Projects

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.

This project will study learning associated with elementary teachers' engagement in professional learning and elementary students' learning related to quantum science, quantum thinking, and careers. The knowledge base required for elementary teachers and students to learn quantum will be identified in order to explore and compare how elementary students and teachers conceptualize and make sense of quantum science concepts.

This exploratory study aims to design, implement, and test climate science and history professional learning materials and experiences for high school teachers. By leveraging existing science and history/social science materials, the program will develop curricular planning tools and lessons to help teachers integrate climate literacy into their instructional units. The goal is to provide students with the knowledge to understand and respond to the social and environmental issues associated with the climate crisis.

This project investigates the STEM teacher pipeline and examine qualifications, from teacher candidates who express interest in teaching STEM through to the eventual career paths of teachers in the workforce. In doing so, the project examines how the supply of STEM teachers has changed over time, whether the supply is adequate in meeting the needs of a changing nation, the qualifications and credentials of STEM teachers, and the implications of the STEM teacher career paths for equity and serving high needs contexts and students.

Online STEM credit courses have become attractive to school leaders as a way to support students who fail STEM courses in face-to-face school year settings. However, there is little research about the processes involved in how schools make decisions regarding student credit recovery. The available research focuses solely on student results and is not definitive enough to support important policy decisions at the district level. This research brings redress to this policy dilemma.

This project contributes to advancing knowledge on STEM education focusing on societal challenges by harnessing the convergence of STEM subjects, including data science and computer science, to empower a minoritized student group—multilingual middle-school learners.

Teachers are extraordinarily important to student learning, but researchers have surprisingly little data about what teachers do moment-to-moment with students. What are the instructional moves and improvisational responses that characterize highly effective practice? To better understand and support U.S. K-12 STEM teachers, this Incubator project will develop a network of "tutor observatories." Tutor observatories are learning environments that record teacher engagements with students along with information about the context of the interaction. From these data, researchers will be able to gain a deeper understanding of STEM teacher practice, identify highly effective practices, and develop training data that can inform a new generation of artificially intelligent tools to support teachers and student learning.

This project envisions a future of work where advanced technologies provide automated, job-embedded, individualized feedback to drive professional learning of the future worker. To achieve this goal, it addresses a fundamental question: Are evaluative or non-evaluative feedback systems more effective in driving professional learning? This question will be tested on professionals where objective, fine-grained feedback is especially critical to improvement--the teaching professions. This research will be situated within English and language arts (ELA) instruction in middle and high school classrooms, where underperformance and inequality in literacy outcomes are persistent problems facing the U.S. Current methods of supporting teacher learning through feedback are sparse, cumbersome, subjective, and evaluative. Thus, a major reconceptualization is needed to provide feedback mechanisms that- meaningfully affect teacher practice and are accessible to all. In partnership with TeachFX, an industry leader in technology-enabled instructional feedback, this project will work with teachers to design and test systems of automated feedback. Insights from the study will lead to feedback systems that empower teaching professionals, generate continued professional learning, and ultimately, increase student achievement.

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.

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.

The Inter-university Consortium for Political and Social Research (ICPSR) will host a workshop that brings together NSF-funded teams working on midscale research infrastructure incubator projects for STEM education research with a focus on education equity. ICPSR will share information, resources, and support incubator teams in developing and managing mid-scale infrastructure projects. These incubator projects have identified research infrastructure gaps related to assessments, teacher practices, and digital tools to support student learning and have proposed pilot tools, cyberinfrastructure, large-scale datasets, etc., for filling these gaps. To scale these pilots, the teams will need to successfully develop proposals to create mid-scale research infrastructure (Midscale RI). However, Midscale RI proposals require specialized knowledge that is not common within the STEM education research community and thus may limit the community’s ability to develop competitive Midscale RI proposals.

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.

Partnership development between universities and school districts requires an understanding that each organization has a distinct institutional point of view that must be considered in defining and shaping collaborative work. The goals and objectives of each organization may not always align, and at times may compete or conflict with each other. With the understanding that successful partnerships are those where practitioners and researchers achieve high levels of trust, commitment, transparency, interdependence, and mutual benefit, this project centers on building a partnership between a university that serves a largely Hispanic student population and a rural school district that also serves a community that has long been underrepresented in STEM education and career opportunities. The partners will jointly focus on how to respond to three negative impacts of the COVID-19 pandemic: 1) limited access to quality learning opportunities, 2) increased student learning gaps in STEM subjects, and 3) a local teacher shortage.

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.

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.

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 project will develop and study approaches to equip 4th and 5th grade general and special education teachers to teach computer science (CS) to a broad range of learners with disabilities through professional development. The project will aim to improve accessibility, accommodations, and highlight the role of paraeducators to increase participation and learning in CS for students with disabilities, and it will investigate the impact of the professional development on teachers’ instruction and the influence of the professional development model on student learning, ability beliefs, and attitudes about CS.

Cybersecurity is becoming an increased concern among young technology users; however, elementary school teachers often have limited preparation to teach students about cybersecurity. This project is designed to iteratively develop, refine, and test an innovative professional development program that supports teachers to infuse cybersecurity into 4th-5th grade mathematics and science instruction. The project will synergistically merge cybersecurity with mathematics and science content in authentic, real-world contexts to teach topics such as cyberbullying, digital security, encryption/decryption, digital privacy, and digital footprint.

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 project will develop and study approaches to equip 4th and 5th grade general and special education teachers to teach computer science (CS) to a broad range of learners with disabilities through professional development. The project will aim to improve accessibility, accommodations, and highlight the role of paraeducators to increase participation and learning in CS for students with disabilities, and it will investigate the impact of the professional development on teachers’ instruction and the influence of the professional development model on student learning, ability beliefs, and attitudes about CS.

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.

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.

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.