that leverage and sustain student and community assets. This project aims to support middle school teachers in their enactment of an integrated science, engineering, and computational modeling curriculum unit and understand how teachers customize computationally rich, Next Generation Science Standards (NGSS)-aligned curricular materials to their own schools and classrooms.
Despite years of research and interventions to address inequities that are largely related to race, science education continues to perpetuate these inequities in both participation and outcomes in science. This CAREER project will address the need to provide science teachers with a framework for considering race and racial dynamics in science teaching as well as exemplars in science teaching and professional development to support teachers’ teaching identities and praxis.
National frameworks for science education in the United States advocate for bringing science, technology, engineering, mathematics, and computer science (STEM+CS) disciplines together in K-12 classrooms. Although curricular materials are emerging to support STEM+CS integration, research demonstrates that teachers need support to engage students in authentic STEM+CS practices that leverage and sustain student and community assets. This project aims to support middle school teachers in their enactment of an integrated science, engineering, and computational modeling curriculum unit and understand how teachers customize computationally rich, Next Generation Science Standards (NGSS)-aligned curricular materials to their own schools and classrooms.
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.
Environmental issues like wildfires can serve as effective science learning contexts to promote scientific literacy and citizenship. This project will partner with teachers, teacher educators, and disciplinary experts in data science, fire ecology, public health, and environmental communication to co-design a data-driven, justice-oriented, and issue-based unit on wildfires. In the unit, student will engage in various data practices to gain insights into the issue of wildfires and how it affects their lives and communities. The project seeks to theorize how learners can leverage disciplinary knowledge and practices in environmental and data science as a foundation for making data-informed actions towards a more just and sustainable society.
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.
Anxiety about math has increased for some students due to disruptions in their learning during the COVID-19 pandemic. This partnership development project involving Portland State University and the Tigard-Tualatin School District addresses pandemic-related learning challenges in middle school mathematics, with a focus on math anxiety. Across the yearlong project, the partners play equal roles in co-developing research, practice, and policy proposals aimed at enhancing math outcomes and reducing math anxiety among the district’s middle school students.
This project partners with a mathematics department at a public middle school to co-design, analyze, and improve teachers’ translanguaging pedagogies, that is pedagogies that draw on students’ full linguistic repertoires as resources for their learning. This project will investigate how teachers make sense of and enact translanguaging pedagogies, how translanguaging pedagogies shape students’ mathematical experiences and learning opportunities, and how teachers’ learning of translanguaging spaces can be supported.
Over the years, researchers and practitioners have created and tested different ways to support students who struggle with learning mathematics. These methods include directly teaching various mathematics skills and strategies that affect mathematics performance, such as alleviating mathematics anxiety and fostering motivation and engagement in mathematics learning. The idea is that teaching mathematics using a mix of these skills or strategies might help students learn better than teaching just one skill or strategy at a time. However, it remains unclear which skills or strategies should be taught together and if mixing different skills or strategies leads to differential effects across different students or contexts. Understanding this is vital because it can help researchers and practitioners determine the best ways to address the need of struggling students in mathematics. A network meta-analysis will allow the field to examine different combinations of instructional skills/strategies as well as their interaction effects, which can provide more optimal information about different instructional approaches.
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.
Science and engineering teaching and curriculum have begun to engage learners’ knowledge of themselves, their communities, and their experiences of science and engineering. This knowledge can make the experience of learning science and engineering more meaningful and impactful as learners can see greater connections between the content and how their own experiences and communities. However, assessment approaches for documenting and presenting what learners’ know have typically not been able to sufficiently represent the new approaches to teaching and learning. This conference brings together researchers, school leaders, and teachers to develop frameworks and resources for making culturally sustaining approaches to teaching and learning science and engineering.
In the 21st century, the educational landscape is undergoing a seismic shift, with Artificial Intelligence (AI) emerging as a pivotal force reshaping the contours of teaching and learning, especially in the realm of science education. As educators, policymakers, and researchers grapple with the challenges and opportunities presented by this technological juggernaut, this project underscores the imperative to weave AI's transformative potential seamlessly with the foundational principles of Diversity, Equity, and Inclusion (DEI). The vision driving this initiative is twofold: harnessing the unparalleled capabilities of AI to revolutionize educational experiences while ensuring that these innovations are accessible, relevant, and beneficial to every student, irrespective of their background or circumstances.
K-12 teachers are a critical resource for promoting equitable STEM achievement and attainment. Experimental research, however, rarely identifies specific, transferable STEM instructional practices, because STEM education research has typically implemented student-level randomization far more than it has implemented teacher-level randomization. A major barrier limiting scientific progress is the lack of a large-scale trialing infrastructure that can support teacher-level randomization and experimentation, given the logistical constraints of recruiting multiple sites and successfully randomizing at the teacher or classroom level. This Midscale Research Infrastructure Incubator will launch a two-year, accelerated process to address these challenges and develop a consensus plan for a STEM-teacher-focused trialing platform.
The goal of the project is to understand the current conditions, challenges, and resources that pertain to mathematics education in rural areas in the United States.