This project will investigate how NGSS has been implemented in California schools during the ongoing COVID-19 pandemic. Through a state-wide survey, analysis of administrative data, interviews and case studies, this project will assess the impact of COVID-19 on NGSS implementation on a large scale, and more importantly, the extent to which high minority, high-poverty districts are disproportionately affected. It will also identify policy options available to state and school districts. By collecting critical and timely data, this project will contribute new knowledge to understanding of the impact of COVID-19 on NGSS implementation.
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.
This project is developing, validating, and evaluating computer modeling-based formative assessments to improve student learning in chemistry. Activities include developing a series of computer models related to key topics in high school chemistry, developing questions to probe student understanding of matter and energy, identifying teaching and learning resources appropriate for different levels of student conceptual understanding, and developing professional development resources on integrating formative assessments into high school chemistry courses.
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 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.
Data Nuggets (http://datanuggets.org) are classroom activities, co-designed by scientists and teachers, which give students practice interpreting quantitative information and making claims based on evidence. 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.