Projects

This project will address the need for high quality evidence-based models, practices, and tools for high school teachers and the development of students' problem solving and analytical skills by leveraging novel research and design approaches using digital tools and two well-established online instructional platforms: Zoom In and Common Online Data Analysis Platform.            

Given the changes in instructional practices needed to support high quality mathematics teaching and learning based on college and career readiness standards, school districts need to provide professional learning opportunities for teachers that support those changes. The project is based on the TRUmath framework and will build a coherent and scalable plan for providing these opportunities in high school mathematics departments, a traditionally difficult unit of organizational change.

This project is developing and conducting research on the Cohort Model for addressing the mathematics education of students that perform in the bottom quartile on state and district tests. The predicted outcome is that most students will remain in the cohort for all four years and that almost all of those who do will perform well enough on college entrance exams to be admitted and will test out of remedial mathematics courses.

This project will modify the teacher preparation program for preK-8 teachers. The program is designed to help pre-service teachers learn mathematics well, learn to access students' cultural funds of knowledge, and learn to encourage students' mathematical thinking. The developers are designing (a) modules that can be used in teacher preparation courses, (b) a mentoring program for new teachers, and (c) on-line networks to facilitate collaboration among participating teachers and institutions.

This exploratory project is to enhance the ability of teachers to provide high quality STEM education for all students by developing research-based materials that enable teachers to facilitate students' progress toward statistical understanding.

This project is developing and studying high school curriculum modules that integrate social justice topics with statistical data investigations to promote skills and interest in data science among underrepresented groups in STEM.

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.

The goal of this research is to investigate whether the integration of real data from cutting-edge scientific research in grade 6-10 classrooms will increase students’ quantitative reasoning ability in the context of science. We will adapt the materials to address current science and mathematics standards, including key concepts from  develop a professional development program for teachers, and test the efficacy of the materials through a quasi-experiment.

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 investigate the professional development supports needed for teaching bioinformatics at the high school level. The project team will work with biology and mathematics teachers to co-design instructional modules to engage students with core bioinformatics concepts and computational literacies, by focusing on local community health issues supported through mobile learning activities. The overarching goal of the project is to help create an engage population of informatics-informed students who are capable of critically analyzing information and able to solve local problems related to their health and well-being.

The production of news stories and student-oriented instruction in the classroom are designed to increase student learning of STEM content through student-centered inquiry and reflections on metacognition. This project scales up the PBS NewsHour Student Reporting Labs (SRL), a model that trains teens to produce video reports on important STEM issues from a youth perspective.

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.

The goal of this project is to formalize subjective ideas about the important concept of replication, provide statistical analyses for evaluating replication studies, provide properties for evaluating the conclusiveness of replication studies, and provide principles for designing conclusive and efficient programs of replication studies.

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.

The project will develop modules for grades 9-12 that integrate mathematics, computing and science in sustainability contexts. The project materials also include information about STEM careers in sustainability to increase the relevancy of the content for students and broaden their understanding of STEM workforce opportunities. It uses summer workshops to pilot test materials and online support and field testing in four states. 

LOCUS (Levels of Conceptual Understanding in Statistics) is an NSF Funded DRK12 project (NSF#118618) focused on developing assessments of statistical understanding. These assessments will measure students’ understanding across levels of development as identified in the Guidelines for Assessment and Instruction in Statistics Education (GAISE). The intent of these assessments is to provide teachers and researchers with a valid and reliable assessment of conceptual understanding in statistics consistent with the Common Core State Standards (CCSS).

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.

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 is developing teaching modules that engage high school students in learning and using mathematics. Using geo-spatial technologies, students explore their city with the purpose of collecting data they bring back to the formal classroom and use as part of their mathematics lessons. This place-based orientation helps students connect their everyday and school mathematical thinking. Researchers are investigating the impact of place-based learning on students' attitudes, beliefs, and self-concepts about mathematics in urban schools.

This project will examine the impact of a 12-year statewide science field trip program called LabVenture, a hands-on program in discovery and inquiry that brings middle school students and teachers across the state of Maine to the Gulf of Maine Research Institute (GMRI) to become fully immersed in explorations into the complexities of local marine science ecosystems.

This project seeks to strengthen the teaching of statistics and data science in grades 6-12 through the design and implementation of an online professional learning environment for teachers. The professional learning environment aims to support in-service teachers in developing stronger content knowledge related to statistics, and knowledge of how to effectively teach statistics in their classrooms.

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 design, develop, and test a new curriculum unit for high school chemistry courses that is organized around the question, "How does chemistry shape where I live?" The new unit will integrate relevant Earth science data, scientific practices, and key urban environmental research findings with the chemistry curriculum to gain insights into factors that support the approach to teaching and learning advocated by current science curriculum standards.

This project will provide a virtual environment for completing the Food, Energy, and Water (FEW) graduate student experience. The proposed work facilitates a transition from interdisciplinary to transdisciplinary training of existing faculty and current graduate students through a virtual resource center to help develop systematic processes for interdisciplinary thinking about large societal problems, especially those at the nexus of food, energy, and water.

This project will provide structured and meaningful scaffolds for teachers in examining two research-based teaching strategies hypothesized to positively impact mathematics achievement in the areas of mathematical modeling and problem solving. The project investigates whether the order in which teachers apply these practices within the teaching of mathematics content has an impact on student learning.