Statistics

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.

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.

Integrating Chemistry and Earth Science

This project will design, develop, and test a new curriculum unit for high school chemistry courses that is organized around the question, "How does chemistry shape where I live?" The new unit will integrate relevant Earth science data, scientific practices, and key urban environmental research findings with the chemistry curriculum to gain insights into factors that support the approach to teaching and learning advocated by current science curriculum standards.

Award Number: 
1721163
Funding Period: 
Tue, 08/15/2017 to Wed, 07/31/2019
Full Description: 

This Integrating Chemistry and Earth science (ICE) project will design, develop, and test a new curriculum unit for high school chemistry courses that is organized around the question, "How does chemistry shape where I live?" The new unit will integrate relevant Earth science data, scientific practices, and key urban environmental research findings with the chemistry curriculum to gain insights into factors that support the approach to teaching and learning advocated by current science curriculum standards. The overarching goal of the project is to develop teacher capacity to teach and evaluate student abilities to use the practices of scientists and concepts from Earth science and chemistry to understand important phenomena in their immediate, familiar environments. The project has the potential to serve as a model for how to make cutting edge science directly accessible to all students. The project is a collaborative effort that engages scientists, science education researchers, curriculum developers, school curriculum and instruction leaders, and science teachers in the longer term challenge of infusing Earth science concepts and practices across the core high school science courses.

Current guidelines and standards for science education promote learning that engages students in three interrelated dimensions: disciplinary core ideas, scientific practices, and crosscutting ideas. This project is guided by the hypothesis that when provided sustained opportunities to engage in three-dimensional learning experiences, in an integrated Earth science and chemistry context, students will improve in their ability to demonstrate the coordination of disciplinary core ideas, scientific practices, and crosscutting concepts when solving problems and developing explanations related to scientific phenomena. This project will employ a design based research approach, and during the two development-enactment-analysis-and-redesign cycles, the project team will collect student assessment data, teacher interview data, observational data from lessons, teacher surveys, and reflective teacher logbooks. These collected data will provide information about how teachers implement the lessons, what students do during the lessons, and what students learn from them that will lead to better design and a better understanding of student learning. This information will be used to inform the modification of lessons from cycle to cycle, and to inform the professional development materials for teachers. The research agenda for the project is guided by the following questions: 1. What are the design features of ICE lessons that support teachers in enacting three-dimensional instruction within the context of their classroom? 2. What are the design features of embedded three-dimensional assessments that yield useful classroom data for teachers and researchers regarding their students' abilities to integrate core ideas, scientific practices, and crosscutting concepts? 3. What is the nature of student learning related to disciplinary core ideas, scientific practices, and crosscutting concepts that results from students' engagement in ICE lesson sets? 4. What differences emerge in student engagement and learning outcomes for ICE lessons that incorporate local phenomena or data sets as compared to lessons that do not? 5. What contextual factors (i.e., school context, administrative support, time constraints, etc.) influence teachers' implementation of three-dimensional instruction embedded within ICE lessons?


Project Videos

2019 STEM for All Video Showcase

Title: Integrating Chemistry and Earth Science (ICE)

Presenter(s): Alan Berkowitz, Vonceil Anderson, Bess Caplan, Kevin Garner, & Jonathon Grooms


CAREER: Designing and Enacting Mathematically Captivating Learning Experiences for High School Mathematics

This project explores how secondary mathematics teachers can plan and enact learning experiences that spur student curiosity, captivate students with complex mathematical content, and compel students to engage and persevere (referred to as "mathematically captivating learning experiences" or "MCLEs"). The study will examine how high school teachers can design lessons so that mathematical content itself is the source of student intrigue, pursuit, and passion.

Lead Organization(s): 
Award Number: 
1652513
Funding Period: 
Wed, 02/15/2017 to Mon, 01/31/2022
Full Description: 

This design and development project explores how secondary mathematics teachers can plan and enact learning experiences that spur student curiosity, captivate students with complex mathematical content, and compel students to engage and persevere (referred to as "mathematically captivating learning experiences" or "MCLEs"). This study is important because of persistent disinterest by secondary students in mathematics in the United States. This study will examine how high school teachers can design lessons so that mathematical content itself is the source of student intrigue, pursuit, and passion. To do this, the content within mathematical lessons (both planned and enacted) is framed as mathematical stories and the felt tension between how information is revealed and withheld from students as the mathematical story unfolds is framed as its mathematical plot. The Mathematical Story Framework (Dietiker, 2013, 2015) foregrounds both the coherence (does the story make sense?) and aesthetic (does it stimulate anticipation for what is to come, and if so, how?) dimensions of mathematics lessons. The project will generate principles for lesson design usable by teachers in other settings and exemplar lessons that can be shared.

Specifically, this project draws from prior curriculum research and design to (a) develop a theory of teacher MCLE design and enactment with the Mathematical Story Framework, (b) increase the understanding(s) of the aesthetic nature of mathematics curriculum by both researchers and teachers, and (c) generate detailed MCLE exemplars that demonstrate curricular coherence, cognitive demand, and aesthetic dimensions of mathematical lessons. The project is grounded in a design-based research framework for education research. A team of experienced high school teachers will design and test MCLEs (four per teacher) with researchers through three year-long cycles. Prior to the first cycle, data will be collected (interview, observations) to record initial teacher curricular strategies regarding student dispositions toward mathematics. Then, a professional development experience will introduce the Mathematical Story Framework, along with other curricular frameworks to support the planning and enacting of lessons (i.e., cognitive demand and coherence). During the design cycles, videotaped observations and student aesthetic measures (surveys and interviews) for both MCLEs and a non-MCLEs (randomly selected to be the lesson before or after the MCLE) will be collected to enable comparison. Also, student dispositional measures, collected at the beginning and end of each cycle, will be used to learn whether and how student attitudes in mathematics change over time. Of the MCLEs designed and tested, a sample will be selected (based on aesthetic and mathematical differences) and developed into models, complete with the rationale for and description of aesthetic dimensions.

INFEWS/T4: The INFEWS-ER: a Virtual Resource Center Enabling Graduate Innovations at the Nexus of Food, Energy, and Water Systems

This project will provide a virtual environment for completing the Food, Energy, and Water (FEW) graduate student experience. The proposed work facilitates a transition from interdisciplinary to transdisciplinary training of existing faculty and current graduate students through a virtual resource center to help develop systematic processes for interdisciplinary thinking about large societal problems, especially those at the nexus of food, energy, and water.

Award Number: 
1639340
Funding Period: 
Thu, 09/01/2016 to Mon, 08/31/2020
Full Description: 

This project will provide a virtual environment for completing the Food, Energy, and Water (FEW) graduate student experience, thereby facilitating the generation of human capital who can address grand challenges at the nexus of food, energy, and water. The INFEWS-ER will provide educational resources (ER) targeting innovations at the nexus of FEW by combining the fundamental sciences of food, energy, and water with the skills and knowledge of interdisciplinary problem solving and the latest computational modeling and analysis tools and data. These individuals will be capable of analyzing scenarios at the scale of nations, continents, and the globe. The INFEWS-ER will offer certificate programs where FEW Graduate Scholars can demonstrate their capabilities in interdisciplinary thinking, Big Data, and computational modeling and analysis, thereby receiving a credential demonstrating their level of achievement. Further, The INFEWS-ER will offer a faculty fellowship program to incentivize a network of academics that will provide a scaffolded learning environment for graduates, effectively creating a hub for INFEWS research, education, and training.

The proposed work facilitates a transition from interdisciplinary to transdisciplinary training of existing faculty and current graduate students (who will become future faculty, practitioners, and policy makers) through a virtual resource center that will be accessible beyond the project team and project timeframe. Students will develop systematic processes for interdisciplinary thinking. They will be in the best possible position to target large societal problems, especially those at the nexus of food, energy, and water. New, interdisciplinary solutions will emerge, solutions that are sensitive to a wider array of constraints and ideals. Those solutions will reflect the best possible integration of technological, socio-economic, and socio-political constructs. Project impacts include educational and workforce development of the next generation of academics, multi-institution collaboration, and enhanced infrastructure for transdisciplinary research and education. The INFEWS-ER also has the potential to influence the way interdisciplinary research and education is implemented in the future through the archival dissemination of not only learning modules, but also the evaluations and lessons learned from the implementation of the center.

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.

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