Statistics

Zoom In! Learning Science with Data

This project will address the need for high quality evidence-based models, practices, and tools for high school teachers and the development of students' problem solving and analytical skills by leveraging novel research and design approaches using digital tools and two well-established online instructional platforms: Zoom In and Common Online Data Analysis Platform.            

Award Number: 
1621289
Funding Period: 
Thu, 09/15/2016 to Sat, 08/31/2019
Full Description: 

This project will expand the DRK-12 portfolio by contributing to a limited program portfolio on data science, and also by being responsive to a broader, national discourse on data science, exemplified in the data-dependent scientific practices emphasis in the Next Generation Science Standards (NGSS). With the impetus toward data literacy, an acute need has emerged for high quality evidence-based models, practices, and tools to better prepare high school teachers to teach data skills and for students to develop the problem solving and analytical skills needed to interpret and understand data, particularly in the sciences. This project will address these challenges by leveraging novel research and design approaches, using digital tools and two well-established online instructional platforms; Zoom In and Common Online Data Analysis Platform.

With a user base of over 27,000 teachers and students, the existing Zoom In platform has proven successful in fostering evidence-based inquiry among social studies teachers. This project will test the feasibility of the platform to facilitate data-focused inquiry and skill development among high school science teachers and their students. In Year 1, two NGSS-aligned digital curriculum modules and supporting materials focused on scientific phenomena and problems in biology and earth science will be developed for high school science teachers and embedded in a modified iteration of Zoom In. The Common Online Data Analysis Platform (CODAP) will be integrated into the modules to make hierarchical data structures, modeling, visualizations, and dynamic linking possible within Zoom In. A pilot and usability test will be conducted with 16 teachers and 100 students from diverse New York City public high schools. Two teacher focus groups and think-aloud sessions with the students will be held. In Year 2, the remaining four modules will be developed. Guided by four research questions, field testing with teachers and students will be done to assess the content, CODAP data tools, Zoom-in student supports, teacher supports, and outcome measures. In Year 3, final revisions to the tools will be completed. A small-scale efficacy test will be conducted to assess aspects of the implementation process, practices, and overall impact of the modules on student learning. For the efficacy study, a two-level cluster-randomized design will be employed to randomly assign schools to the Zoom In intervention. A comparison group will use another existing well-designed data literacy digital instructional platform but without key aspects of Zoom In. Outcome measures will be administered at the beginning and end of the school year to the treatment and comparison groups. Back-end data, observational data, and teacher log data will be collected and analyzed. Qualitative data will be gathered from teacher and student observations and interviews and analyzed. Researchers will analyze the impact on student learning using hierarchical linear models with an effect treatment condition and student-and-class-level covariates. The research findings will be broadly disseminated through the Zoom In platform, conferences, publications, and social media.


Project Videos

2019 STEM for All Video Showcase

Title: Zoom In! Learning Science with Data

Presenter(s): Megan Silander & Bill Tally


Building a Next Generation Diagnostic Assessment and Reporting System within a Learning Trajectory-Based Mathematics Learning Map for Grades 6-8

This project will build on prior funding to design a next generation diagnostic assessment using learning progressions and other learning sciences research to support middle grades mathematics teaching and learning. The project will contribute to the nationally supported move to create, use, and apply research based open educational resources at scale.

Award Number: 
1621254
Funding Period: 
Thu, 09/15/2016 to Sat, 08/31/2019
Full Description: 

This project seeks to design a next generation diagnostic assessment using learning progressions and other research (in the learning sciences) to support middle grades mathematics teaching and learning. It will focus on nine large content ideas, and associated Common Core State Standards for Mathematics. The PIs will track students over time, and work within school districts to ensure feasibility and use of the assessment system.

The research will build on prior funding by multiple funding agencies and address four major goals. The partnership seeks to address these goals: 1) revising and strengthening the diagnostic assessments in mathematics by adding new item types and dynamic tools for data gathering 2) studying alternative ways to use measurement models to assess student mathematical progress over time using the concept of learning trajectories, 3) investigating how to assist students and teachers to effectively interpret reports on math progress, both at the individual and the class level, and 4) engineering and studying instructional strategies based on student results and interpretations, as they are implemented within competency-based and personalized learning classrooms. The learning map, assessment system, and analytics are open source and can be used by other research and implementation teams. The project will exhibit broad impact due to the number of states, school districts and varied kinds of schools seeking this kind of resource as a means to improve instruction. Finally, the research project contributes to the nationally supported move to create, use, and apply research based open educational resources at scale.

CAREER: Multilevel Mediation Models to Study the Impact of Teacher Development on Student Achievement in Mathematics

This project will develop a comprehensive framework to inform and guide the analytic design of teacher professional development studies in mathematics. An essential goal of the research is to advance a science of teaching and learning in ways that traverse both research and education.

Lead Organization(s): 
Award Number: 
1552535
Funding Period: 
Thu, 09/01/2016 to Tue, 08/31/2021
Full Description: 

This is a Faculty Early Career Development Program (CAREER) project. The CAREER program is a National Science Foundation-wide activity that offers the most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research. The intellectual merit and broader impacts of this study lie in two complementary contributions of the project. First, the development of the statistical framework for the design of multilevel mediation studies has significant potential for broad impact because it develops a core platform that is transferable to other STEM (science, technology, engineering, and mathematics) education areas and STEM disciplines. Second, the development of software and curricular materials to implement this framework further capitalize on the promise of this work because it distributes the results in an accessible manner to diverse sets of research and practitioner groups across STEM education areas and STEM disciplines. Together, the components of this project will substantially expand the scope and quality of evidence generated through mathematics professional development and, more generally, multilevel mediation studies throughout STEM areas by increasing researchers' capacity to design valid and comprehensive studies of the theories of action and change that underlie research programs.

This project will develop a comprehensive framework to inform and guide the analytic design of teacher professional development studies in mathematics. The proposed framework incorporates four integrated research and education components: (1) develop statistical formulas and tools to guide the optimal design of experimental and non-experimental multilevel mediation studies in the presence of measurement error, (2) develop empirical estimates of the parameters needed to implement these formulas to design teacher development studies in mathematics, (3) develop free and accessible software to execute this framework, and (4) develop training materials and conduct workshops on the framework to improve the capacity of the field to design effective and efficient studies of teacher development. An essential goal of the research is to advance a science of teaching and learning in ways that traverse both research and education.

Mathematical and Computational Methods for Planning a Sustainable Future II

The project will develop modules for grades 9-12 that integrate mathematics, computing and science in sustainability contexts. The project materials also include information about STEM careers in sustainability to increase the relevancy of the content for students and broaden their understanding of STEM workforce opportunities. It uses summer workshops to pilot test materials and online support and field testing in four states. 

Lead Organization(s): 
Award Number: 
1503414
Funding Period: 
Wed, 07/15/2015 to Sun, 06/30/2019
Full Description: 

The project will develop modules for grades 9-12 that integrate mathematics, computing and science in sustainability contexts. The project materials also include information about STEM careers in sustainability to increase the relevancy of the content for students and broaden their understanding of STEM workforce opportunities. It uses summer workshops to pilot test materials and online support and field testing in four states. Outcomes include the modules, tested and revised; strategies for transfer of learning embedded in the modules; and a compendium of green jobs, explicitly related to the modules. The Discovery Research K-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). 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 proposed projects. The STEM+Computing Partnerships (STEM+C) Program is a joint effort between the Directorate for Education & Human Resources (EHR) and Directorate Computer & Information Science & Engineering (CISE). Reflecting the increasing role of computational approaches in learning across the STEM disciplines, STEM+C supports research and development efforts that integrate computing within one or more STEM disciplines and/or integrate STEM learning in computer science; 2) advance multidisciplinary, collaborative approaches for integrating computing in STEM in and out of school, and 3) build capacity in K-12 computing education through foundational research and focused teacher preparation

The project is a full design and development project in the learning strand of DRK-12. The goal is to enhance transfer of knowledge in mathematics and science via sustainability tasks with an emphasis on mathematical and scientific practices. The research questions focus on how conceptual representations and the modules support students' learning and especially transfer to novel problems. The project design integrates the research with the curriculum development. It includes a mixed methods data collection and analysis from teachers and students (e.g., interviews, content exams, focus groups, implementation logs). Assessment of student work includes both short, focused problems in the content area and longer project-based tasks providing a range of assessments of student learning. The investigators will develop a rubric for scoring student work on the tasks. The curriculum design process includes iterations of the modules over time with feedback from teachers and using data collected from the implementation.

TRUmath and Lesson Study: Supporting Fundamental and Sustainable Improvement in High School Mathematics Teaching (Collaborative Research: Schoenfeld)

Given the changes in instructional practices needed to support high quality mathematics teaching and learning based on college and career readiness standards, school districts need to provide professional learning opportunities for teachers that support those changes. The project is based on the TRUmath framework and will build a coherent and scalable plan for providing these opportunities in high school mathematics departments, a traditionally difficult unit of organizational change.

Award Number: 
1503454
Funding Period: 
Wed, 07/01/2015 to Sun, 06/30/2019
Full Description: 

Given the changes in instructional practices needed to support high quality mathematics teaching and learning based on college and career readiness standards, school districts need to provide professional learning opportunities for teachers that support those changes. The project will build a coherent and scalable plan for providing these opportunities in high school mathematics departments, a traditionally difficult unit of organizational change. Based on the TRUmath framework, characterizing the five essential dimensions of powerful mathematics classrooms, the project brings together a focus on curricular materials that support teaching, Lesson Study protocols and materials, and a professional learning community-based professional development model. The project will design and revise professional development and coaching guides and lesson study mathematical resources built around the curricular materials. The project will study changes in instructional practice and impact on student learning. By documenting the supports used in the Oakland Unified School District where the research and development will be conducted, the resources can be used by other districts and in similar work by other research-practice partnerships.

This project hypothesizes that the quality of classroom instruction can be defined by five dimensions - quality of the mathematics; cognitive demand of the tasks; access to mathematics content in the classroom; student agency, authority, and identity; and uses of assessment. The project will use an iterative design process to develop and refine a suite of tool, including a conversation guide to support productive dialogue between teachers and coaches, support materials for building site-based professional learning materials, and formative assessment lessons using Lesson Study as a mechanism to enact reforms of these dimensions. The study will use a pre-post design and natural variation to student the relationships between these dimensions, changes in teachers' instructional practice, and student learning using hierarchical linear modeling with random intercept models with covariates. Qualitative of the changes in teachers' instructional practices will be based on coding of observations based on the TRUmath framework. The study will also use qualitative analysis techniques to identify themes from surveys and interviews on factors that promote or hinder the effectiveness of the intervention.

PBS NewsHour STEM Student Reporting Labs: Broad Expansion of Youth Journalism to Support Increased STEM Literacy Among Underserved Student Populations and Their Communities

The production of news stories and student-oriented instruction in the classroom are designed to increase student learning of STEM content through student-centered inquiry and reflections on metacognition. This project scales up the PBS NewsHour Student Reporting Labs (SRL), a model that trains teens to produce video reports on important STEM issues from a youth perspective.

Award Number: 
1503315
Funding Period: 
Sat, 08/01/2015 to Wed, 07/31/2019
Full Description: 

The Discovery Research K-12 program (DR-K12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools (RMTs). 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 proposed projects. This project scales up the PBS NewsHour Student Reporting Labs (SRL), a model that trains teens to produce video reports on important STEM issues from a youth perspective. Participating schools receive a SRL journalism and digital media literacy curriculum, a mentor for students from a local PBS affiliate, professional development for educators, and support from the PBS NewsHour team. The production of news stories and student-oriented instruction in the classroom are designed to increase student learning of STEM content through student-centered inquiry and reflections on metacognition. Students will develop a deep understanding of the material to choose the best strategy to teach or tell the STEM story to others through digital media. Over the 4 years of the project, the model will be expanded from the current 70 schools to 150 in 40 states targeting schools with high populations of underrepresented youth. New components will be added to the model including STEM professional mentors and a social media and media analytics component. Project partners include local PBS stations, Project Lead the Way, and Share My Lesson educators.

The research study conducted by New Knowledge, LLC will add new knowledge about the growing field of youth science journalism and digital media. Front-end evaluation will assess students' understanding of contemporary STEM issues by deploying a web-based survey to crowd-source youth reactions, interest, questions, and thoughts about current science issues. A subset of questions will explore students' tendencies to pass newly-acquired information to members of the larger social networks. Formative evaluation will include qualitative and quantitative studies of multiple stakeholders at the Student Reporting Labs to refine the implementation of the program. Summative evaluation will track learning outcomes/changes such as: How does student reporting on STEM news increase their STEM literacy competencies? How does it affect their interest in STEM careers? Which strategies are most effective with underrepresented students? How do youth communicate with each other about science content, informing news media best practices? The research team will use data from pre/post and post-delayed surveys taken by 1700 students in the STEM Student Reporting Labs and 1700 from control groups. In addition, interviews with teachers will assess the curriculum and impressions of student engagement.


Project Videos

2019 STEM for All Video Showcase

Title: How Video Storytelling Reengages Teenagers in STEM Learning

Presenter(s): Leah Clapman & William Swift

2018 STEM for All Video Showcase

Title: PBS NewsHour's STEM SRL Transforms Classrooms into Newsrooms

Presenter(s): Leah Clapman & William Swift

2017 STEM for All Video Showcase

Title: PBS is Building the Next Generation of STEM Communicators

Presenter(s): Leah Clapman, John Fraser, Su-Jen Roberts, & Bill Swift


Scientific Data in Schools: Measuring the Efficacy of an Innovative Approach to Integrating Quantitative Reasoning in Secondary Science (Collaborative Research: Stuhlsatz)

Lead Organization(s): 
Award Number: 
1503005
Funding Period: 
Wed, 07/15/2015 to Fri, 05/31/2019
Project Evaluator: 
Kristin Bass
Full Description: 

The goal of this project is to investigate whether the integration of real data from cutting-edge scientific research in grade 6-10 classrooms will increase students’ quantitative reasoning ability in the context of science. Data Nuggets are activity-based resources that address current needs in STEM education and were developed by science graduate students and science teachers at Michigan State University through prior support from the NSF GK-12 program and the BEACON Center for the Study of Evolution in Action. The goal of Data Nuggets is to engage students in the practices of science through an innovative approach that combines scientific content from authentic research with key concepts in quantitative reasoning. Partners from Michigan State University and BSCS will adapt the materials to address current science and mathematics standards, create a professional development program for teachers, and test the efficacy of the materials through a cluster-randomized trial in the classrooms of 30 teachers in Michigan, Colorado, and California.

The project will study whether short, targeted interventions of classroom activities embedded within a typical curriculum can impact student outcomes. Prior to the study teachers will participate in professional development. Classrooms of the teachers in the study will be randomly assigned to either a treatment or comparison condition. Student outcome measures will include understanding of quantitative reasoning in the context of science, understanding of the practices and processes of science, student engagement and motivation, and interest in science.

In order to adequately train the next generation of citizens and scientists, research is needed on how quantitative reasoning skills build upon each other throughout K-16 science education Students need to experience activities that emphasize how science is conducted, and apply their understandings of how scientists reason quantitatively. Establishing the efficacy of Data Nuggets could provide the field with information about supplementing existing curriculum with short interventions targeted at particular scientific practices. By facilitating student access to authentic science, Data Nuggets bridge the gap between scientists and the public. Scientists who create Data Nuggets practice their communication skills and share both the process of science and research findings with K-12 students (and perhaps their families), undergraduates, and teachers, improving the understanding of science in society.

GRIDS: Graphing Research on Inquiry with Data in Science

The Graphing Research on Inquiry with Data in Science (GRIDS) project will investigate strategies to improve middle school students' science learning by focusing on student ability to interpret and use graphs. GRIDS will undertake a comprehensive program to address the need for improved graph comprehension. The project will create, study, and disseminate technology-based assessments, technologies that aid graph interpretation, instructional designs, professional development, and learning materials.

Award Number: 
1418423
Funding Period: 
Mon, 09/01/2014 to Sat, 08/31/2019
Full Description: 

The Graphing Research on Inquiry with Data in Science (GRIDS) project is a four-year full design and development proposal, addressing the learning strand, submitted to the DR K-12 program at the NSF. GRIDS will investigate strategies to improve middle school students' science learning by focusing on student ability to interpret and use graphs. In middle school math, students typically graph only linear functions and rarely encounter features used in science, such as units, scientific notation, non-integer values, noise, cycles, and exponentials. Science teachers rarely teach about the graph features needed in science, so students are left to learn science without recourse to what is inarguably a key tool in learning and doing science. GRIDS will undertake a comprehensive program to address the need for improved graph comprehension. The project will create, study, and disseminate technology-based assessments, technologies that aid graph interpretation, instructional designs, professional development, and learning materials.

GRIDS will start by developing the GRIDS Graphing Inventory (GGI), an online, research-based measure of graphing skills that are relevant to middle school science. The project will address gaps revealed by the GGI by designing instructional activities that feature powerful digital technologies including automated guidance based on analysis of student generated graphs and student writing about graphs. These materials will be tested in classroom comparison studies using the GGI to assess both annual and longitudinal progress. Approximately 30 teachers selected from 10 public middle schools will participate in the project, along with approximately 4,000 students in their classrooms. A series of design studies will be conducted to create and test ten units of study and associated assessments, and a minimum of 30 comparison studies will be conducted to optimize instructional strategies. The comparison studies will include a minimum of 5 experiments per term, each with 6 teachers and their 600-800 students. The project will develop supports for teachers to guide students to use graphs and science knowledge to deepen understanding, and to develop agency and identity as science learners.

CodeR4STATS - Code R for AP Statistics

This project builds on prior efforts to create teaching resources for high-school Advanced Placement Statistics teachers to use an open source statistics programming language called "R" in their classrooms. The project brings together datasets from a variety of STEM domains, and will develop exercises and assessments to teach students how to program in R and learn the underlying statistics concepts.

Lead Organization(s): 
Award Number: 
1418163
Funding Period: 
Mon, 09/01/2014 to Sat, 08/31/2019
Full Description: 

Increasingly, all STEM fields rely on being able to understand data and use statistics. This project builds on prior efforts to create teaching resources for high-school Advanced Placement Statistics teachers to use an open source statistics programming language called "R" in their classrooms. The project brings together datasets from a variety of STEM domains, and will develop exercises and assessments to teach students how to program in R and learn the underlying statistics concepts. Thus, this project attempts to help students learn coding, statistics, and STEM simultaneously in the context of AP Stats. In addition, researchers will examine the extent to which students learn statistical concepts, computational fluency, and critical reasoning skills better with the online tools.

The resources developed by the project aim to enhance statistics learning through an integrated application of strategies previously documented to be effective: a focus on data visualization and representation, engaging students in meaningful investigations with complex real-world data sets, utilizing computational tools and techniques to analyze data, and better preparing educators for the needs of a more complex and technologically-rich mathematical landscape. This project will unite these lines of work into one streamlined pedagogical environment called CodeR4STATS with three kinds of resources: computing resources, datasets, and assessment resources. Computing resources will include freely available access to an instance of the cloud-based R-studio with custom help pages. Data resources will include over 800 scientific datasets from Woods Hole Oceanographic Institute, Harvard University's Institute for Quantitative Social Science, Hubbard Brook Experimental Forest, Boston University, and Tufts University with several highlighted in case studies for students; these will be searchable within the online environment. Assessment and tutoring resources will be provided using the tutoring platform ASSISTments which uses example tracing to provide assessment, feedback, and tailored instruction. Teacher training and a teacher online discussion board will also be provided. Bringing these resources together will be programming lab activities, five real-world case studies, and sixteen statistics assignments linked to common core math standards. Researchers will use classroom observational case studies from three classrooms over two years, including cross-case comparison of lessons in the computational environment versus offline lessons; student and teacher interviews; and an analysis of learner data from the online system, especially the ASSISTments-based assessment data. This research will examine learning outcomes and help refine design principles for statistics learning environments.

Common Online Data Analysis Platform (CODAP)

This project aims to engage students in meaningful scientific data collection, analysis, visualization, modeling, and interpretation. It targets grades 9-12 science instruction. The proposed research poses the question "How do learners conceive of and interact with empirical data, particularly when it has a hierarchical structure in which parameters and results are at one level and raw data at another?"

Lead Organization(s): 
Award Number: 
1435470
Funding Period: 
Tue, 10/01/2013 to Fri, 09/30/2016
Full Description: 

This project aims to engage students in meaningful scientific data collection, analysis, visualization, modeling, and interpretation. It targets grades 9-12 science instruction. The proposed research poses the question "How do learners conceive of and interact with empirical data, particularly when it has a hierarchical structure in which parameters and results are at one level and raw data at another?" As working with data becomes an integral part of students' learning across STEM curricula, understanding how students conceive of data grows ever more important. This is particularly timely as science becomes more and more data driven.

The team will develop and test a Common Online Data Analysis Platform (CODAP). STEM curriculum development has moved online, but development of tools for students to engage in data analysis has yet to follow suit. As a result, online curriculum development projects are often forced to develop their own data analysis tools, settle for desktop tools, or do without. In a collaboration with NSF-funded projects at the Concord Consortium, Educational Development Center, and University of Minnesota, the project team is developing an online, open source data analysis platform that can be used not only by these three projects, but subsequently by others.

The proposed research breaks new ground both in questions to be investigated and in methodology. The investigations build on prior research on students' understanding of data representation, measures of center and spread, and data modeling to look more closely at students' understanding of data structures especially as they appear in real scientific situations. Collaborative design based on three disparate STEM projects will yield a flexible data analysis environment that can be adopted by additional projects in subsequent years. Such a design process increases the likelihood that CODAP will be more than a stand-alone tool, and can be meaningfully integrated into online curricula. CODAP's overarching goal is to improve the preparation of students to fully participate in an increasingly data-driven society. It proposes to do so by improving a critical piece of infrastructure: namely, access to classroom-friendly data analysis tools by curriculum developers who wish to integrate student engagement with data into content learning.

This project is asociated with award number 1316728 with the same title.

Pages

Subscribe to Statistics