Science

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.

Building Networks and Enhancing Diversity in the K-12 STEM Teaching Workforce

The goal of this planning grant is to explicitly focus on broadening participation in the K-12 STEM teaching workforce, with the theory of action that diversifying the K-12 STEM teaching workforce would in the long term help more students see STEM as accessible to them and then be more likely to choose a STEM degree or career.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2040784
Funding Period: 
Tue, 12/01/2020 to Tue, 11/30/2021
Full Description: 

The goal of this planning grant is to explicitly focus on broadening participation in the K-12 STEM teaching workforce, with the theory of action that diversifying the K-12 STEM teaching workforce would in the long term help more students see STEM as accessible to them and then be more likely to choose a STEM degree or career. Currently there is a large demographic discrepancy between students and teachers in K-12 schools. Studies have highlighted that the diverse teaching workforce benefits not only students of color but all students. Since 2017, the Smithsonian Science Education Center has conducted an annual STEM Diversity Summit, with the goal of building a coalition (built on collective impact) for attracting and retaining a diverse K-12 STEM teaching workforce, in which teams of teachers and administrators representing 83 school districts, schools, and states across the country shared their problems and developed a logic model to attract and retain a diverse K-12 STEM teaching workforce in their region with annual support from a matched mentor. This planning grant supports revisiting those former teams to better understand the dynamics of systems change through close examination of the successes and challenges outlined in their logic models with the perspective of the Cultural-Historical Activity Theory (CHAT). Under the collaborative infrastructure elements of shared vision and partnerships, this planning grant will inform and lay the foundation for a future alliance focused on diversifying the K-12 STEM teaching workforce.

In this planning grant, the Smithsonian in collaboration with Howard University, as well as in partnership with other experts in STEM teacher education, professional development, and diversityincluding from Harvard University, Rutgers University, 100kin10, National Board for Professional Teaching Standards, MA Department of Higher Education, STEM Equity Alliance, National Science Teaching Association, and private industrywill work on four primary activities. First, a survey will be developed and conducted with faculty members of Institutions of Higher Education (IHEs), including approximately 100 Minority Serving Institutions, which serve diverse populations in K-12 teacher preparation programs and STEM education across the country. The goal of the survey is to understand what roles IHEs play in organizational change management and strategic planning to diversify the K-12 STEM teaching workforce. Second, a virtual workshop will be convened to bring former STEM Diversity Summit attendees and their extended networks to reflect on their progress and activities in past years and discuss strategic long-term plans. Third, a survey with the virtual workshop participants will be conducted to better understand their practices, attitudes, and perceptions about their roles to create culturally diverse ecosystems in K-12 STEM education. Finally, all the collected information from the above activities will be used to investigate strategies and evidence-based practices of enhancing diversity in the K-12 STEM teaching workforce, and an iterative source book will be developed based on those findings as an initial resource to ground future work. Over a 12 month period, this planning grant will build a network between the former teams and with the extended partners, including the NSF INCLUDES National Network, and help them to grow as regional hubs within a Future NSF INCLUDES Alliance focused on diversifying the K-12 STEM teacher workforce, with the Smithsonian as the backbone organization.

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