Projects

The focus of this conference is to carefully examine past and current research with an eye toward improving its impact on practice and to create concrete steps that could shape the nature and impact of mathematics education research.

This project aims to support the mathematics learning of students with disabilities through the development and use of mixed reality simulations for elementary mathematics teacher preparation. These simulations represent low-stakes opportunities for preservice teachers to practice research-based instructional strategies to support mathematics learning, and to receive feedback on their practices.

The COVID-19 pandemic has significantly disrupted the ability of teacher education programs to place their teacher candidates in typical K-12 teaching settings as a part of learning to teach. This project examines how simulated classroom field experiences for preservice teachers can be implemented in online and emergency remote teacher education courses.

This project will develop, pilot, and refine a set of coordinated and complementary activities that teacher education programs can use in both online and face-to-face settings to provide practice-based opportunities for preservice teachers to develop their ability to facilitate argumentation-focused discussions in mathematics and science.

Through the integration of STEM content and literacy, this project will study the ways teachers implement project practices integrating literacy activities into STEM learning. Teachers will facilitate instruction using scenarios that present students with everyday, STEM-related issues, presented as scenarios, that they read and write about. After reading and engaging with math and science content, students will write a source-based argument in which they state a claim, support the claim with evidence from the texts, and explain the multiple perspectives on the issue.

This study focuses on working with teachers to develop assessment practices that focuses on the three NGSS dimensions of science ideas, practices and cross-cutting concepts, and adds two more dimensions; teachers will develop assessment tasks interesting to students, and promote the development of their science identities. To advance equitable opportunities for all students to learn science, this project will design and provide an online course to support rural teachers who teach science in grades 6-12. The course will focus on improving classroom science assessment practices and instruction to meet the unique needs of rural educators and their students.

This project will test and refine a teaching model that brings together current research about the role of language in science learning, the role of cultural connections in students' science engagement, and how students' science knowledge builds over time. The outcome of this project will be to provide an integrated framework that can guide current and future science teachers in preparing all students with the conceptual and linguistic practices they will need to succeed in school and in the workplace.

Through this project, researchers will develop internet-based assessments designed to capture learning outcomes that (a) measure the higher order cognitive skills that are essential to current reform efforts, and (b) that report results in ways that are readily accessible and interpretable.

The goal of this project is to develop learning progressions and assessment items targeting computational thinking. The items will be used for a test of college-ready critical reasoning skills and will be integrated into an existing online assessment system, the Berkeley Assessment System Software.

This project would investigate a new model of professional development for teams of science teachers in grades K-8 who would create electronic portfolios documenting how they taught specific concepts about energy. In addition, teachers would also select evidence of student understanding of the concepts and add those materials to their portfolios. The study focuses on teaching and learning energy core ideas and science practices that are aligned with the Next Generation Science Standards (NGSS).

This project represents a new approach to quality assessment of K-12 science and engineering learning experiences. By updating and expanding the Dimensions of Success (DoS) observation tool initially established for informal science learning settings to middle school science and engineering classrooms (DoS-MSSE), the project will create and implement a sustainable and scalable system of support for teachers who are learning how to implement the Next Generation Science Standards (NGSS) Framework for K-12 effectively and equitably.

The project focuses on the development of formative assessment tools that highlight assets of students’ use of crosscutting concepts (CCCs) while engaged in science and engineering practices in grades 9-12 Life Sciences.

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 Framework for K-12 Science Education has set forth an ambitious vision for science learning by integrating disciplinary science ideas, scientific and engineering practices, and crosscutting concepts, so that students could develop competence to meet the STEM challenges of the 21st century. Achieving this vision requires transformation of assessment practices from relying on multiple-choice items to performance-based knowledge-in-use tasks. However, these performance-based constructed-response items often prohibit timely feedback, which, in turn, has hindered science teachers from using these assessments. Artificial Intelligence (AI) has demonstrated great potential to meet this assessment challenge. To tackle this challenge, experts in assessment, AI, and science education will gather for a two-day conference at University of Georgia to generate knowledge of integrating AI in science assessment.

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 aims to support teachers to engage their students in mathematical problem posing (problem-posing-based learning, or P-PBL). P-PBL is a powerful approach to the teaching and learning of mathematics, and provides students with opportunities to engage in authentic mathematical practices.

This project will develop and test a web-based platform to increase the quality of teacher-administered tests in science classrooms. It draws on classroom teacher knowledge while employing the rigorous statistical methods used in standardized assessment creation and validation. The content focus is on the disciplinary core ideas for grades 6-8 physical science in the Next Generation Science Standards (NGSS).

This project will study the utility of a machine learning-based assessment system for supporting middle school science teachers in making instructional decisions based on automatically generated student reports (AutoRs). The assessments target three-dimensional (3D) science learning by requiring students to integrate scientific practices, crosscutting concepts, and disciplinary core ideas to make sense of phenomena or solve complex problems.

The COVID-19 pandemic has highlighted the need for supporting student learning about viral outbreaks and other complex societal issues. Given the complexity of issues like viral outbreaks, engaging learners with different types of models (e.g., mechanistic, computational and system models) is critical. However, there is little research available regarding how learners coordinate sense making across different models. This project will address the gap by studying student learning with different types of models and will use these findings to develop and study new curriculum materials that incorporate multiple models for teaching about viral epidemics in high school biology classes.

This project explores how to help teachers identify and support early elementary children’s emergent computational thinking. The project will engage researchers, professional development providers, and early elementary teachers (K-2) in a collaborative research and development process to design a scalable professional development experience for grade K-2 teachers. The project will field test and conduct research on the artifacts, facilitation strategies, and modes of interaction that effectively prepare K-2 teachers to learn about their students’ emergent use of computational thinking strategies.

This project will study the utility of a machine learning-based assessment system for supporting middle school science teachers in making instructional decisions based on automatically generated student reports (AutoRs). The assessments target three-dimensional (3D) science learning by requiring students to integrate scientific practices, crosscutting concepts, and disciplinary core ideas to make sense of phenomena or solve complex problems.

This project will study the utility of a machine learning-based assessment system for supporting middle school science teachers in making instructional decisions based on automatically generated student reports (AutoRs). The assessments target three-dimensional (3D) science learning by requiring students to integrate scientific practices, crosscutting concepts, and disciplinary core ideas to make sense of phenomena or solve complex problems.

This project explores the ways in which thoughtfully designed simulations can provide preservice teachers with formative assessment opportunities that serve as a complement to, or alternative to as needed, feedback derived from field placement contexts. A set of simulations will be designed with a focus on eliciting and interpreting student thinking. These simulations will be used with preservice teachers in three elementary teacher preparation programs of varying size and demographics.

This project will study the utility of a machine learning-based assessment system for supporting middle school science teachers in making instructional decisions based on automatically generated student reports (AutoRs). The assessments target three-dimensional (3D) science learning by requiring students to integrate scientific practices, crosscutting concepts, and disciplinary core ideas to make sense of phenomena or solve complex problems.

CADRE is the resource network that supports researchers and developers who participate in DRK-12 projects on teaching and learning in the science, technology, engineering and mathematics disciplines. CADRE works with projects to strengthen and share methods, findings, results and products, helping to build collaboration around a strong portfolio of STEM education resources, models and technologies. CADRE raises external audiences’ awareness and understanding of the DRK-12 program, and builds new knowledge.