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
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.
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.
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.
As the nation tackles the challenges of energy transition, K-12 education must prepare a future STEM workforce that can not only apply STEM skills but also address reasoning through complex sociotechnical problems involving social justice. Aligned with the principles of socially transformative engineering and focused on students of color, this project involves the design and implementation of a novel STEM education curriculum that will support the development of secondary students’ abilities to reason through ambiguous and ethical challenges through design projects and to transfer these competencies to everyday life and future workplaces.
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.
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.
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.
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.
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.
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 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.
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.
Research has shown that educational games can increase student motivation, support critical thinking, problem-solving, and communication skills. This project will explore what approaches to the design of virtual labs, games, and bridging curriculum can most effectively support middle-school student development of interest and learning of scientific practices and contribute to the development of a science identity.
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 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.
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.