Middle School

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.

Supporting Teachers to Teach Mathematics through Problem Posing

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

Lead Organization(s): 
Award Number: 
2101552
Funding Period: 
Sun, 08/01/2021 to Thu, 07/31/2025
Full Description: 

This project aims to support teachers to engage their students in mathematical problem posing (problem-posing-based learning, or P-PBL). P-PBL is a powerful approach to the teaching and learning of mathematics, and provides students with opportunities to engage in authentic mathematical practices. For example, conjecturing in mathematics, a form of problem posing, often plays an important role in solving complex problems, and problem posing is an important component of mathematical modeling. Yet despite its importance, widely used curriculum materials fail to incorporate P-PBL in substantial and consistent ways, leaving teachers with few resources to enact this process. This project will develop problem-posing lessons and illustrative cases of teachers implementing P-PBL that will not only support teachers to develop a vision of what P-PBL looks like and how to implement it in their own classrooms, but will also serve as rich resources for professional development (PD) providers. This project will generate valuable findings about teaching using problem posing for district administrators, mathematics teachers, educators, and researchers as well as curriculum developers and policy makers. The team will develop and pilot a set of 20−30 research-based P-PBL cases that provide critical details for the implementation of P-PBL and reveal “lessons learned” from the development process.

The project promises broader impact on the field of mathematics education as the first goal is to support teachers to teach mathematics through engaging their students in mathematical problem posing. By guiding students to construct and investigate their own problems, P-PBL both helps to create mathematical learning opportunities and develops students’ mathematical agency and positive mathematical identities. A networked improvement community of teachers and researchers will integrate problem posing into daily mathematics instruction and continuously improve the quality of P-PBL through iterative task and lesson design. The intellectual merit of this project is its contribution of new and important insights about teaching mathematics through problem posing. This will be realized through the second project goal which is to longitudinally investigate the promise of supporting teachers to teach with P-PBL for enhancing teachers’ instructional practice and students’ learning. A quasi-experimental design coupled with design-based research methodology and improvement science will be used to understand how, when, and why P-PBL works in practice. Specifically, we plan to follow a sample of 36 teachers and their approximately 3,600 students from six middle schools for multiple years to longitudinally explore the promise of P-PBL for developing teachers’ beliefs about problem posing, their beliefs about P-PBL, and their actual instructional practice. We will also investigate students’ learning as measured by problem-posing performance, problem-solving performance, and mathematics disposition. The findings of the project will add not only to the field’s understanding of the promise of supporting teachers to integrate P-PBL into their mathematics instruction, but also to its understanding of the challenges that teachers face when engaging in a networked improvement community that is focused on improving tasks and lessons by integrating P-PBL.

Practice-Driven Professional Development for Algebra Teachers

This project seeks to develop a personalized, scalable PD approach that centers on and builds from algebra teachers’ practices and individual strengths. The project will focus its PD efforts on instructional actions that are tailored to teachers' existing practice, can be readily adopted, and are easily accessible.

Lead Organization(s): 
Award Number: 
2101508
Funding Period: 
Thu, 07/01/2021 to Mon, 06/30/2025
Full Description: 

Professional development (PD) is a direct attempt to improve the quality of instruction for teachers already in the classroom. Traditional PD is typically costly in terms of time and money, and efforts tend to be delivered as a one-size-fits-all approach. Furthermore, for teachers who adopt novel techniques such as flipped instruction, there may be few resources to support their efforts. This project seeks to develop a personalized, scalable PD approach that centers on and builds from algebra teachers’ practices and individual strengths. The project will focus its PD efforts on instructional actions that are tailored to teachers' existing practice, can be readily adopted, and are easily accessible. The project team have termed such instructional actions high-uptake practices. The project will develop and field test PD materials to support algebra teachers at scale via these high-uptake practices.

In addition to developing the PD materials, the project team will research the efficacy of this PD model in terms of student learning outcomes and teacher instructional practices in approximately 60 algebra classrooms. The main data sources will include teacher observation data, teacher interviews and surveys, student pre/posttests, student surveys, and PD analytics. The research will characterize the immediate and longer term impacts of the PD on teachers’ instructional practices; and characterize the impact of teachers’ participation in the PD on students’ learning outcomes and engagement. The research questions include: 1) In what ways does teachers’ participation in the PD impact their instructional practices? 2) Do students of teachers who participate in the PD demonstrate differential growth in learning outcomes? 3) Do students of teachers who participate in the PD have increased rates of homework completion?; and 4) Do students of teachers who participate in the PD have increased engagement during individual work time? In meeting both our PD development and research aims, this project will contribute knowledge about the effectiveness of an incremental, practice-driven approach to PD and instructional change.

Improving Professional Development in Mathematics by Understanding the Mechanisms that Translate Teacher Learning into Student Learning

This project explores the mechanisms by which teachers translate what they learn from professional development into their teaching practice. The goal of this project is to study how the knowledge and skills teachers acquire during professional development (PD) translate into more conceptually oriented mathematics teaching and, in turn, into increased student learning.

Lead Organization(s): 
Award Number: 
2100617
Funding Period: 
Wed, 09/01/2021 to Sun, 08/31/2025
Full Description: 

A great deal is known about the effects of mathematics teacher professional development on teachers' mathematical knowledge for teaching. While some professional development programs show meaningful changes in teacher knowledge, these changes do not always translate into changes in teacher practice. This project explores the mechanisms by which teachers translate what they learn from professional development into their teaching practice. The goal of this project is to study how the knowledge and skills teachers acquire during professional development (PD) translate into more conceptually oriented mathematics teaching and, in turn, into increased student learning. The project builds on a promising video-based PD that engages teachers in analyzing videos of classroom mathematics teaching. Previous research indicates that teachers who can analyze teaching by focusing on the nature of the mathematical learning opportunities experienced by students often teach more effectively. The researchers aim to better understand the path teachers follow as they develop this professional competency and translate it into more ambitious teaching that supports richer student learning. The lack of understanding of how a PD program can reach students is a significant barrier to improving the effectiveness of PD. To build this understanding, the researchers aim to test and refine an implementation theory that specifies the obstacles teachers face as they apply their learning to their classroom teaching and the contextual supports that help teachers surmount these obstacles. Lessons learned from understanding the factors that impact the effects of PD will help educators design PD programs that maximize the translation of teacher learning into student learning.

The project will recruit and support a cohort of teachers, grades 4–5 (n=40) and grades 6–7 (n=40) for three years to trace growth in teacher learning, changes in teaching practices, and increases in student learning. The PD will be provided throughout the year for three consecutive years. The researchers will focus on two mathematics topics with a third topic assessed to measure transfer effects. Several cycles of lesson analysis will occur each year, with small grade-alike curriculum-alike groups assisted by trained coaches to help teachers translate their growing analysis skills into planning, implementing, and reflecting on their own lessons. Additional days will be allocated each year to assist the larger groups of teachers in developing pedagogical content knowledge for analyzing teaching. The research focuses on the following questions: 1) What are the relationships between teacher learning from PD, classroom teaching, and student learning, how do hypothesized mediating variables affect these relationships, and how do these relationships change as teachers become more competent at analyzing teaching?; and 2) How do teachers describe the obstacles and supports they believe affect their learning and teaching, and how do these obstacles and supports deepen and broaden the implementation theory? Multi-level modeling will be used to address the first question, taking into account for the nested nature of the data, in order to test a model that hypothesizes direct and indirect relationships between teacher learning and teaching practice and, in turn, teaching practice and student learning. Teachers will take assessments each year, for each mathematics topic, on the analysis of teaching skills, on the use of teaching practices, and on students’ learning. Cluster analysis will be used to explore the extent to which the relationship between learning to analyze the mathematics of a lesson, teaching quality, and student achievement may be different for different teachers based on measured characteristics. Longitudinal analysis will be used to examine the theoretical relationships among variables in the hypothesized path model. Teachers’ mathematical knowledge for teaching, lesson planning, and textbook curricular material use will be examined as possible mediating variables between teacher learning and teaching practice. To address the second research question, participants will engage in annual interviews about the factors they are obstacles to doing this work and about the supports within and outside of the PD that ameliorate these obstacles. Quantitative analyses will test the relationships between the obstacles and supports with teacher learning and classroom teaching. Through qualitative analyses, the obstacles and supports to translating professional learning into practice will be further articulated. These obstacles and supports, along with the professional development model, will be disseminated to the field.

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

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: 
2101668
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.

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

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: 
2101667
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.

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