Technology

SmartCAD: Guiding Engineering Design with Science Simulations (Collaborative Research: Xie)

This project investigates how real time formative feedback can be automatically composed from the results of computational analysis of student design artifacts and processes with the envisioned SmartCAD software. The project conducts design-based research on SmartCAD, which supports secondary science and engineering with three embedded computational engines capable of simulating the mechanical, thermal, and solar performance of the built environment.

Lead Organization(s): 
Award Number: 
1503196
Funding Period: 
Mon, 06/15/2015 to Fri, 05/31/2019
Full Description: 

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. 

In this project, SmartCAD: Guiding Engineering Design with Science Simulations, the Concord Consortium (lead), Purdue University, and the University of Virginia investigate how real time formative feedback can be automatically composed from the results of computational analysis of student design artifacts and processes with the envisioned SmartCAD software. Through automatic feedback based on visual analytic science simulations, SmartCAD is able to guide every student at a fine-grained level, allowing teachers to focus on high-level instruction. Considering the ubiquity of CAD software in the workplace and their diffusion into precollege classrooms, this research provides timely results that could motivate the development of an entire genre of CAD-based learning environments and materials to accelerate and scale up K-12 engineering education. The project conducts design-based research on SmartCAD, which supports secondary science and engineering with three embedded computational engines capable of simulating the mechanical, thermal, and solar performance of the built environment. These engines allow SmartCAD to analyze student design artifacts on a scientific basis and provide automatic formative feedback in forms such as numbers, graphs, and visualizations to guide student design processes on an ongoing basis. 

The research hypothesis is that appropriate applications of SmartCAD in the classroom results in three learning outcomes: 1) Science knowledge gains as indicated by a deeper understanding of the involved science concepts and their integration at the completion of a design project; 2) Design competency gains as indicated by the increase of iterations, informed design decisions, and systems thinking over time; and 3) Design performance improvements as indicated by a greater chance to succeed in designing a product that meets all the specifications within a given period of time. While measuring these learning outcomes, this project also probes two research questions: 1) What types of feedback from simulations to students are effective in helping them attain the outcomes? and 2) Under what conditions do these types of feedback help students attain the outcomes? To test the research hypothesis and answer the research questions, this project develops three curriculum modules based on the Learning by Design (LBD) Framework to support three selected design challenges: Solar Farms, Green Homes, and Quake-Proof Bridges. This integration of SmartCAD and LBD situate the research in the LBD context and shed light on how SmartCAD can be used to enhance established pedagogical models such as LBD. Research instruments include knowledge integration assessments, data mining, embedded assessments, classroom observations, participant interviews, and student questionnaires. This research is carried out in Indiana, Massachusetts, and Virginia simultaneously, involving more than 2,000 secondary students at a number of socioeconomically diverse schools. Professional development workshops are provided to familiarize teachers with SmartCAD materials and implementation strategies prior to the field tests. An external Critical Review Committee consisting of five engineering education researchers and practitioners oversee and evaluate this project formatively and summative. Project materials and results are disseminated through publications, presentations, partnerships, and the Internet.

Fostering STEM Trajectories: Bridging ECE Research, Practice, and Policy

This project will convene stakeholders in STEM and early childhood education to discuss better integration of STEM in the early grades. PIs will begin with a phase of background research to surface critical issues in teaching and learning in early childhood education and STEM.  A number of reports will be produced including commissioned papers, vision papers, and a forum synthesis report.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1417878
Funding Period: 
Mon, 06/15/2015 to Tue, 05/31/2016
Full Description: 

Early childhood education is at the forefront of the minds of parents, teachers, policymakers as well as the general public. A strong early childhood foundation is critical for lifelong learning. The National Science Foundation has made a number of early childhood grants in science, technology, engineering and mathematics (STEM) over the years and the knowledge generated from this work has benefitted researchers. Early childhood teachers and administrators, however, have little awareness of this knowledge since there is little research that is translated and disseminated into practice, according to the National Research Council. In addition, policies for both STEM and early childhood education has shifted in the last decade. 

The Joan Ganz Cooney Center and the New America Foundation are working together to highlight early childhood STEM education initiatives. Specifically, the PIs will convene stakeholders in STEM and early childhood education to discuss better integration of STEM in the early grades. PIs will begin with a phase of background research to surface critical issues in teaching and learning in early childhood education and STEM. The papers will be used as anchor topics to organize a forum with a broad range of stakeholders including policymakers as well as early childhood researchers and practitioners. A number of reports will be produced including commissioned papers, vision papers, and a forum synthesis report. The synthesis report will be widely disseminated by the Joan Ganz Cooney Center and the New America Foundation.

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 project.

Teaching Environmental Sustainability - Model My Watershed (Collaborative Research: Staudt)

This project will develop curricula for environmental/geoscience disciplines for high-school classrooms. The Model My Watershed (MMW) v2 app will bring new environmental datasets and geospatial capabilities into the classroom, to provide a cloud-based learning and analysis platform accessible from a web browser on any computer or mobile device, thus overcoming the cost and technical obstacles to integrating Geographic Information System technology in secondary education.

Lead Organization(s): 
Award Number: 
1417722
Funding Period: 
Mon, 09/01/2014 to Fri, 08/31/2018
Full Description: 

This project will develop curricula for environmental/geoscience disciplines for high-school classrooms. It will teach a systems approach to problem solving through hands-on activities based on local data and issues. This will provide an opportunity for students to act in their communities while engaging in solving problems they find interesting, and require synthesis of prior learning. The Model My Watershed (MMW) v2 app will bring new environmental datasets and geospatial capabilities into the classroom, to provide a cloud-based learning and analysis platform accessible from a web browser on any computer or mobile device, thus overcoming the cost and technical obstacles to integrating Geographic Information System technology in secondary education. It will also integrate new low-cost environmental sensors that allow students to collect and upload their own data and compare them to data visualized on the new MMW v2. This project will transform the ability of teachers throughout the nation to introduce hands-on geospatial analysis activities in the classroom, to explore a wide range of geographic, social, political and environmental concepts and problems beyond the project's specific curricular focus.

The Next Generation Science Standards state that authentic research experiences are necessary to enhance STEM learning. A combination of computational modeling and data collection and analysis will be integrated into this project to address this need. Placing STEM content within a place- and problem-based framework enhances STEM learning. Students, working in groups, will not only design solutions, they will be required to defend them within the application portal through the creation of multimedia products such as videos, articles and web 2.0 presentations. The research plan tests the overall hypothesis that students are much more likely to develop an interest in careers that require systems thinking and/or spatial thinking, such as environmental sciences, if they are provided with problem-based, place-based, hands-on learning experiences using real data, authentic geospatial analysis tools and models, and opportunities to collect their own supporting data. The MMW v2 web app will include a data visualization tool that streams data related to the modeling application. This database will be modified to integrate student data so teachers and students can easily compare their data to data collected by other students and the government and research data. All data will be easily downloadable so that students can increase the use of real data to support the educational exercises. As a complement to the model-based activities, the project partners will design, manufacture, and distribute a low-cost environmental monitoring device, called the Watershed Tracker. This device will allow students to collect real-world data to enhance their understanding of watershed dynamics. Featuring temperature, light, humidity, and soil moisture sensors, the Watershed Tracker will be designed to connect to tablets and smartphones through the audio jack common to all of these devices.

Learning about Ecosystems Science and Complex Causality through Experimentation in a Virtual World

This project will develop a modified virtual world and accompanying curriculum for middle school students to help them learn to more deeply understand ecosystems patterns and the strengths and limitations of experimentation in ecosystems science. The project will build upon a computer world called EcoMUVE, a Multi-User Virtual Environment or MUVE, and will develop ways for students to conduct experiments within the virtual world and to see the results of those experiments.

Project Email: 
Lead Organization(s): 
Award Number: 
1416781
Funding Period: 
Mon, 09/01/2014 to Thu, 08/31/2017
Full Description: 

EcoXPT from videohall.com on Vimeo.

Comprehending how ecosystems function is important knowledge for citizens in making decisions and for students who aspire to become scientists. This understanding requires deep thinking about complex causality, unintended side-effects, and the strengths and limitations of experimental science. These are difficult concepts to learn due to the many interacting components and non-linear interrelationships involved. Ecosystems dynamics is particularly difficult to teach in classrooms because ecosystems involve complexities such as phenomena distributed widely across space that change over long time frames. Learning when and how experimental science can provide useful information in understanding ecosystems dynamics requires moving beyond the limited affordances of classrooms. The project will: 1) advance understanding of experimentation in ecosystems as it can be applied to education; 2) show how student learning is affected by having opportunities to experiment in the virtual world that simulate what scientists do in the real world and with models; and 3) produce results comparing this form of teaching to earlier instructional approaches. This project will result in a learning environment that will support learning about the complexities of the earth's ecosystem.

The project will build upon a computer world called EcoMUVE, a Multi-User Virtual Environment or MUVE, developed as part of an earlier NSF-funded project. A MUVE is a simulated world in which students can virtually walk around, make observations, talk to others, and collect data. EcoMUVE simulates a pond and a forest ecosystem. It offers an immersive context that makes it possible to teach about ecosystems in the classroom, allowing exploration of the complexities of large scale problems, extended time frames and and multiple causality. To more fully understand how ecosystems work, students need the opportunity to experiment and to observe what happens. This project will advance this earlier work by developing ways for students to conduct experiments within the virtual world and to see the results of those experiments. The project will work with ecosystem scientists to study the types of experiments that they conduct, informing knowledge in education about how ecosystem scientists think, and will build opportunities for students that mirror what scientists do. The project will develop a modified virtual world and accompanying curriculum for middle school students to help them learn to more deeply understand ecosystems patterns and the strengths and limitations of experimentation in ecosystems science. The resulting program will be tested against existing practice, the EcoMUVE program alone, and other programs that teach aspects of ecosystems dynamics to help teachers know how to best use these curricula in the classroom.

Supports for Science and Mathematics Learning in Pre-Kindergarten Dual Language Learners: Designing and Expanding a Professional Development System

SciMath-DLL is an innovative preschool professional development (PD) model that integrates supports for dual language learners (DLLs) with high quality science and mathematics instructional offerings. It engages teachers with workshops, classroom-based coaching, and professional learning communities. Based on initial evidence of promise, the SciMath-DLL project will expand PD offerings to include web-based materials.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1417040
Funding Period: 
Tue, 07/01/2014 to Sat, 06/30/2018
Full Description: 

The 4-year project, Supports for Science and Mathematics Learning in Pre-Kindergarten Dual Language Learners: Designing and Expanding a Professional Development System (SciMath-DLL), will address a number of educational challenges. Global society requires citizens and a workforce that are literate in science, technology, engineering, and mathematics (STEM), but many U.S. students remain ill prepared in these areas. At the same time, the children who fill U.S. classrooms increasingly speak a non-English home language, with the highest concentration in the early grades. Many young children are also at risk for lack of school readiness in language, literacy, mathematics, and science due to family background factors. Educational efforts to offset early risk factors can be successful, with clear links between high quality early learning experiences and later academic outcomes. SciMath-DLL will help teachers provide effective mathematics and science learning experiences for their students. Early educational support is critical to assure that all students, regardless of socioeconomic or linguistic background, learn the STEM content required to become science and mathematics literate. Converging lines of research suggest that participation in sustained mathematics and science learning activities could enhance the school readiness of preschool dual language learners. Positive effects of combining science inquiry with supports for English-language learning have been identified for older students. For preschoolers, sustained science and math learning opportunities enhance language and pre-literacy skills for children learning one language. Mathematics skills and science knowledge also predict later mathematics, science, and reading achievement. What has not been studied is the extent to which rich science and mathematics experiences in preschool lead to better mathematics and science readiness and improved language skills for preschool DLLs. Because the preschool teaching force is not prepared to support STEM learning or to provide effective supports for DLLs, professional development to improve knowledge and practice in these areas is required before children's learning outcomes can be improved.

SciMath-DLL is an innovative preschool professional development (PD) model that integrates supports for DLLs with high quality science and mathematics instructional offerings. It engages teachers with workshops, classroom-based coaching, and professional learning communities. Development and research activities incorporate cycles of design-expert review-enactment- analysis-redesign; collaboration between researcher-educator teams at all project stages; use of multiple kinds of data and data sources to establish claims; and more traditional, experimental methodologies. Based on initial evidence of promise, the SciMath-DLL project will expand PD offerings to include web-based materials, making the PD more flexible for use in a range of educational settings and training circumstances. An efficacy study will be completed to examine the potential of the SciMath-DLL resources, model, and tools to generate positive effects on teacher attitudes, knowledge, and practice for early mathematics and science and on children's readiness in these domains in settings that serve children learning two languages. By creating a suite of tools that can be used under differing educational circumstances to improve professional knowledge, skill, and practice around STEM, the project increases the number of teachers who are prepared to support children as STEM learners and, thus, the number of children who can be supported as STEM learners.

Supporting Secondary Students in Building External Models (Collaborative Research: Damelin)

This project will (1) develop and test a modeling tool and accompanying instructional materials, (2) explore how to support students in building and using models to explain and predict phenomena across a range of disciplines, and (3) document the sophistication of understanding of disciplinary core ideas that students develop when building and using models in grades 6-12. 

Lead Organization(s): 
Award Number: 
1417809
Funding Period: 
Fri, 08/01/2014 to Tue, 07/31/2018
Full Description: 

The Concord Consortium and Michigan State University will collaborate to: (1) develop and test a modeling tool and accompanying instructional materials, (2) explore how to support students in building and using models to explain and predict phenomena across a range of disciplines, and (3) document the sophistication of understanding of disciplinary core ideas that students develop when building and using models in grades 6-12. By iteratively designing, developing and testing a modeling tool and instructional materials that facilitate the building of dynamic models, the project will result in exemplary middle and high school materials that use a model-based approach as well as an understanding of the potential of this approach in supporting student development of explanatory frameworks and modeling capabilities. A key goal of the project is to increase students' learning of science through modeling and to study student engagement with modeling as a scientific practice. 

The project provides the nation with middle and high school resources that support students in developing and using models to explain and predict phenomena, a central scientific and engineering practice. Because the research and development work will be carried out in schools in which students typically do not succeed in science, the products will also help produce a population of citizens capable of continuing further STEM learning and who can participate knowledgeably in public decision making. The goals of the project are to (1) develop and test a modeling tool and accompanying instructional materials, (2) explore how to support students in building, using, and revising models to explain and predict phenomena across a range of disciplines, and (3) document the sophistication of understanding of disciplinary core ideas that students develop when building and using models in grades 6-12. Using a design-based research methodology, the research and development efforts will involve multiple cycles of designing, developing, testing, and refining the systems modeling tool and the instructional materials to help students meet important learning goals related to constructing dynamic models that align with the Next Generation Science Standards. The learning research will study the effect of working with external models on student construction of robust explanatory conceptual understanding. Additionally, it will develop a set of professional development resources and teacher scaffolds to help the expanding community of teachers not directly involved in the project take advantage of the materials and strategies for maximizing the impact of the curricular materials.

Researching the Impact of an Online MOOC Designed to Transform Student Engagement and Achievement in Mathematics

This study examines non-cognitive factors, mindsets, cognitive factors, and strategies for learning mathematics, in the context of a MOOC combined with classroom instruction for middle grades students in mathematics. No previous mindset study has researched the impact of mindset messages within mathematics, and the proposed study will add important knowledge to this field.

Lead Organization(s): 
Award Number: 
1443790
Funding Period: 
Tue, 07/01/2014 to Tue, 06/30/2015
Full Description: 

This project is designed to study a Massively Open Online Course (MOOC), expected to have approximately 2 million students, which will supplement middle grades mathematics classes to understand the impact on students' mathematical learning and engagement in mathematics. The MOOC learning environment, used with school aged children in concert with their regular mathematics course, focuses on helping students to develop positive and productive beliefs, or growth mindsets, about their own potential in mathematics and to teach the students a range of strategies that lead to mathematics success. A better understanding of growth mindsets and learning how to learn mathematics in the context of regular classroom instruction potentially makes important contributions in introducing a new intervention to tens of thousands of students. This contribution is made in concert with providing evidence of impact of using MOOCs coupled with classroom instruction with school aged children on student learning. If the study finds that the mathematics intervention MOOC significantly increases students' achievement and engagement with mathematics, it can be scaled nationally and potentially change the face of mathematics education in the United States.

This study examines non-cognitive factors, mindsets, cognitive factors, and strategies for learning mathematics, in the context of a MOOC combined with classroom instruction for middle grades students in mathematics. No previous mindset study has researched the impact of mindset messages within mathematics, and the proposed study will add important knowledge to this field. The study will also contribute to new knowledge of MOOCs, of their potential as learning opportunities and of the design of innovative pedagogies. Using a blocked randomized control trial of 10,000 students in two California districts, the statistical design employed will enable schools to implement the program across entire classes of students. The study employs measures of pre/post changes in mathematics engagement, mindset, use of mathematics strategies, and mathematics achievement, with close examination of the implications for girls, students of color, students of different socio-economic-status and low achieving students.

Learning Linkages: Integrating Data Streams of Multiple Modalities and Timescales (Collaborative Research: Sherin)

In this project, researchers will collaborate to enhance understanding of influences on learning, and improve teaching and learning in high school and middle school STEM classes. They will leverage the latest tools for data processing and many different streams of data that can be collected in technology-rich classrooms to (1) identify classroom factors that affect learning and (2) explore how to use that data to automatically track development of students' understanding and capabilities over time.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1418020
Funding Period: 
Mon, 09/01/2014 to Thu, 08/31/2017
Full Description: 

This Research on Education and Learning (REAL) project arises from an October 2014 Ideas Lab on Data-intensive Research to Improve Teaching and Learning. The intentions of that effort were to (1) bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term; and (2) revolutionize learning in the longer term. In this project, researchers from Carnegie-Mellon University, Wested, Arizona State University, and Northwestern University will collaborate to enhance understanding of influences on learning, and improve teaching and learning in high school and middle school STEM classes. To accomplish this, they will leverage the latest tools for data processing and many different streams of data that can be collected in technology-rich classrooms to (1) identify classroom factors that affect learning and (2) explore how to use that data to automatically track development of students' understanding and capabilities over time.

Two forces are poised to transform research on learning. First, more and more student work is conducted on computers and online, producing vast amounts of learning-related data. At the same time, advances in computing, data mining, and learning analytics are providing new tools for the collection, analysis, and representation of these data. Together, the available data and analytical tools enable smart and responsive systems that personalize learning experiences for individual learners. The PIs aim to collect highly enriched data that go far beyond typical computer data capture, leveraging the latest tools for data processing to generate new insights about STEM teaching and learning. Working to maximize the potential while mitigating the risks of automated data collection and analysis, they will: (1) collect and integrate diverse sources of data including log files, videos, and written artifacts from across eight different two-week enactments of two different computer supported learning environments (one used in middle school math and one in high school science); and (2) compare analyses of log-file data with analyses of integrated datasets to understand the possibilities and limitations in using log-file data for assessment of student learning and proficiency. The collaborators expect their findings will inform both theories and practical recommendations applicable across a wide range of disciplines and settings.

Investigating How to Enhance Scientific Argumentation through Automated Feedback in the Context of Two High School Earth Science Curriculum Units

This project responds to the need for technology-enhanced assessments that promote the critical practice of scientific argumentation--making and explaining a claim from evidence about a scientific question and critically evaluating sources of uncertainty in the claim. It will investigate how to enhance this practice through automated scoring and immediate feedback in the context of two high school curriculum units--climate change and fresh-water availability--in schools with diverse student populations. 

Lead Organization(s): 
Award Number: 
1418019
Funding Period: 
Mon, 09/01/2014 to Fri, 08/31/2018
Full Description: 

With the current emphasis on learning science by actively engaging in the practices of science, and the call for integration of instruction and assessment; new resources, models, and technologies are being developed to improve K-12 science learning. Student assessment has become a nationwide educational priority due, in part, to the need for relevant and timely data that inform teachers, administrators, researchers, and the public about how all students perform and think while learning science. This project responds to the need for technology-enhanced assessments that promote the critical practice of scientific argumentation--making and explaining a claim from evidence about a scientific question and critically evaluating sources of uncertainty in the claim. It will investigate how to enhance this practice through automated scoring and immediate feedback in the context of two high school curriculum units--climate change and fresh-water availability--in schools with diverse student populations. The project will apply advanced automated scoring tools to students' written scientific arguments, provide individual students with customized feedback, and teachers with class-level information to assist them with improving scientific argumentation. The key outcome of this effort will be a technology-supported assessment model of how to advance the understanding of argumentation, and the use of multi-level feedback as a component of effective teaching and learning. The project will strengthen the program's current set of funded activities on assessment, focusing these efforts on students' argumentation as a complex science practice.

This design and development research targets high school students (n=1,940) and teachers (n=22) in up to 10 states over four years. The research questions are: (1) To what extent can automated scoring tools, such as c-rater and c-rater-ML, diagnose students' explanations and uncertainty articulations as compared to human diagnosis?; (2) How should feedback be designed and delivered to help students improve scientific argumentation?; (3) How do teachers use and interact with class-level automated scores and feedback to support students' scientific argumentation with real-data and models?; and (4) How do students perceive their overall experience with the automated scores and immediate feedback when learning core ideas in climate change and fresh-water availability topics through scientific argumentation enhanced with modeling? In Years 1 and 2, plans are to conduct feasibility studies to build automated scoring models and design feedback for previously tested assessments for the two curriculum units. In Year 3, the project will implement design studies in order to identify effective feedback through random assignment. In Year 4, a pilot study will investigate if effective feedback should be offered with or without scores. The project will employ a mixed-methods approach. Data-gathering strategies will include classroom observations; screencast and log data of teachers' and students' interaction with automated feedback; teachers' and students' surveys with selected- and open-ended questions; and in-depth interviews with teachers and students. All constructed-response explanations and uncertainty items will be scored using automated scoring engines with fine-grained rubrics. Data analysis strategies will include multiple criteria to evaluate the quality of automated scores; descriptive statistical abalyses; analysis of variance to investigate differences in outcomes from the designed studies' pre/posttests and embedded assessments; analysis of covariance to investigate student learning trajectories; two-level hierarchical linear modeling to study the clustering of students within a class; and analysis of screencasts and log data.

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.

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