Statistics

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 Sat, 04/30/2022
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.

LabVenture - Revealing Systemic Impacts of a 12-Year Statewide Science Field Trip Program

This project will examine the impact of a 12-year statewide science field trip program called LabVenture, a hands-on program in discovery and inquiry that brings middle school students and teachers across the state of Maine to the Gulf of Maine Research Institute (GMRI) to become fully immersed in explorations into the complexities of local marine science ecosystems.

Award Number: 
1811452
Funding Period: 
Sat, 09/01/2018 to Thu, 08/31/2023
Full Description: 

This research in service to practice project will examine the impact of a 12-year statewide science field trip program called LabVenture. This hands-on program in discovery and inquiry brings middle school students and teachers across the state of Maine to the Gulf of Maine Research Institute (GMRI) in Portland, Maine to become fully immersed in explorations into the complexities of local marine science ecosystems. These intensive field trip experiences are led by informal educators and facilitated entirely within informal contexts at GMRI. Approximately 70% of all fifth and sixth grade students in Maine participate in the program each year and more than 120,000 students have attended since the program's inception in 2005. Unfortunately, little is known to date on how the program has influenced practice and learning ecosystems within formal, informal, and community contexts. As such, this research in service to practice project will employ an innovative research approach to understand and advance knowledge on the short and long-term impacts of the program within different contexts. If proven effective, the LabVenture program will elucidate the potential benefits of a large-scale field trip program implemented systemically across a community over time and serve as a reputable model for statewide adoption of similar programs seeking innovative strategies to connect formal and informal science learning to achieve notable positive shifts in their local, statewide, or regional STEM learning ecosystems.

Over the four-year project duration, the project will reach all 16 counties in the State of Maine. The research design includes a multi-step, multi-method approach to gain insight on the primary research questions. The initial research will focus on extant data and retrospective data sources codified over the 12-year history of the program. The research will then be expanded to garner prospective data on current participating students, teachers, and informal educators. Finally, a community study will be conducted to understand the potential broader impacts of the program. Each phase of the research will consider the following overarching research questions are: (1) How do formal and informal practitioners perceive the value and purposes of the field trip program and field trip experiences more broadly (field trip ontology)? (2) To what degree do short-term field trip experiences in informal contexts effect cognitive and affective outcomes for students? (3) How are community characteristics (e.g., population, distance from GMRI, proximity to the coast) related to ongoing engagement with the field trip program? (4) What are aspects of the ongoing field trip program that might embed it as an integral element of community culture (e.g., community awareness of a shared social experience)? (5) To what degree does a field trip experience that is shared by schools across a state lead to a traceable change that can be measured for those who participated and across the broader community? and (6) In what ways, if at all, can a field trip experience that occurs in informal contexts have an influence on the larger learning ecosystem (e.g., the Maine education system)? Each phase of the research will be led by a team of researchers with the requisite expertise in the methodologies and contexts required to carry out that particular aspect of the research (i.e., retrospective study, prospective study, community study). In addition, evaluation and practitioner panels of experts will provide expertise and guidance on the research, evaluation, and project implementation. The project will culminate with a practitioner convening, to share project findings more broadly with formal and informal practitioners, and promote transfer from research to practice. Additional dissemination strategies include conferences, network meetings, and peer-reviewed publications.

Methods for Assessing Replication

The goal of this project is to formalize subjective ideas about the important concept of replication, provide statistical analyses for evaluating replication studies, provide properties for evaluating the conclusiveness of replication studies, and provide principles for designing conclusive and efficient programs of replication studies.

Lead Organization(s): 
Award Number: 
1841075
Funding Period: 
Sat, 09/01/2018 to Tue, 08/31/2021
Full Description: 

Replication of prior findings and results is a fundamental feature of science and is part of the logic supporting the claim that science is self-correcting. However, there is little prior research on the methodology for studying replication. Research involving meta-analysis and systematic reviews that summarizes a collection of research studies is more common. However, the question of whether the findings from a set of experimental studies replicate one another has received less attention. There is no clearly defined and widely accepted definition of a successful replication study or statistical literature providing methodological guidelines on how to design single replication studies or a set of replication studies. The research proposed here builds this much needed methodology.

The goal of this project is to formalize subjective ideas about the important concept of replication, provide statistical analyses for evaluating replication studies, provide properties for evaluating the conclusiveness of replication studies, and provide principles for designing conclusive and efficient programs of replication studies. It addresses three fundamental problems. The first is how to define replication: What, precisely, should it mean to say that the results in a collection of studies replicate one another? Second, given a definition of replication, what statistical analyses should be done to decide whether the collection of studies replicate one another and what are the properties of these analyses (e.g., sensitivity or statistical power)? Third, how should one or more replication studies be designed to provide conclusive answers to questions of replication? The project has the potential for impact on a range of empirical sciences by providing statistical tools to evaluate the replicability of experimental findings, assessing the conclusiveness of replication attempts, and developing software to help plan programs of replication studies that can provide conclusive evidence of replicability of scientific findings.

Strengthening Data Literacy across the Curriculum (SDLC)

This project is developing and studying high school curriculum modules that integrate social justice topics with statistical data investigations to promote skills and interest in data science among underrepresented groups in STEM.

Project Email: 
Award Number: 
1813956
Funding Period: 
Sun, 07/01/2018 to Wed, 06/30/2021
Full Description: 

The Strengthening Data Literacy across the Curriculum (SDLC) project is an exploratory/early stage design and development effort that aims to promote understanding of core statistical concepts and interest in quantitative data analysis among high school students from underrepresented groups in STEM. Led by a collaboration of researchers and developers at Education Development Center (EDC), statistics educators at California Polytechnic State University (Cal Poly), and technology developers at The Concord Consortium, the project is creating and studying a set of curriculum modules targeted to high school students who are taking mathematics or statistics classes that are not at advanced-placement (AP) levels. Iteratively developed and tested in collaboration with high school statistics and social studies teachers, the modules consist of applied data investigations structured around a four-step data investigation cycle that engage students in explorations of authentic social science issues using large-scale data sets from the U.S. Census Bureau. The project hypothesizes that students who engage in guided investigations using data visualization tools to explore and visualize statistical concepts may develop deeper understandings of these concepts as well as the data investigation process. Similarly, high school students – particularly those from historically marginalized groups who are underrepresented in STEM fields – may develop greater interest in statistics when they can use data to examine patterns of social and economic inequality and questions related to social justice.

One module, Investigating Income Inequality in the U.S., focuses on describing, comparing, and making sense of quantitative variables. Students deepen their understanding of this content by investigating questions such as: How have incomes for higher- and lower-income individuals in the U.S. changed over time? How much income inequality exists between males and females in the U.S.? Does education explain the wage gap between males and females? Another module, Investigating Immigration to the U.S., focuses on describing, comparing, and making sense of categorical variables. Students investigate questions such as: Are there more immigrants in the U.S. today than in previous years? Where have immigrants to the U.S. come from, now and in the past? Are immigrants as likely as the U.S. born to be participating in the labor force, after adjusting for education? Students conduct these analyses using the Common Online Data Analysis Platform (CODAP), an open-source set of tools that supports data visualization and conceptual understanding of statistical ideas over calculations. Lessons encourage collaborative inquiry and provide students with experiences in multivariable analysis—an important domain that is underemphasized in current high school mathematics and statistics curricula but critical for analyzing data in a big-data world.

The project is using a mixed methods approach to study three primary research questions: 1) What is the feasibility of implementing SDLC modules, and what supports may teachers and students need to use the modules? 2) In what ways may different features and components of the SDLC modules help to promote positive student learning and interest outcomes? 3) To what extent do students show greater interest in statistics and data analysis, as well as improved understandings of target statistical concepts, after module use? To investigate these questions, the project has worked with 12 mathematics and six social studies teachers in diverse public high schools in Massachusetts and California to conduct iterative research with over 600 students. Through this work, the project aims to build knowledge of curriculum-based approaches that prepare and attract more diverse populations to data science fields.


 Project Videos

2021 STEM for All Video Showcase

Title: Promoting Data Literacy with Social Justice Investigations

Presenter(s): Josephine Louie, Beth Chance, Emily Fagan, Soma Roy, & Jennifer Stiles


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