Science

Supporting High School Students and Teachers with a Digital, Localizable, Climate Education Experience

This partnership of BSCS Science Learning, Oregon Public Broadcasting, and the National Oceanic and Atmospheric Administration advances curriculum materials development for high quality units that are intentionally designed for adaptation by teachers for their local context. The project will create a base unit on carbon cycling as a foundation for understanding how and why the Earth's climate is changing, and it will study the process of localizing the unit for teachers to implement across varied contexts to incorporate local phenomena, problems, and solutions.

Lead Organization(s): 
Award Number: 
2100808
Funding Period: 
Thu, 07/01/2021 to Mon, 06/30/2025
Full Description: 

Teachers regularly adapt curriculum materials to localize for their school or community context, yet curriculum materials are not always created to support this localization. Developing materials that are intentionally designed for localization has potential to support rich science learning across different contexts, especially for a topic like climate change where global change can have varied local effects. This partnership of BSCS Science Learning, Oregon Public Broadcasting, and the National Oceanic and Atmospheric Administration advances curriculum materials development for high quality units that are intentionally designed for adaptation by teachers for their local context. It will develop and test a design process bringing together national designers and teachers across the country. Teachers will be supported through professional learning to adapt from the base unit to create a local learning experience for their students. The project will create a base unit on carbon cycling as a foundation for understanding how and why the Earth's climate is changing, and it will study the process of localizing the unit for teachers to implement across varied contexts to incorporate local phenomena, problems, and solutions. The unit will be fully digital with rich visual experiences, simulations, and computer models that incorporate real-time data and the addition of localized data sets. These data-based learning experiences will support students in reasoning with data to ask and answer questions about phenomena. Research will study the unit development and localization process, the supports appropriate for teachers and students, and the impact on classroom practice.

The project will adopt an iterative design process to create a Storyline base unit, aligned to Next Generation Science Standards, for localization, piloting, and an implementation study with 40 teachers. To support teacher learning, the project adopts the STeLLA teacher professional learning model. To support student learning, the project addresses climate change content knowledge with a focus on socioscientific issues and students’ sense of agency with environmental science. The project will research how the educative features in the unit and the professional development impact teachers’ practice, including their content knowledge, comfort for teaching a socioscientific issue, and their ability to productively localize materials from a base unit. The study uses a cohort-control quasi-experimental design to examine the impact of the unit and professional learning experience on dimensions of students' sense of agency with environmental science. The study will also include exploratory analyses to examine whether all students benefit from the unit. It uses a pre-post design to examine impacts on teacher knowledge and practice.

Empowering Teachers to See and Support Student Use of Crosscutting Concepts in the Life Sciences

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.

Lead Organization(s): 
Award Number: 
2100822
Funding Period: 
Sun, 08/01/2021 to Wed, 07/31/2024
Full Description: 

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. In response to the calls set forth by the Framework for K-12 Science Education and Next Generation Science Standards (NGSS), the field has most successfully researched and developed assessment tools for disciplinary core ideas and the science and engineering practices. The CCCs, which serve as the connective links across science domains, however, remain more abstractly addressed. Presently, science educators have little guidance for what student use of CCCs looks like or how to assess and nurture such use. This project, with its explicit attention to the CCCs, advances true three-dimensional scientific understanding in both research and the classroom. Leveraging formative assessment as a vehicle for student and teacher development taps into proven successful instructional strategies (e.g., sharing visions of successful learning, descriptive feedback, self-assessment), while also advancing formative assessment, itself, by strengthening and illustrating how these strategies may focus on the CCCs. Further, a strengths-based approach will center culturally related differences in students’ use of CCCs to achieve more equitable opportunities to engage in classroom sensemaking practices. This work impacts the field of science education by 1) enabling a more thorough realization of NGSS ideals, 2) strengthening teachers’ abilities to identify diverse demonstrations of CCCs, and 3) showcasing the impact of novel classroom tools to sharpen teachers’ abilities to solicit, notice, and act upon evidence of emergent student scientific thinking within their instructional practices.

This design-based implementation research project will engage teachers in the iterative development and refinement of rubrics that support three-dimensional science understanding through formative assessment. The high school biology classrooms that compose the study site are engaged in ambitious science teaching-inspired instruction. An inductive, bottom-up approach (Brookhart, 2013) will allow researchers, teachers, and students to co-construct rubrics. Analysis of classroom observations, artifact collection, interviews with teachers and students, and expert-panel ratings will produce a rubric for each CCC that integrates relevant science and engineering practices and is applicable across a range of disciplinary core ideas. These rubrics will illustrate progressions of increasingly advanced use of each of the CCCs, to guide the construction, pursuit, and assessment of learning goals. There will be two design cycles that allow for the collection of validity evidence and produce rubrics with the potential for broad application by educators. Complementary lines of qualitative and quantitative (i.e., psychometric) analysis will contribute to development and validation of the rubrics and their formative uses. Project inquiry will focus on 1) how the rubrics can represent CCCs for key disciplinary practices, 2) the extent to which teachers’ and students’ understandings of the rubrics align, and 3) how implementation of the rubrics impacts teachers’ and students’ understandings of the CCCs.

Supporting Instructional Decision Making: The Potential of Automatically Scored Three-Dimensional Assessment System (Collaborative Research: Krajcik)

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.

Lead Organization(s): 
Award Number: 
2100964
Funding Period: 
Wed, 09/01/2021 to Sun, 08/31/2025
Full Description: 
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. Led by collaborators from University of Georgia, Michigan State University, University of Illinois at Chicago, and WestEd, the project team will develop computer scoring algorithms, a suite of AutoRs, and an array of pedagogical content knowledge supports (PCKSs). These products will assist middle school science teachers in the use of 3D assessments, making informative instructional changes, and improve students’ 3D learning. The project will generate knowledge about teachers’ uses of 3D assessments and examine the potential of automatically scored 3D assessments.
 
The project will achieve the research goals using a mixed-methods design in three phases. Phase I: Develop AutoRs. Machine scoring models for the 3D assessment tasks will be developed using existing data. To support teachers’ interpretation and use of automatic scores, the project team will develop AutoRs and examine how teachers make use of these initial reports. Based on observations and feedback from teachers, AutoRs will be refined using an iterative procedure so that teachers can use them with more efficiency and productivity. Phase II: Develop and test PCKSs. Findings from Phase I, the literature, and interviews with experienced teachers will be employed to develop PCKSs. The project will provide professional learning with teachers on how to use the AutoRs and PCKSs. The project will research how teachers use AutoRs and PCKSs to make instructional decisions. The findings will be used to refine the PCKSs. Phase III: Classroom implementation. In this phase a study will be conducted with a new group of teachers to explore the effectiveness and usability of AutoRs and PCKSs in terms of supporting teachers’ instructional decisions and students’ 3D learning. This project will create knowledge about and formulate a theory of how teachers interpret and attend to students’ performance on 3D assessments, providing critical information on how to support teachers’ responsive instructional decision making. The collaborative team will widely disseminate various products, such as 3D assessment scoring algorithms, AutoRs, PCKSs, and the corresponding professional development programs, and publications to facilitate 3D instruction and learning.

Supporting Instructional Decision Making: The Potential of Automatically Scored Three-Dimensional Assessment System (Collaborative Research: Zhai)

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.

Lead Organization(s): 
Award Number: 
2101104
Funding Period: 
Wed, 09/01/2021 to Sun, 08/31/2025
Full Description: 
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. Led by collaborators from University of Georgia, Michigan State University, University of Illinois at Chicago, and WestEd, the project team will develop computer scoring algorithms, a suite of AutoRs, and an array of pedagogical content knowledge supports (PCKSs). These products will assist middle school science teachers in the use of 3D assessments, making informative instructional changes, and improve students’ 3D learning. The project will generate knowledge about teachers’ uses of 3D assessments and examine the potential of automatically scored 3D assessments.
 
The project will achieve the research goals using a mixed-methods design in three phases. Phase I: Develop AutoRs. Machine scoring models for the 3D assessment tasks will be developed using existing data. To support teachers’ interpretation and use of automatic scores, the project team will develop AutoRs and examine how teachers make use of these initial reports. Based on observations and feedback from teachers, AutoRs will be refined using an iterative procedure so that teachers can use them with more efficiency and productivity. Phase II: Develop and test PCKSs. Findings from Phase I, the literature, and interviews with experienced teachers will be employed to develop PCKSs. The project will provide professional learning with teachers on how to use the AutoRs and PCKSs. The project will research how teachers use AutoRs and PCKSs to make instructional decisions. The findings will be used to refine the PCKSs. Phase III: Classroom implementation. In this phase a study will be conducted with a new group of teachers to explore the effectiveness and usability of AutoRs and PCKSs in terms of supporting teachers’ instructional decisions and students’ 3D learning. This project will create knowledge about and formulate a theory of how teachers interpret and attend to students’ performance on 3D assessments, providing critical information on how to support teachers’ responsive instructional decision making. The collaborative team will widely disseminate various products, such as 3D assessment scoring algorithms, AutoRs, PCKSs, and the corresponding professional development programs, and publications to facilitate 3D instruction and learning.

Supporting Instructional Decision Making: The Potential of Automatically Scored Three-Dimensional Assessment System (Collaborative Research: Weiser)

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.

Lead Organization(s): 
Award Number: 
2101112
Funding Period: 
Wed, 09/01/2021 to Sun, 08/31/2025
Full Description: 
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. Led by collaborators from University of Georgia, Michigan State University, University of Illinois at Chicago, and WestEd, the project team will develop computer scoring algorithms, a suite of AutoRs, and an array of pedagogical content knowledge supports (PCKSs). These products will assist middle school science teachers in the use of 3D assessments, making informative instructional changes, and improve students’ 3D learning. The project will generate knowledge about teachers’ uses of 3D assessments and examine the potential of automatically scored 3D assessments.
 
The project will achieve the research goals using a mixed-methods design in three phases. Phase I: Develop AutoRs. Machine scoring models for the 3D assessment tasks will be developed using existing data. To support teachers’ interpretation and use of automatic scores, the project team will develop AutoRs and examine how teachers make use of these initial reports. Based on observations and feedback from teachers, AutoRs will be refined using an iterative procedure so that teachers can use them with more efficiency and productivity. Phase II: Develop and test PCKSs. Findings from Phase I, the literature, and interviews with experienced teachers will be employed to develop PCKSs. The project will provide professional learning with teachers on how to use the AutoRs and PCKSs. The project will research how teachers use AutoRs and PCKSs to make instructional decisions. The findings will be used to refine the PCKSs. Phase III: Classroom implementation. In this phase a study will be conducted with a new group of teachers to explore the effectiveness and usability of AutoRs and PCKSs in terms of supporting teachers’ instructional decisions and students’ 3D learning. This project will create knowledge about and formulate a theory of how teachers interpret and attend to students’ performance on 3D assessments, providing critical information on how to support teachers’ responsive instructional decision making. The collaborative team will widely disseminate various products, such as 3D assessment scoring algorithms, AutoRs, PCKSs, and the corresponding professional development programs, and publications to facilitate 3D instruction and learning.

Supporting Instructional Decision Making: The Potential of Automatically Scored Three-Dimensional Assessment System (Collaborative Research: Yin)

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.

Award Number: 
2101166
Funding Period: 
Wed, 09/01/2021 to Sun, 08/31/2025
Full Description: 
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. Led by collaborators from University of Georgia, Michigan State University, University of Illinois at Chicago, and WestEd, the project team will develop computer scoring algorithms, a suite of AutoRs, and an array of pedagogical content knowledge supports (PCKSs). These products will assist middle school science teachers in the use of 3D assessments, making informative instructional changes, and improve students’ 3D learning. The project will generate knowledge about teachers’ uses of 3D assessments and examine the potential of automatically scored 3D assessments.
 
The project will achieve the research goals using a mixed-methods design in three phases. Phase I: Develop AutoRs. Machine scoring models for the 3D assessment tasks will be developed using existing data. To support teachers’ interpretation and use of automatic scores, the project team will develop AutoRs and examine how teachers make use of these initial reports. Based on observations and feedback from teachers, AutoRs will be refined using an iterative procedure so that teachers can use them with more efficiency and productivity. Phase II: Develop and test PCKSs. Findings from Phase I, the literature, and interviews with experienced teachers will be employed to develop PCKSs. The project will provide professional learning with teachers on how to use the AutoRs and PCKSs. The project will research how teachers use AutoRs and PCKSs to make instructional decisions. The findings will be used to refine the PCKSs. Phase III: Classroom implementation. In this phase a study will be conducted with a new group of teachers to explore the effectiveness and usability of AutoRs and PCKSs in terms of supporting teachers’ instructional decisions and students’ 3D learning. This project will create knowledge about and formulate a theory of how teachers interpret and attend to students’ performance on 3D assessments, providing critical information on how to support teachers’ responsive instructional decision making. The collaborative team will widely disseminate various products, such as 3D assessment scoring algorithms, AutoRs, PCKSs, and the corresponding professional development programs, and publications to facilitate 3D instruction and learning.

Crowd-Sourced Online Nexus for Developing Assessments of Middle-School Physical Science Disciplinary Core Ideas

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).

Lead Organization(s): 
Award Number: 
2101493
Funding Period: 
Wed, 09/01/2021 to Sat, 08/31/2024
Full Description: 

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). Teachers now spend an estimate 20% of their time in assessment, yet have relatively few tools to draw upon when creating them. Over time, they learn to adapt items from available curriculum materials and textbooks. On the other hand, standardized assessment developers have the benefit of expert item writers, long development cycles, a large and diverse student population, and sophisticated psychometric tools. This project combines these two approaches, drawing upon teachers to contribute their best items, then immediately piloting them using crowdsourced subjects. Psychometric analysis generates measures of item quality and then “recycles” items to participating teachers for improvement. In this way, a large test item bank will be constructed utilizing teacher input with each item possessing: appropriate reading levels, NGSS alignment, scientific accuracy, appropriate difficulty, high statistical discrimination, and negligible difference by gender, race, or ethnicity. Involvement in this project has potential benefits for teachers lacking formal training in assessment, familiarizing participants with the NGSS, and with the elements of high-quality test development.

The project will gauge the merits of a novel collaborative system for the development and validation of high-quality test items and assessment instruments. It will measure the degree to which teachers can generate effective items and improve existing items exhibiting problematic issues when given the guidance of rigorous psychometric measures that estimate item quality. It will build on earlier research showing that an adult, crowd-sourced sample works well as an initial proxy for grade 6-8 science students, allowing for extremely rapid feedback on item quality (often overnight), with item response theory computation used to establish item difficulty, item discrimination, guessing levels, and differential item functioning (gender and racial/ethnicity bias). In addition, computed measures of misconception strength, scientific correctness, reading level, and match to the NGSS will help to guide revision by teachers. Use of Bayesian futility analysis will “triage” items, minimizing costly testing of items when deemed unlikely to meet item quality criteria, lowering costs. Field testing with a large sample of grade 6-8 students will provide a final check on item quality. Items will be developed much more inexpensively than by methods used for standardized test development. Two pairs (public-release and secure for chemistry and physics) of assessment instruments will be constructed and be freely available to science teachers for classroom use and by education researchers and curriculum developers. A system that provides quick feedback on item quality could potentially transform university instruction and professional development opportunities in assessment. While starting with selected response (multiple-choice) items, the project will be able to implement a larger variety of formats in the future, incorporating automated approaches as they become available.

Exploratory Evidence on the Factors that Relate to Elementary School Science Learning Gains Among English Language Learners

This project will provide evidence on how school, classroom, teacher, and student factors shape elementary school science learning trajectories for English learners (ELs). The project will broaden ELs’ participation in STEM learning by investigating how individual, classroom, and school level situations such as instructional practices, learning environments, and characteristics of school personnel relate to EL elementary school science learning.

Lead Organization(s): 
Award Number: 
2100419
Funding Period: 
Sat, 05/15/2021 to Sun, 04/30/2023
Full Description: 

The nation’s schools are growing in linguistic and cultural diversity, with students identified as English learners (ELs) comprising more than ten percent of the student population. Unfortunately, existing research suggests that ELs lag behind other students in science achievement, even in the earliest grades of school. This project will provide evidence on how school, classroom, teacher, and student factors shape elementary school science learning trajectories for ELs. The project will broaden ELs’ participation in STEM learning by investigating how individual, classroom, and school level situations (inputs) such as instructional practices, learning environments, and characteristics of school personnel relate to EL elementary school science learning. Specifically, this study explores (1) a series of science inputs (time on science, content covered, availability of lab resources, and teacher training in science instruction), and (2) EL-specific inputs (classroom language use, EL instructional models, teacher certification and training, and the availability of EL support staff), in relation to ELs’ science learning outcomes from a national survey.

This study provides a comprehensive analysis of English learners’ (ELs) science learning in the early grades and the English learner instructional inputs and science instructional inputs that best predict early science outcomes (measured by both standardized science assessments and teacher-rated measures of science skills). The study uses the nationally representative Early Childhood Longitudinal Study (ECLS-K:2011) and employs a regression framework with latent class analysis to identify promising inputs that promote early science learning for ELs. Conceptually, rather than viewing the school-based inputs in isolation, the study explores how they combine to enhance students’ science learning trajectories. The study addresses the following research questions: How do science test performance trajectories vary across and within EL student groups in elementary school? How do access to school, teacher, and classroom level science and EL inputs vary across and within EL student groups in elementary school? Which school, teacher, and classroom level science and EL inputs are predictive of greater science test performance gains and teacher-rated science skills in elementary school? Are the relationships among these school, teacher, and classroom level inputs and student test performance and teacher-rated science skills different for subgroups of EL students, particularly by race/ethnicity or by immigration status? Are there particular combinations of school, teacher, and classroom level inputs that are predictive of science learning gains (test scores and teacher-rated skills) for ELs as compared to students more broadly?

The Impact of COVID on American Education in 2021: Continued Evidence from the Understanding America Study

This study will build upon the team's prior research from early in the pandemic. Researchers will continue to collect data from families and aims to understand parents’ perspectives on the educational impacts of COVID-19 by leveraging a nationally representative, longitudinal study, the Understanding America Study (UAS). The study will track educational experiences during the Spring and Summer of 2021 and into the 2021-22 school year.

Award Number: 
2120194
Funding Period: 
Mon, 03/01/2021 to Mon, 02/28/2022
Full Description: 

The COVID-19 epidemic has been a tremendous disruption to the education of U.S. students and their families, and evidence suggests that this disruption has been unequally felt across households by income and race/ethnicity. While other ongoing data collection efforts focus on understanding this disruption from the perspective of students or educators, less is known about the impact of COVID-19 on children’s prek-12 educational experiences as reported by their parents, especially in STEM subjects. This study will build upon the team's prior research from early in the pandemic. Researchers will continue to collect data from families and aims to understand parents’ perspectives on the educational impacts of COVID-19 by leveraging a nationally representative, longitudinal study, the Understanding America Study (UAS). The study will track educational experiences during the spring and summer of 2021 and into the 2021-22 school year. The team will analyze outcomes overall and for key demographic groups of interest as students and teachers return to in-person instruction during 2021. This RAPID project allows critically important data to continue to be collected and contribute to continued understanding of the impacts of and responses to the pandemic by American families.

Since March of 2020, the UAS has been tracking the educational impacts of COVID-19 for a nationally representative sample of approximately 1,400 households with preK-12 children. Early results focused on quantifying the digital divide and documenting the receipt of important educational services--like free meals and special education servicesafter COVID-19 began. This project will support the continued targeted administration of UAS questions to parents about students’ learning experiences and engagement, overall and in STEM subjects, data analysis, and dissemination of results to key stakeholder groups. Findings will be reported overall and across key demographic groups including ethnicity, disability, urbanicity, and socioeconomic status. This project will also produce targeted research briefs addressing pressing policy questions aimed at supporting intervention strategies in states, districts, and schools moving forward. Widespread dissemination will take place through existing networks and in collaboration with other research projects focused on understanding the COVID-19 crisis. All cross-sectional and longitudinal UAS data files will be publicly available shortly after conclusion of administration so that other researchers can explore the correlates of, and outcomes associated with, COVID-19.

Enhancing Science Education through Virtual Reality: A Conference to Design Simulations that Enhance the Clinical Preparation of Secondary Science Teachers

This conference focuses on the use of virtual/mixed reality simulation in the preparation of secondary science teachers. The conference convenes experts in simulation in teacher preparation, practicing high school teachers, and teacher candidates to engage in a design process related to mixed reality simulations. Conference attendees will identify important gaps in science teacher preparation and design prototype simulation environments for addressing those gaps.

Award Number: 
2040747
Funding Period: 
Fri, 01/01/2021 to Fri, 12/31/2021
Full Description: 

This conference focuses on the use of virtual/mixed reality simulation in the preparation of secondary science teachers. Educator preparation programs (EPPs) face significant challenges in providing science teacher candidates with quality clinical placements in high school science classrooms. Placements typically do not include the variety of science subject areas that teacher candidates are likely to teach (e.g., biology, chemistry, geoscience, physics) and the classes may not include important student populations such as English language learners or students who receive special education services. The use of virtual and/or mixed reality teaching simulations can address these needs by providing teacher candidates with opportunities to teach a wide range of science content and a diverse set of science learners. This conference brings together stakeholders in science teacher education to develop prototype simulation environments to address these gaps.

The conference convenes experts in simulation in teacher preparation, practicing high school teachers, and teacher candidates to engage in a design process related to mixed reality simulations. Conference attendees will identify important gaps in science teacher preparation and design prototype simulation environments for addressing those gaps. Mursion, a leader in mixed-reality teaching simulations, will provide the platform and resources to rapidly design and test prototypes that build on their current simulation deployment and provide these prototypes to conference attendees and members of the American Association of Colleges of Teacher Education (AACTE) to use and test in their EPP settings. Another key outcome of the convening is the development of a networked improvement community that would develop guidance for the effective use of simulations in science teacher preparation. This work will have a broad impact as the networked improvement community will continue to iterate and advance the use of the simulations in high school science teacher preparation programs.

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