There have been prominent and widespread calls for high school science students to work with data in more complex ways that better align with and support the work of professional scientists and engineers. However, high school students' analysis and interpretation of scientific data is often limited in scope, complexity, and authentic purpose. This project aims to support and advance students' work with ecological data in high school biology classrooms by embracing a new approach: Bayesian data analysis methods. Such methods involve expressing initial ideas or beliefs and updating them quantitatively with data that students access or record. This project will empower 20 high school teachers and their approximately 1,200 students to make sense of data within and beyond classroom contexts. It also will involve sharing research findings, an educational technology tool for Bayesian data analysis, and curricular resources in open and accessible ways.
Projects
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.
Early childhood educators (ECEs) understand that effective science teaching and learning requires content knowledge related to science concepts and practices and pedagogical knowledge. However, ECEs, especially in rural communities, express a lack of science content knowledge and confidence in incorporating science-related conversations in their early care and education settings, and they believe this might be a result of limited professional training relevant to science content. This project aims to strengthen key capabilities in ECEs, including the ability to (1) build science content knowledge and confidence in guiding young children's scientific investigation, (2) closely observe children's interactions with science materials, and (3) use those observations in the reflection, planning, and practice of science teaching.
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 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.
High-quality early educational experiences, particularly in mathematics, are crucial for students’ success in K-12 schooling. To create these foundational experiences for young children, early childhood educators need opportunities to enhance their mathematics teaching through job-embedded, sustained professional learning. This partnership development project establish a collaboration among early childhood mathematics educators, school and district leaders, the state department of education, and university faculty in Delaware that aims to enhance children’s early mathematics learning by collaboratively designing support systems for strengthening their teachers’ professional learning.
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.
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.
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.
To position students as mathematically competent, middle grades mathematics teachers need easily accessible professional learning (PL), including opportunities to participate in discussions about both mathematics content and teaching practices. A Video in the Middle (VIM) based learning series, the Coherent Asynchronous Online Mathematics Teacher Professional Learning (PL) project will help address this need by producing (1) a refined version of the existing VIM design and development prototype and (2) an asynchronous, collaborative online learning series comprising ten 2.5-hour sessions that focus on positioning students as mathematically competent in representing and conceptualizing transformations-based similarity, slope, or linear functions.
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.