Blacks/African Americans

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.

CAREER: Black and Latinx Parents Leading chANge and Advancing Racial (PLANAR) Justice in Elementary Mathematics

This project explores possibilities for localized change led by parents and caregivers. By making explicit how to foster and increase Black and Latinx parents’ engagement in solidarity with community organizations and teachers, this project could provide a model for other communities and schools seeking to advance racial justice in mathematics education.

Project Email: 
Lead Organization(s): 
Award Number: 
2046856
Funding Period: 
Thu, 07/01/2021 to Tue, 06/30/2026
Full Description: 

Decades of reform efforts in mathematics education continue to fail Black and Latinx children, in part, because parents are excluded from decisions about school mathematics. Nonetheless, Black and Latinx families often persist in supporting their individual children, but a shift toward collective organizing among parents as change agents in school mathematics is necessary for meeting the needs of every student. This project explores possibilities for localized change led by parents. By making explicit how to foster and increase Black and Latinx parents’ engagement in solidarity with community organizations and teachers, this project could provide a model for other communities and schools seeking to advance racial justice in mathematics education.

Through critical community-engaged scholarship and in collaboration with ten Black and Latinx families, ten teachers, and two community organizations, the research team will co-design and co-study two educational programs aimed at advancing racial justice in elementary mathematics. The first program seeks to build parents’ capacity to catalyze change across classrooms and schools within their local communities; and the second program will provide teacher professional development that supports elementary teachers of mathematics to learn with and from Black and Latinx families. A mixed methods research design that utilizes narrative inquiry and social network analysis will facilitate refinement of the educational program models by addressing two research objectives: (1) to understand the experiences of Black and Latinx parents as they build capacity to lead change and (2) to study the development, nature, and impact of parent-teacher-community partnerships that promote a shared vision for racial justice in mathematics. Findings could extend the field's understanding of community-initiated and community-led change in school mathematics and produce a model that helps ensure increased access and opportunity for Black and Latinx students in matheparents are excluded from decisions about school mathematics. Nonetheless, Black and Latinx families often persist in supporting their individual children, but a shift toward collective organizing among parents as change agents in school mathematics is necessary for meeting the needs of every student. This project explores possibilities for localized change led by parents. By making explicit how to foster and increase Black and Latinx parents’ engagement in solidarity with community organizations and teachers, this project could provide a model for other communities and schools seeking to advance racial justice in mathematics education.Through critical community-engaged scholarship and in collaboration with ten Black and Latinx families, ten teachers, and two community organizations, the research team will co-design and co-study two educational programs aimed at advancing racial justice in elementary mathematics.matics education.

Developing and Researching K-12 Teacher Leaders Enacting Anti-bias Mathematics Education (Collaborative Research: Yeh)

The goal of this project is to study the design and development of community-centered, job-embedded professional development for classroom teachers that supports bias reduction. The project team will partner with three school districts serving racially, ethnically, linguistically, and socio-economically diverse communities, for a two-year professional development program.

Lead Organization(s): 
Award Number: 
2101666
Funding Period: 
Sun, 08/01/2021 to Thu, 07/31/2025
Full Description: 

There is increased recognition that engaging all students in learning mathematics requires an explicit focus on anti-bias mathematics teaching. Teachers, even with positive intentions, have biases, causing them to treat students differently and impacting how they distribute students’ opportunities to learn in K-12 mathematics classrooms. Research is needed to examine models of mathematics teacher professional development that explicitly addresses bias reduction. The goal of this project is to study the design and development of community-centered, job-embedded professional development for classroom teachers that supports bias reduction. The project team will partner with three school districts serving racially, ethnically, linguistically, and socio-economically diverse communities, for a two-year professional development program. The aim is to reduce bias through: analyzing and designing mathematics teaching with colleagues, students, and families to create classrooms and schools based on community-centered mathematics; engaging in anti-bias teaching routines; and building relationships with parents, caretakers, and community members. The project team will study teacher leader professional development, including the professional development model, framework, and tools, along with what teacher leaders across district contexts and grade-levels take up and use in their instructional practice.  This will potentially have wider implications for supporting more equitable mathematics teaching and leadership. Project activities, resources, and tools will be shared with the broader community of mathematics educators and researchers for use in other contexts.

The goal of this two-phase, design based research project is to iteratively design and research teacher leaders’ (TLs) participation in community-centered, job-embedded professional development and investigate their subsequent impact on classrooms, schools, and districts. The project builds on the existing Math Studio professional development model to create a Community Centered Math Studio, integrating the Anti-bias Mathematics Education Framework into the work. The project seeks to understand how the professional development model supports the development of teacher leaders' knowledge, dispositions, and practices for teaching and leading anti-bias mathematics education, and how teachers' subsequent classroom practice can cultivate students' mathematical engagement, discourse, and interests. The project will measure aspects of teacher knowledge and classroom practice by integrating existing classroom observation rubrics and STEM interest surveys to assess the impact on teacher classroom practice and student outcomes. The project will engage 12 TLs and approximately 60 additional teachers working with those TLs in two years of professional development using the Community Centered Math Studio Model to support anti-bias mathematics teaching. Data will be collected for all teachers related to their participation in the professional learning, with six teachers being followed for additional data collection and in-depth case studies. The project's outcomes will contribute to theories of how TLs build adaptive expertise for teaching and leading to reduce bias in classrooms, departments, schools, and districts. In addition, the project will contribute new and adapted research instruments on anti-bias teaching and leading. The research outcomes will add to the growing research base that describes the nature of equitable mathematics teaching in K-12 classrooms and increases access to meaningful mathematics for students, teachers, and communities.

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.

Incorporating Professional Science Writing into High School STEM Research Projects

The goal of this project is to expand high school student participation in the peer-review process and in publishing in JEI, a science journal dedicated to mentoring pre-college students through peer-reviewed publication. By publishing pre-college research in an open access website, the project will build understanding of how engaging in these activities can change high school students' perceptions and practices of scientific inquiry.

Lead Organization(s): 
Award Number: 
2010333
Funding Period: 
Wed, 07/15/2020 to Fri, 06/30/2023
Project Evaluator: 
Maya Patel
Full Description: 

This exploratory project addresses important challenge of incorporating disciplinary literacy practices in scientific inquiry projects of high school students. The project will incorporate the peer-review process and publication in the Journal of Emerging Investigators (JEI). The Next Generation Science Standards emphasize constructs from disciplinary literacy such as engaging in argument from evidence, and evaluating and communicating information. However, there are few resources available to students and teachers that integrate these constructs in authentic forms that reflect the practices of professional scientists. High school student learners engage in scientific inquiry, but rarely participate in authentic forms of communication, forms that are reflective of how scientists communicate and participate in the primary literature of their fields. The project has three aims: 1) Generate knowledge of the impact of peer-review and publication on perceptions and skills of scientific inquiry and STEM identity, 2) Generate knowledge of how participation in peer-review and publication are impacted by contextual factors (differences in mentors and research contexts), and 3) Develop JEI field-guides across a range of contexts in which students conduct their research.

The goal of the project is to expand high school student participation in the peer-review process and in publishing in JEI, a science journal dedicated to mentoring pre-college students through peer-reviewed publication. By publishing pre-college research in an open access website, the project will build understanding of how engaging in these activities can change high school students' perceptions and practices of scientific inquiry. The project will investigate how participation in peer-reviewed publications will have an impact on student learning by administering a set of pre- and post-surveys to students who submit a paper to JEI. The project will expand student participation in JEI via outreach to teachers in under-resourced and remote areas by delivering virtual and in-person workshops which will serve to demystify peer review and publication, and explore ways to integrate these processes into existing inquiry projects. Other efforts will focus on understanding how student contextual experiences can impact their learning of scientific inquiry. These student experiences include the location of the project (school, home, university lab), the type of mentor they have, and how they became motivated to pursue publication of their research. The project will recruit students from under-resourced schools in New York through a collaboration with MathForAmerica and from rural areas through outreach with STEM coordinators in the Midwest. The resources created will be disseminated directly on the JEI website.

Creating a Model for Sustainable Ambitious Mathematics Programs in High-Need Settings: A Researcher-Practitioner Collaboration

This project will study a successful, ambitious mathematics reform effort in high-needs secondary schools. The goal is to develop resources and tools to support other high-needs schools and districts in transforming and sustaining  their mathematics programs. The model focuses on the resources required for change and the aspects of the organization that support or constrain change in mathematics teaching and learning.

Lead Organization(s): 
Award Number: 
2010111
Funding Period: 
Sat, 08/01/2020 to Wed, 07/31/2024
Full Description: 

A long-standing challenge in secondary mathematics education is broadening participation in STEM. Reform of schools and districts to support this goal can be challenging to sustain. This implementation and improvement project will study a successful, ambitious mathematics reform effort in high-needs secondary schools. The goal is to develop resources and tools to support other high-needs schools and districts in transforming and sustaining  their mathematics programs. The model focuses on the resources required for change and the aspects of the organization that support or constrain change in mathematics teaching and learning. The project team includes school district partners that have successfully transformed mathematics teaching to better support students' learning.

The project will develop a model for understanding the demands and resources from an organizational perspective that support ambitious mathematics teaching and learning reforms. Demands are requirements for physical resources or efforts that need to be met in the instructional system. Resources are the material, human, instructional, and organizational requirements needed to address demands. The project will develop the model through a collaboration of researchers, professional development leaders, students, teachers, coaches, and administrators to: (1) understand the demands created throughout a school or district when implementing an ambitious secondary mathematics program in a high-need context; (2) identify the resources and organizational dynamics necessary to address the demands and thus sustain the program; and (3) articulate a model for a sustainable ambitious secondary mathematics program in high-need settings that has validity across a range of implementation contexts. To develop the model over multiple iterations, the project will examine the demands and resources related to implementing an ambitious mathematics program, the perspectives of stakeholders, the organizational structure, and the program goals and implementation. The project will also conduct a systematic literature review to bring together findings from the successful district and other research findings. The data collection and analysis process will include interviews, document analysis, collection of artifacts, and observations across four phases of the project.  Participants will include students, teachers, instructional support personnel, and administrators (from schools and the district).

Reaching Across the Hallway: An Interdisciplinary Approach to Teaching Computer Science in Rural Schools

This project will develop, test, and refine a "train-the-trainer" professional development model for rural teacher-leaders. The project goal is to design and develop a professional development model that supports teachers integrating culturally relevant computer science skills and practices into their middle school social studies classrooms, thereby broadening rural students' participation in computer science.

Lead Organization(s): 
Award Number: 
2010256
Funding Period: 
Wed, 07/01/2020 to Sun, 06/30/2024
Full Description: 

Strengthening computer science (CS) and computational thinking (CT) education is a national priority with particular attention to increasing the number of teachers prepared to deliver computer science courses. For rural schools, that collectively serve more than 10 million students, it is especially challenging. Rural schools find it difficult to recruit and retain STEM teachers that are prepared to teach computer science and computational thinking. This project will develop, test, and refine a "train-the-trainer" professional development model for rural teacher-leaders. The project will build teachers' self-efficacy to deliver computer science concepts and practices into middle school social studies classrooms. The project is led by CodeVA (a statewide non-profit in Virginia), in partnership with TERC (a STEM-focused national research institution) and the University of South Florida College of Education, and in collaboration with six rural school districts in Virginia. The project goal is to design and develop a professional development model that supports teachers integrating culturally relevant computer science skills and practices into their middle school social studies classrooms, thereby broadening rural students' participation in computer science. The professional development model will be designed and developed around meeting rural teachers, where they are, geographically, economically, and culturally. The model will also be sustainable and will work within the resource constraints of the rural school district. The model will also be built on strategies that will broadly spread CS education while building rural capacity.

The project will use a mixed-methods research approach to understand the model's potential to build capacity for teaching CS in rural schools. The research design is broken down into four distinct phases; planning/development prototyping, piloting and initial dissemination, an efficacy study, and analysis, and dissemination. The project will recruit 45 teacher-leaders and one district-level instructional coach, 6th and 7th-grade teachers, and serve over 1900 6th and 7th-grade students. Participants will be recruited from the rural Virginia school districts of Buchanan, Russell, Charlotte, Halifax, and Northampton. The research question for phase 1 is what is each district's existing practice around computer science education (if any) and social studies education? Phases 2, 3 and 4 research will examine the effectiveness of professional development on teacher leadership and the CS curricular integration. Phase 4 research will examine teacher efficacy to implement the professional development independently, enabling district teachers to integrate CS into their social studies classes. Teacher data sources for each phase include interviews with administrators and teachers, teacher readiness surveys, observations, an examination of artifacts, and CS/CT content interviews. Student data will consist of classroom observation and student attitude surveys. Quantitative and qualitative data will be triangulated to address each set of research questions and provide a reliability check on findings. Qualitative data, such as observations/video, and interview data will be analyzed through codes that represent expected themes and patterns related to teachers' and coaches' experiences. Project results will be communicated through presentations at conferences such as Special Interest Group on Computer Science Education, the Computer Science Teachers Association (CSTA), the National Council for Social Studies (NCSS), and the American Educational Research Association. Lesson plans will be made available on the project website, and links will be provided through publications and newsletters such as the NCSS Middle-Level Learner, NCSS Social Education, CSTA the Voice, the NSF-funded CADREK12 website and the NSF-funded STEM Video Showcase.

How Deep Structural Modeling Supports Learning with Big Ideas in Biology (Collaborative Research: Capps)

This project addresses the pressing need to more effectively organize STEM (science, technology, engineering, and mathematics) teaching and learning around "big ideas" that run through science disciplines. Unfortunately, finding ways to teach big ideas effectively so they become useful as knowledge frameworks is a significant challenge. Deep structure modeling (DSM), the innovation advanced in this project, is designed to meet this challenge in the context of high school biology.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2010223
Funding Period: 
Sat, 08/01/2020 to Wed, 07/31/2024
Full Description: 

This project addresses the pressing need to more effectively organize STEM (science, technology, engineering, and mathematics) teaching and learning around "big ideas" that run through science disciplines. This need is forcefully advanced by policy leaders including the National Research Council and the College Board. They point out that learning is more effective when students organize and link information within a consistent knowledge framework, which is what big ideas should provide. Unfortunately, finding ways to teach big ideas effectively so they become useful as knowledge frameworks is a significant challenge. Deep structure modeling (DSM), the innovation advanced in this project, is designed to meet this challenge in the context of high school biology. In DSM, students learn a big idea as the underlying, or "deep" structure of a set of examples that contain the structure, but with varying outward details. As learners begin to apprehend the deep structure (i.e., the big idea) within the examples, they use the tools and procedures of scientific modeling to express and develop it. According to theories of learning that undergird DSM, the result of this process should be a big idea that is flexible, meaningful, and easy to express, thus providing an ideal framework for making sense of new information learners encounter (i.e., learning with the big idea). To the extent that this explanation is born out in rigorous research tests and within authentic curriculum materials, it contributes important knowledge about how teaching and learning can be organized around big ideas, and not only for deep structural modeling but for other instructional approaches as well.

This project has twin research and prototype development components. Both are taking place in the context of high school biology, in nine classrooms across three districts, supporting up to 610 students. The work focuses on three design features of DSM: (1) embedding model source materials with intuitive, mechanistic ideas; (2) supporting learners to abstract those ideas as a deep structure shared by a set of sources; and (3) representing this deep structure efficiently within the model. In combination, these features support students to understand an abstract, intuitively rich, and efficient knowledge structure that they subsequently use as a framework to interpret, organize, and link disciplinary content. A series of five research studies build on one another to develop knowledge about whether and how the design features bring about these anticipated effects. Earlier studies in the sequence are small-scale classroom experiments randomly assigning students to either deep structural modeling or to parallel, non modeling controls. Measures discriminate for the anticipated effects during learning and on posttests. Later studies use qualitative methods to carefully trace the anticipated effects over time and across topics. As a group, these studies are contributing generalized knowledge of how learners can effectively abstract and represent big ideas and how these ideas can be leveraged as frameworks for learning content with understanding. Two research-tested biology curriculum prototypes are being developed as the studies evolve: a quarter-year DSM biology curriculum centered on energy; and an eighth-year DSM unit centered on natural selection.

How Deep Structural Modeling Supports Learning with Big Ideas in Biology (Collaborative Research: Shemwell)

This project addresses the pressing need to more effectively organize STEM (science, technology, engineering, and mathematics) teaching and learning around "big ideas" that run through science disciplines. Unfortunately, finding ways to teach big ideas effectively so they become useful as knowledge frameworks is a significant challenge. Deep structure modeling (DSM), the innovation advanced in this project, is designed to meet this challenge in the context of high school biology.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2010334
Funding Period: 
Sat, 08/01/2020 to Wed, 07/31/2024
Full Description: 

This project addresses the pressing need to more effectively organize STEM (science, technology, engineering, and mathematics) teaching and learning around "big ideas" that run through science disciplines. This need is forcefully advanced by policy leaders including the National Research Council and the College Board. They point out that learning is more effective when students organize and link information within a consistent knowledge framework, which is what big ideas should provide. Unfortunately, finding ways to teach big ideas effectively so they become useful as knowledge frameworks is a significant challenge. Deep structure modeling (DSM), the innovation advanced in this project, is designed to meet this challenge in the context of high school biology. In DSM, students learn a big idea as the underlying, or "deep" structure of a set of examples that contain the structure, but with varying outward details. As learners begin to apprehend the deep structure (i.e., the big idea) within the examples, they use the tools and procedures of scientific modeling to express and develop it. According to theories of learning that undergird DSM, the result of this process should be a big idea that is flexible, meaningful, and easy to express, thus providing an ideal framework for making sense of new information learners encounter (i.e., learning with the big idea). To the extent that this explanation is born out in rigorous research tests and within authentic curriculum materials, it contributes important knowledge about how teaching and learning can be organized around big ideas, and not only for deep structural modeling but for other instructional approaches as well.

This project has twin research and prototype development components. Both are taking place in the context of high school biology, in nine classrooms across three districts, supporting up to 610 students. The work focuses on three design features of DSM: (1) embedding model source materials with intuitive, mechanistic ideas; (2) supporting learners to abstract those ideas as a deep structure shared by a set of sources; and (3) representing this deep structure efficiently within the model. In combination, these features support students to understand an abstract, intuitively rich, and efficient knowledge structure that they subsequently use as a framework to interpret, organize, and link disciplinary content. A series of five research studies build on one another to develop knowledge about whether and how the design features bring about these anticipated effects. Earlier studies in the sequence are small-scale classroom experiments randomly assigning students to either deep structural modeling or to parallel, non modeling controls. Measures discriminate for the anticipated effects during learning and on posttests. Later studies use qualitative methods to carefully trace the anticipated effects over time and across topics. As a group, these studies are contributing generalized knowledge of how learners can effectively abstract and represent big ideas and how these ideas can be leveraged as frameworks for learning content with understanding. Two research-tested biology curriculum prototypes are being developed as the studies evolve: a quarter-year DSM biology curriculum centered on energy; and an eighth-year DSM unit centered on natural selection.

Exploring Early Childhood Teachers' Abilities to Identify Computational Thinking Precursors to Strengthen Computer Science in Classrooms

This project will explore PK-2 teachers' content knowledge by investigating their understanding of the design and implementation of culturally relevant computer science learning activities for young children. The project team will design a replicable model of PK-2 teacher professional development to address the lack of research in early computer science education.

Lead Organization(s): 
Award Number: 
2006595
Funding Period: 
Tue, 09/01/2020 to Thu, 08/31/2023
Full Description: 

Strengthening computer science education is a national priority with special attention to increasing the number of teachers who can deliver computer science education in schools. Yet computer science education lacks the evidence to determine how teachers come to think about computational thinking (a problem-solving process) and how it could be integrated within their day-to-day classroom activities. For teachers of pre-kindergarten to 2nd (PK-2) grades, very little research has specifically addressed teacher learning. This oversight challenges the achievement of an equitable, culturally diverse, computationally empowered society. The project team will design a replicable model of PK-2 teacher professional development in San Marcos, Texas, to address the lack of research in early computer science education. The model will emphasize three aspects of teacher learning: a) exploration of and reflection on computer science and computational thinking skills and practices, b) noticing and naming computer science precursor skills and practices in early childhood learning, and c) collaborative design, implementation and assessment of learning activities aligned with standards across content areas. The project will explore PK-2 teachers' content knowledge by investigating their understanding of the design and implementation of culturally relevant computer science learning activities for young children. The project includes a two-week computational making and inquiry institute focused on algorithms and data in the context of citizen science and historical storytelling. The project also includes monthly classroom coaching sessions, and teacher meetups.

The research will include two cohorts of 15 PK-2 teachers recruited from the San Marcos Consolidated Independent School District (SMCISD) in years one and two of the project. The project incorporates a 3-phase professional development program to be run in two cycles for each cohort of teachers. Phase one (summer) includes a 2-week Computational Making and Inquiry Institute, phase two (school year) includes classroom observations and teacher meetups and phase three (late spring) includes an advanced computational thinking institute and a community education conference. Research and data collection on impacts will follow a mixed-methods approach based on a grounded theory design to document teachers learning. The mixed-methods approach will enable researchers to triangulate participants' acquisition of new knowledge and skills with their developing abilities to implement learning activities in practice. Data analysis will be ongoing, interweaving qualitative and quantitative methods. Qualitative data, including field notes, observations, interviews, and artifact assessments, will be analyzed by identifying analytical categories and their relationships. Quantitative data includes pre to post surveys administered at three-time points for each cohort. Inter-item correlations and scale reliabilities will be examined, and a repeated measures ANOVA will be used to assess mean change across time for each of five measures. Project results will be communicated via peer-reviewed journals, education newsletters, annual conferences, family and teacher meetups, and community art and culture events, as well as on social media, blogs, and education databases.

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