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Developing a Modeling Orientation to Science: Teaching and Learning Variability and Change in Ecosystems (Collaborative Research: Lehrer)

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

Lead Organization(s): 
Award Number: 
2010207
Funding Period: 
Tue, 09/01/2020 to Thu, 08/31/2023
Full Description: 

There is an ongoing need to find ways to make science relevant for school students and an increasing need to support student interpretation of large data sets. This project addresses these needs by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts. Students construct and critique models that they and peers invent and, through the lens of models, develop foundational knowledge about the roles of variability and change in ecosystem functioning, as well as the roles of models and argumentation in scientific practice. The context for students' work is a set of citizen science-based investigations of changes in ecosystems in Maine conducted in twelve collaborating classrooms. The project studies how and to what extent students' use of different forms of modeling emerges from and informs how they investigate ecosystems. A parallel research effort investigates how and to what extent the development of teachers' comfort and proficiency with modeling changes students' engagement in these forms of modeling and students' understandings of ecosystems. A key contribution of the project is capitalizing on the Gulf of Maine Research Institutes's Ecosystem Investigation Network's citizen science field research to ground for middle school students the need to invent, revise, and contest models about real ecosystems. The understandings that result from the project's research provide evidence toward first, scaling the learning experiences to the network of 500+ teachers who are part of the Ecosystem Investigation Network, and, second, replication by programs nationally that aim to engage students in data-rich, field-based ecological investigations.

The investigation takes place in twelve collaborating middle-school classrooms, drawn from the network of 500+ Maine teachers trained in Maine's Ecosystem Investigation Network. Over the course of their field investigations, students engage in the construction, critique, and revision of three forms of modeling that play central roles in ecology: microcosms, system dynamics, and data modeling. Two innovations are introduced over the course of the project. The first is focused on enriching classroom supports for engaging in multiple forms of modeling. The second involves enhancing middle school teachers' learning about modeling, especially in the context of large data citizen science investigations. The study uses a mixed methods approach to explore the impact of the innovations on the experiences and understandings of both teachers and students. Instruments include teacher interviews and questionnaires, student interviews, and classroom observation. The understandings that result from the project's research will inform the design of professional development for teachers around data analysis and interpretation, and around how student understanding of modeling develops with sustained support, both of which are practices at the heart of scientific literacy.

Developing a Modeling Orientation to Science: Teaching and Learning Variability and Change in Ecosystems (Collaborative Research: Peake)

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

Partner Organization(s): 
Award Number: 
2010119
Funding Period: 
Tue, 09/01/2020 to Thu, 08/31/2023
Full Description: 

There is an ongoing need to find ways to make science relevant for school students and an increasing need to support student interpretation of large data sets. This project addresses these needs by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts. Students construct and critique models that they and peers invent and, through the lens of models, develop foundational knowledge about the roles of variability and change in ecosystem functioning, as well as the roles of models and argumentation in scientific practice. The context for students' work is a set of citizen science-based investigations of changes in ecosystems in Maine conducted in twelve collaborating classrooms. The project studies how and to what extent students' use of different forms of modeling emerges from and informs how they investigate ecosystems. A parallel research effort investigates how and to what extent the development of teachers' comfort and proficiency with modeling changes students' engagement in these forms of modeling and students' understandings of ecosystems. A key contribution of the project is capitalizing on the Gulf of Maine Research Institutes's Ecosystem Investigation Network's citizen science field research to ground for middle school students the need to invent, revise, and contest models about real ecosystems. The understandings that result from the project's research provide evidence toward first, scaling the learning experiences to the network of 500+ teachers who are part of the Ecosystem Investigation Network, and, second, replication by programs nationally that aim to engage students in data-rich, field-based ecological investigations.

The investigation takes place in twelve collaborating middle-school classrooms, drawn from the network of 500+ Maine teachers trained in Maine's Ecosystem Investigation Network. Over the course of their field investigations, students engage in the construction, critique, and revision of three forms of modeling that play central roles in ecology: microcosms, system dynamics, and data modeling. Two innovations are introduced over the course of the project. The first is focused on enriching classroom supports for engaging in multiple forms of modeling. The second involves enhancing middle school teachers' learning about modeling, especially in the context of large data citizen science investigations. The study uses a mixed methods approach to explore the impact of the innovations on the experiences and understandings of both teachers and students. Instruments include teacher interviews and questionnaires, student interviews, and classroom observation. The understandings that result from the project's research will inform the design of professional development for teachers around data analysis and interpretation, and around how student understanding of modeling develops with sustained support, both of which are practices at the heart of scientific literacy.

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.

Pandemic Learning Loss in U.S. High Schools: A National Examination of Student Experiences

As a result of the COVID-19 pandemic, schools across much of the U.S. have been closed since mid-March of 2020 and many students have been attempting to continue their education away from schools. Student experiences across the country are likely to be highly variable depending on a variety of factors at the individual, home, school, district, and state levels. This project will use two, nationally representative, existing databases of high school students to study their experiences in STEM education during the COVID-19 pandemic.

Lead Organization(s): 
Award Number: 
2030436
Funding Period: 
Fri, 05/15/2020 to Fri, 04/30/2021
Full Description: 

As a result of the COVID-19 pandemic, schools across much of the U.S. have been closed since mid-March of 2020 and many students have been attempting to continue their education away from schools. Student experiences across the country are likely to be highly variable depending on a variety of factors at the individual, home, school, district, and state levels. This project will use two, nationally representative, existing databases of high school students to study their experiences in STEM education during the COVID-19 pandemic. The study intends to ascertain whether students are taking STEM courses in high school, the nature of the changes made to the courses, and their plans for the fall. The researchers will identify the electronic learning platforms in use, and other modifications made to STEM experiences in formal and informal settings. The study is particularly interested in finding patterns of inequities for students in various demographic groups underserved in STEM and who may be most likely to be affected by a hiatus in formal education.

This study will collect data using the AmeriSpeak Teen Panel of approximately 2,000 students aged 13 to 17 and the Infinite Campus Student Information System with a sample of approximately 2.5 million high school students. The data sets allow for relevant comparisons of student experiences prior to and during the COVID-19 pandemic and offer unique perspectives with nationally representative samples of U.S. high school students. New data collection will focus on formal and informal STEM learning opportunities, engagement, STEM course taking, the nature and frequency of instruction, interactions with teachers, interest in STEM, and career aspirations. Weighted data will be analyzed using descriptive statistics and within and between district analysis will be conducted to assess group differences. Estimates of between group pandemic learning loss will be provided with attention to demographic factors.

This RAPID award is made by the DRK-12 program in the Division of Research on Learning. The Discovery Research PreK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers, through the research and development of new innovations and approaches. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for the projects.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

 

 

 

 

CAREER: Promoting Equitable and Inclusive STEM Contexts in High School

This project focuses on fostering equitable and inclusive STEM contexts with attention to documenting and reducing adolescents' experiences of harassment, bias, prejudice and stereotyping. This research will contribute to understanding of the current STEM educational climates in high schools and will help to identify factors that promote resilience in the STEM contexts, documenting how K-12 educators can structure their classrooms and schools to foster success of all students in STEM classes.

Award Number: 
1941992
Funding Period: 
Sat, 02/01/2020 to Fri, 01/31/2025
Full Description: 

This project focuses on fostering equitable and inclusive STEM contexts with attention to documenting and reducing adolescents' experiences of harassment, bias, prejudice and stereotyping. An important barrier to persistence in STEM fields for marginalized groups, including women and ethnic minorities, relates to a culture in many STEM organizations, such as academic institutions, that fosters discrimination, harassment and prejudicial treatment of those from underrepresented groups. This research will contribute to understanding of the current STEM educational climates in high schools and will help to identify factors that promote resilience in the STEM contexts, documenting how K-12 educators can structure their classrooms and schools to foster success of all students in STEM classes. Further, this work will explore how to create schools where students stand-up for each other and support each other so that any student who is interested will feel welcome in STEM classes and programs.

This research aims to examine cultures of discrimination and harassment in STEM contexts with attention to: 1) assessing STEM climates in high schools in order to identify the character of discrimination and harassment, 2) understanding how youth think about these instances of bias and discrimination; 3) identifying pathways to resilience for underrepresented youth pursuing STEM interests, and 4) testing an intervention to promote bystander intervention from those who witness discrimination and harassment in STEM contexts. This research will take an intersectional approach recognizing that those who are marginalized by multiple dimensions of their identity may experience STEM contexts differently than those who are marginalized by one dimension of their identity. Because adolescence is a critical developmental period during which youth are forming their attitudes, orientations and lifelong behaviors, this research will attend to issues of bias and discrimination well before individuals enter college STEM classrooms or the STEM workforce: namely, during high school. Further, this work will examine the creation of equitable STEM climates in both college-preparation classes as well as workforce development STEM programs offered though or in partnership with high schools. This research will provide clear evidence to document the current culture of STEM contexts in high schools, using mixed methods, including surveys, qualitative interviews and longitudinal measurement. Further, the project will involve development and implementation of an intervention, which will provide the first test of whether bystander intervention can be fostered in STEM students and will involve training STEM students in key 21st century skills, such as social-cognitive capacities and interpersonal skills, enabling them to speak up and support peers from marginalized backgrounds when they observe discrimination and harassment.

CAREER: Supporting Model Based Inference as an Integrated Effort Between Mathematics and Science

This project will design opportunities for mathematics and science teachers to coordinate their instruction to support a more coherent approach to teaching statistical model-based inference in middle school. It will prepare teachers to help more students develop a deeper understanding of ideas and practices related to measurement, data, variability, and inference and to use these tools to generate knowledge about the natural world.

Award Number: 
1942770
Funding Period: 
Sat, 02/01/2020 to Fri, 01/31/2025
Full Description: 

This project will design opportunities for mathematics and science teachers to coordinate their instruction to support a more coherent approach to teaching statistical model-based inference in middle school. It will prepare teachers to help more students develop a deeper understanding of ideas and practices related to measurement, data, variability, and inference. Since there is little research to show how to productively coordinate learning experiences across disciplinary boundaries of mathematics and science education, this project will address this gap by: (1) creating design principles for integrating instruction about statistical model-based inference in middle grades that coordinates data modeling instruction in mathematics classes with ecology instruction in science classes; (2) generating longitudinal (2 years) evidence about how mathematical and scientific ideas co-develop as students make use of increasingly sophisticated modeling and inferential practices; and (3) designing four integrated units that coordinate instruction across mathematics and science classes in 6th and 7th grade to support statistical model-based inference.

This project will use a multi-phase design-based research approach that will begin by observing teachers' current practices related to statistical model-based inference. Information from this phase will help guide researchers, mathematics teachers, and science teachers in co-designing units that integrate data modeling instruction in mathematics classes with ecological investigations in science classes. This project will directly observe students' thinking and learning across 6th and 7th grades through sample classroom lessons, written assessment items, and interviews. Data from these aspects of the study will generate evidence about how students make use of mathematical ideas in science class and how their ecological investigations in science class provoke a need for new mathematical tools to make inferences. The resulting model will integrate mathematics and science learning in productive ways that are sensitive to both specific disciplinary learning goals and the ways that these ideas and practices can provide a better approximation for students to knowledge generating practices in STEM disciplines.

Invigorating Statistics Teacher Education Through Professional Online Learning (InSTEP)

This project seeks to strengthen the teaching of statistics and data science in grades 6-12 through the design and implementation of an online professional learning environment for teachers. The professional learning environment aims to support in-service teachers in developing stronger content knowledge related to statistics, and knowledge of how to effectively teach statistics in their classrooms.

Project Email: 
Award Number: 
1908760
Funding Period: 
Thu, 08/01/2019 to Mon, 07/31/2023
Project Evaluator: 
Full Description: 

Implementing meaningful statistics education in middle and high schools has been a persistent challenge in the United States. Statistics and data science are critical domains for STEM careers and the general data literacy of the citizenry. This project seeks to strengthen the teaching of statistics and data science in grades 6-12 through the design and implementation of an online professional learning environment for teachers. The professional learning environment aims to support in-service teachers in developing stronger content knowledge related to statistics, and knowledge of how to effectively teach statistics in their classrooms. The project will also evaluate a model of professional development that integrates personalized online learning and microcredentialing (earning small-scale certifications) to better understand its effectiveness in supporting teacher learning. The project will draw from previous work to assemble online modules that engage teachers in doing high-quality statistics and data science tasks, the analysis of video of teachers' and students' work with those tasks, learning a pedagogical framework for teachers to implement the tasks, and exploring guidelines for identifying and developing high-quality statistics and data science tasks. The project will study teacher learning through the use of these modules, and the pathways that teachers choose through them to understand the effectiveness of the model.

The project builds on previous work by the investigators to develop research-based teacher professional development modules that support learning about statistics and statistics education in grades 6-12. Materials currently developed include a series of microcredentials with design features consistent with research on effective teacher professional development. They include opportunities for teachers to engage with statistics content appropriate to the target grade levels they teach, active learning opportunities that engage them with teachers in similar contexts, and a coherent focus that builds on the knowledge and experience teachers bring to the table. The project will take place in iterative phases, beginning with focus groups of middle and high school teachers and district leaders based on first drafts of the materials. This will be followed by cognitive interviews with teachers who engage in the microcredential ecosystem which will inform modifications to the system. Following this phase, cohorts of teachers (25 in the first cohort, 75 in the second) will participate in scaffolded professional development engagement with the materials, and will be assessed with respect to changes in their knowledge and practice. The project will assess changes in teacher knowledge using reliable and valid measures of statistics knowledge and practice. Data will be collected from the online platform regarding teacher engagement and usage to better understand usage and pathways through the materials. The professional learning platform will be made available as a free and open online source at the close of the project.

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Building Students' Data Literacy through the Co-design of Curriculum by Mathematics and Art Teachers (Collaborative Research: Matuk)

This project aims to enact and study the co-design of classroom activities by mathematics and visual arts teachers to promote middle school students' data literacy.

Award Number: 
1908557
Funding Period: 
Mon, 07/01/2019 to Wed, 06/30/2021
Full Description: 

The aim of this project is to enact and study a process in which middle school teachers of mathematics and visual arts co-design and teach activities that combine math and art to teach data science. Many existing efforts to promote data literacy are grounded in mathematical concepts of central tendency and variation, and typically are narrowly focused in single subject domains. Taking an art-based perspective on data science has the potential to promote student relevance, accessibility, engagement, reasoning, and meaning-making with data science. Moreover, visualization technology has advanced to a degree that the relation between the information in data and visual aesthetic can be leveraged easily. To explore the opportunity this offers, research on this project will examine how to equip teachers to develop such interdisciplinary pedagogical approaches to cultivate their students' data literacy. This exploratory project will provide support for 12 teachers during summer workshops and during the school year as these teachers implement their co-designed units in their classrooms. The work addresses the following questions: (1) How do we support effective co-design of data literacy units among art teachers, mathematics teachers, and researchers? (2) How are teachers able to use the unit materials in their classrooms to engage students in data literacy? And (3) How does an art-based approach support students' data literacy? Answers to these questions will build an understanding of how to support interdisciplinary curriculum design collaborations among researchers and teachers. They will also show how art-integrated, maker-oriented activities can support middle school learners' data literacy development; and how to design technologies that are accessible and powerful to teachers and learners in these interdisciplinary environments.

Through summer workshops and year-round design collaborations, the project will iteratively design, test and refine four units for middle school classrooms, including activities, tools, and assessments, to promote students' data literacy. Data will be collected from co-design sessions as well as classroom-enactments, and will include observations, video/audio recordings, student- and teacher-generated artifacts, and pre and post assessments of students' knowledge and self-efficacy. Mixed methods analyses of these data, and syntheses of findings across participants, classroom enactments, and project years, will explore effective ways to support co-design among art teachers, mathematics teachers, and researchers; and the impact of art-integrated activities on students' data literacy. This project will reach 12 teachers and their students across 6 New York city schools. By building capacity and knowledge about how to initiate and sustain teachers' interdisciplinary curriculum collaborations, the project will have broader impact. Refined project materials, including pedagogical approaches, toolkits and adaptable classroom activities, will be disseminated to facilitate classroom adoption by other educators who wish to undertake similar art-integrated data literacy curriculum design collaborations, and will thus ultimately broaden participation in data science among diverse youth within and beyond New York City.

Building Students' Data Literacy through the Co-design of Curriculum by Mathematics and Art Teachers (Collaborative Research: Vacca)

The aim of this project is to enact and study a process in which middle school teachers of mathematics and visual arts co-design and teach activities that combine math and art to teach data science.

Lead Organization(s): 
Award Number: 
1908142
Funding Period: 
Mon, 07/01/2019 to Wed, 06/30/2021
Full Description: 

The aim of this project is to enact and study a process in which middle school teachers of mathematics and visual arts co-design and teach activities that combine math and art to teach data science. Many existing efforts to promote data literacy are grounded in mathematical concepts of central tendency and variation, and typically are narrowly focused in single subject domains. Taking an art-based perspective on data science has the potential to promote student relevance, accessibility, engagement, reasoning, and meaning-making with data science. Moreover, visualization technology has advanced to a degree that the relation between the information in data and visual aesthetic can be leveraged easily. To explore the opportunity this offers, research on this project will examine how to equip teachers to develop such interdisciplinary pedagogical approaches to cultivate their students' data literacy. This exploratory project will provide support for 12 teachers during summer workshops and during the school year as these teachers implement their co-designed units in their classrooms. The work addresses the following questions: (1) How do we support effective co-design of data literacy units among art teachers, mathematics teachers, and researchers? (2) How are teachers able to use the unit materials in their classrooms to engage students in data literacy? And (3) How does an art-based approach support students' data literacy? Answers to these questions will build an understanding of how to support interdisciplinary curriculum design collaborations among researchers and teachers. They will also show how art-integrated, maker-oriented activities can support middle school learners' data literacy development; and how to design technologies that are accessible and powerful to teachers and learners in these interdisciplinary environments.

Through summer workshops and year-round design collaborations, the project will iteratively design, test and refine four units for middle school classrooms, including activities, tools, and assessments, to promote students' data literacy. Data will be collected from co-design sessions as well as classroom-enactments, and will include observations, video/audio recordings, student- and teacher-generated artifacts, and pre and post assessments of students' knowledge and self-efficacy. Mixed methods analyses of these data, and syntheses of findings across participants, classroom enactments, and project years, will explore effective ways to support co-design among art teachers, mathematics teachers, and researchers; and the impact of art-integrated activities on students' data literacy. This project will reach 12 teachers and their students across 6 New York city schools. By building capacity and knowledge about how to initiate and sustain teachers' interdisciplinary curriculum collaborations, the project will have broader impact. Refined project materials, including pedagogical approaches, toolkits and adaptable classroom activities, will be disseminated to facilitate classroom adoption by other educators who wish to undertake similar art-integrated data literacy curriculum design collaborations, and will thus ultimately broaden participation in data science among diverse youth within and beyond New York City.

Building Students' Data Literacy through the Co-design of Curriculum by Mathematics and Art Teachers (Collaborative Research: Silander)

The aim of this project is to enact and study a process in which middle school teachers of mathematics and visual arts co-design and teach activities that combine math and art to teach data science.

Award Number: 
1908030
Funding Period: 
Mon, 07/01/2019 to Wed, 06/30/2021
Full Description: 

The aim of this project is to enact and study a process in which middle school teachers of mathematics and visual arts co-design and teach activities that combine math and art to teach data science. Many existing efforts to promote data literacy are grounded in mathematical concepts of central tendency and variation, and typically are narrowly focused in single subject domains. Taking an art-based perspective on data science has the potential to promote student relevance, accessibility, engagement, reasoning, and meaning-making with data science. Moreover, visualization technology has advanced to a degree that the relation between the information in data and visual aesthetic can be leveraged easily. To explore the opportunity this offers, research on this project will examine how to equip teachers to develop such interdisciplinary pedagogical approaches to cultivate their students' data literacy. This exploratory project will provide support for 12 teachers during summer workshops and during the school year as these teachers implement their co-designed units in their classrooms. The work addresses the following questions: (1) How do we support effective co-design of data literacy units among art teachers, mathematics teachers, and researchers? (2) How are teachers able to use the unit materials in their classrooms to engage students in data literacy? And (3) How does an art-based approach support students' data literacy? Answers to these questions will build an understanding of how to support interdisciplinary curriculum design collaborations among researchers and teachers. They will also show how art-integrated, maker-oriented activities can support middle school learners' data literacy development; and how to design technologies that are accessible and powerful to teachers and learners in these interdisciplinary environments.

Through summer workshops and year-round design collaborations, the project will iteratively design, test and refine four units for middle school classrooms, including activities, tools, and assessments, to promote students' data literacy. Data will be collected from co-design sessions as well as classroom-enactments, and will include observations, video/audio recordings, student- and teacher-generated artifacts, and pre and post assessments of students' knowledge and self-efficacy. Mixed methods analyses of these data, and syntheses of findings across participants, classroom enactments, and project years, will explore effective ways to support co-design among art teachers, mathematics teachers, and researchers; and the impact of art-integrated activities on students' data literacy. This project will reach 12 teachers and their students across 6 New York city schools. By building capacity and knowledge about how to initiate and sustain teachers' interdisciplinary curriculum collaborations, the project will have broader impact. Refined project materials, including pedagogical approaches, toolkits and adaptable classroom activities, will be disseminated to facilitate classroom adoption by other educators who wish to undertake similar art-integrated data literacy curriculum design collaborations, and will thus ultimately broaden participation in data science among diverse youth within and beyond New York City.

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