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.

# Projects

This project examines middle school students’ graph literacy from an asset-based perspective, documenting the ways in which students think about graphs (i.e., their cognitive strategies and intuitive insights), and the ways in which instruction can build upon that thinking in order to support the development of graph literacy. Drawing from students’ graphical representations of real-life contexts (e.g., population growth) that span various mathematical domains, this program of research will develop a holistic theoretical framework that can inform mathematics instruction in multiple content areas.

Through this project, the North Carolina State University Data Science Academy will identify the key success components of a model for the recruitment and mentoring of a diverse cohort of postdoctoral fellows who will enter their professions with expertise in data science education research. This model is intended to train and support scholars who have differing levels of experience and strengths in STEM, STEM education, education research, or data science.

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.

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.

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.

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.

This project examines the development of statistical literacy that combines statistical reasoning and thinking. The project will use professional learning communities for teachers to learn about statistical literacy and develop learning experiences for their students. The project will engage students and teachers in finding meaningful ways to use statistical reasoning to make data-based arguments and reason about patterns they observe in society.

This project will develop and investigate mathematics language routines focused on data science topics in middle and high school. The study will investigate teachers’ use of mathematics language routines and a professional development model to support teachers’ learning. The educational integration plan in the project will build mathematics teacher expertise and create video cases to support teacher professional development.

This project is developing curricular materials that utilize best teaching practices in improving student understanding of statistics and data science for use in high school Algebra I, Algebra II, and Geometry courses. Although teachers are encouraged to integrate statistics and data science in these kinds of high school courses, teachers do not have sufficient access to resources to accomplish this effectively. The distinctive feature of these curricular materials is the use of simulation-based inference methods, data visualization, and the entire statistical investigation process to improve students’ understanding of the relevance and power of statistics because these approaches are central to statistical thinking and practice.

The Common Core State Standards for Mathematics (CCSSM) problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests.

The Common Core State Standards for Mathematics (CCSSM) problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests.

The goal of this project is to study the design and development of community-centered, job-embedded professional development for classroom teachers that supports bias reduction. The project team will partner with three school districts serving racially, ethnically, linguistically, and socio-economically diverse communities, for a two-year professional development program. The aim is to reduce bias through: analyzing and designing mathematics teaching with colleagues, students, and families to create classrooms and schools based on community-centered mathematics; engaging in anti-bias teaching routines; and building relationships with parents, caretakers, and community members.

This project will develop a process for creating a shared, state-wide vision of high-quality mathematics instruction. It will also develop and study the resources to implement that vision at the state, district, and school levels. In addition, the project will investigate a collaborative process of designing and implementing high-quality mathematics instruction at a state level.

This project will develop a process for creating a shared, state-wide vision of high-quality mathematics instruction. It will also develop and study the resources to implement that vision at the state, district, and school levels. In addition, the project will investigate a collaborative process of designing and implementing high-quality mathematics instruction at a state level.

This project will develop a process for creating a shared, state-wide vision of high-quality mathematics instruction. It will also develop and study the resources to implement that vision at the state, district, and school levels. In addition, the project will investigate a collaborative process of designing and implementing high-quality mathematics instruction at a state level.

This project will provide a field-based science and mathematics teacher education program that supports teaching focused on students’ affective development through culturally responsive practices. The project's teacher education program takes place over a two-year period and models how culturally responsive and affective instruction can occur in the STEM classroom to engage students.

This project will develop an integrated, justice-oriented curriculum and a digital platform for teaching secondary students about data science in science and social studies classrooms. The platform will help students learn about data science using real-world data sets and problems. This interdisciplinary project will also help students meaningfully analyze real-world data sets, interpret social phenomena, and engage in social change.

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

This project will develop, test, and refine a "train-the-trainer" professional development model for rural teacher-leaders. The project goal is to design and develop a professional development model that supports teachers integrating culturally relevant computer science skills and practices into their middle school social studies classrooms, thereby broadening rural students' participation in computer science.

This project will collect and curate digital stories of diverse mathematicians sharing stories of their learning within and beyond schools. These short videos will become part of a more extensive digital database of mathematics stories that will be aligned with K-8 mathematics topics and then materials will be developed for teachers to use. The project team will explore the use of mathematics storytelling on K-8 teacher and student mathematics learning and engagement.

As a result of the COVID-19 pandemic, schools across much of the U.S. have been closed since mid-March of 2020 and many students have been attempting to continue their education away from schools. Student experiences across the country are likely to be highly variable depending on a variety of factors at the individual, home, school, district, and state levels. This project will use two, nationally representative, existing databases of high school students to study their experiences in STEM education during the COVID-19 pandemic. The study intends to ascertain whether students are taking STEM courses in high school, the nature of the changes made to the courses, and their plans for the fall. The researchers will identify the electronic learning platforms in use, and other modifications made to STEM experiences in formal and informal settings. The study is particularly interested in finding patterns of inequities for students in various demographic groups underserved in STEM and who may be most likely to be affected by a hiatus in formal education.

This project will design opportunities for mathematics and science teachers to coordinate their instruction to support a more coherent approach to teaching statistical model-based inference in middle school. It will prepare teachers to help more students develop a deeper understanding of ideas and practices related to measurement, data, variability, and inference and to use these tools to generate knowledge about the natural world.

This project focuses on fostering equitable and inclusive STEM contexts with attention to documenting and reducing adolescents' experiences of harassment, bias, prejudice and stereotyping. This research will contribute to understanding of the current STEM educational climates in high schools and will help to identify factors that promote resilience in the STEM contexts, documenting how K-12 educators can structure their classrooms and schools to foster success of all students in STEM classes.