Projects

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.

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.

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.

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.

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 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 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.

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 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.

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.