Content Knowledge

How Deep Structural Modeling Supports Learning with Big Ideas in Biology (Collaborative Research: Capps)

This project addresses the pressing need to more effectively organize STEM (science, technology, engineering, and mathematics) teaching and learning around "big ideas" that run through science disciplines. Unfortunately, finding ways to teach big ideas effectively so they become useful as knowledge frameworks is a significant challenge. Deep structure modeling (DSM), the innovation advanced in this project, is designed to meet this challenge in the context of high school biology.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2010223
Funding Period: 
Sat, 08/01/2020 to Wed, 07/31/2024
Full Description: 

This project addresses the pressing need to more effectively organize STEM (science, technology, engineering, and mathematics) teaching and learning around "big ideas" that run through science disciplines. This need is forcefully advanced by policy leaders including the National Research Council and the College Board. They point out that learning is more effective when students organize and link information within a consistent knowledge framework, which is what big ideas should provide. Unfortunately, finding ways to teach big ideas effectively so they become useful as knowledge frameworks is a significant challenge. Deep structure modeling (DSM), the innovation advanced in this project, is designed to meet this challenge in the context of high school biology. In DSM, students learn a big idea as the underlying, or "deep" structure of a set of examples that contain the structure, but with varying outward details. As learners begin to apprehend the deep structure (i.e., the big idea) within the examples, they use the tools and procedures of scientific modeling to express and develop it. According to theories of learning that undergird DSM, the result of this process should be a big idea that is flexible, meaningful, and easy to express, thus providing an ideal framework for making sense of new information learners encounter (i.e., learning with the big idea). To the extent that this explanation is born out in rigorous research tests and within authentic curriculum materials, it contributes important knowledge about how teaching and learning can be organized around big ideas, and not only for deep structural modeling but for other instructional approaches as well.

This project has twin research and prototype development components. Both are taking place in the context of high school biology, in nine classrooms across three districts, supporting up to 610 students. The work focuses on three design features of DSM: (1) embedding model source materials with intuitive, mechanistic ideas; (2) supporting learners to abstract those ideas as a deep structure shared by a set of sources; and (3) representing this deep structure efficiently within the model. In combination, these features support students to understand an abstract, intuitively rich, and efficient knowledge structure that they subsequently use as a framework to interpret, organize, and link disciplinary content. A series of five research studies build on one another to develop knowledge about whether and how the design features bring about these anticipated effects. Earlier studies in the sequence are small-scale classroom experiments randomly assigning students to either deep structural modeling or to parallel, non modeling controls. Measures discriminate for the anticipated effects during learning and on posttests. Later studies use qualitative methods to carefully trace the anticipated effects over time and across topics. As a group, these studies are contributing generalized knowledge of how learners can effectively abstract and represent big ideas and how these ideas can be leveraged as frameworks for learning content with understanding. Two research-tested biology curriculum prototypes are being developed as the studies evolve: a quarter-year DSM biology curriculum centered on energy; and an eighth-year DSM unit centered on natural selection.

How Deep Structural Modeling Supports Learning with Big Ideas in Biology (Collaborative Research: Shemwell)

This project addresses the pressing need to more effectively organize STEM (science, technology, engineering, and mathematics) teaching and learning around "big ideas" that run through science disciplines. Unfortunately, finding ways to teach big ideas effectively so they become useful as knowledge frameworks is a significant challenge. Deep structure modeling (DSM), the innovation advanced in this project, is designed to meet this challenge in the context of high school biology.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2010334
Funding Period: 
Sat, 08/01/2020 to Wed, 07/31/2024
Full Description: 

This project addresses the pressing need to more effectively organize STEM (science, technology, engineering, and mathematics) teaching and learning around "big ideas" that run through science disciplines. This need is forcefully advanced by policy leaders including the National Research Council and the College Board. They point out that learning is more effective when students organize and link information within a consistent knowledge framework, which is what big ideas should provide. Unfortunately, finding ways to teach big ideas effectively so they become useful as knowledge frameworks is a significant challenge. Deep structure modeling (DSM), the innovation advanced in this project, is designed to meet this challenge in the context of high school biology. In DSM, students learn a big idea as the underlying, or "deep" structure of a set of examples that contain the structure, but with varying outward details. As learners begin to apprehend the deep structure (i.e., the big idea) within the examples, they use the tools and procedures of scientific modeling to express and develop it. According to theories of learning that undergird DSM, the result of this process should be a big idea that is flexible, meaningful, and easy to express, thus providing an ideal framework for making sense of new information learners encounter (i.e., learning with the big idea). To the extent that this explanation is born out in rigorous research tests and within authentic curriculum materials, it contributes important knowledge about how teaching and learning can be organized around big ideas, and not only for deep structural modeling but for other instructional approaches as well.

This project has twin research and prototype development components. Both are taking place in the context of high school biology, in nine classrooms across three districts, supporting up to 610 students. The work focuses on three design features of DSM: (1) embedding model source materials with intuitive, mechanistic ideas; (2) supporting learners to abstract those ideas as a deep structure shared by a set of sources; and (3) representing this deep structure efficiently within the model. In combination, these features support students to understand an abstract, intuitively rich, and efficient knowledge structure that they subsequently use as a framework to interpret, organize, and link disciplinary content. A series of five research studies build on one another to develop knowledge about whether and how the design features bring about these anticipated effects. Earlier studies in the sequence are small-scale classroom experiments randomly assigning students to either deep structural modeling or to parallel, non modeling controls. Measures discriminate for the anticipated effects during learning and on posttests. Later studies use qualitative methods to carefully trace the anticipated effects over time and across topics. As a group, these studies are contributing generalized knowledge of how learners can effectively abstract and represent big ideas and how these ideas can be leveraged as frameworks for learning content with understanding. Two research-tested biology curriculum prototypes are being developed as the studies evolve: a quarter-year DSM biology curriculum centered on energy; and an eighth-year DSM unit centered on natural selection.

Exploring Early Childhood Teachers' Abilities to Identify Computational Thinking Precursors to Strengthen Computer Science in Classrooms

This project will explore PK-2 teachers' content knowledge by investigating their understanding of the design and implementation of culturally relevant computer science learning activities for young children. The project team will design a replicable model of PK-2 teacher professional development to address the lack of research in early computer science education.

Lead Organization(s): 
Award Number: 
2006595
Funding Period: 
Tue, 09/01/2020 to Thu, 08/31/2023
Full Description: 

Strengthening computer science education is a national priority with special attention to increasing the number of teachers who can deliver computer science education in schools. Yet computer science education lacks the evidence to determine how teachers come to think about computational thinking (a problem-solving process) and how it could be integrated within their day-to-day classroom activities. For teachers of pre-kindergarten to 2nd (PK-2) grades, very little research has specifically addressed teacher learning. This oversight challenges the achievement of an equitable, culturally diverse, computationally empowered society. The project team will design a replicable model of PK-2 teacher professional development in San Marcos, Texas, to address the lack of research in early computer science education. The model will emphasize three aspects of teacher learning: a) exploration of and reflection on computer science and computational thinking skills and practices, b) noticing and naming computer science precursor skills and practices in early childhood learning, and c) collaborative design, implementation and assessment of learning activities aligned with standards across content areas. The project will explore PK-2 teachers' content knowledge by investigating their understanding of the design and implementation of culturally relevant computer science learning activities for young children. The project includes a two-week computational making and inquiry institute focused on algorithms and data in the context of citizen science and historical storytelling. The project also includes monthly classroom coaching sessions, and teacher meetups.

The research will include two cohorts of 15 PK-2 teachers recruited from the San Marcos Consolidated Independent School District (SMCISD) in years one and two of the project. The project incorporates a 3-phase professional development program to be run in two cycles for each cohort of teachers. Phase one (summer) includes a 2-week Computational Making and Inquiry Institute, phase two (school year) includes classroom observations and teacher meetups and phase three (late spring) includes an advanced computational thinking institute and a community education conference. Research and data collection on impacts will follow a mixed-methods approach based on a grounded theory design to document teachers learning. The mixed-methods approach will enable researchers to triangulate participants' acquisition of new knowledge and skills with their developing abilities to implement learning activities in practice. Data analysis will be ongoing, interweaving qualitative and quantitative methods. Qualitative data, including field notes, observations, interviews, and artifact assessments, will be analyzed by identifying analytical categories and their relationships. Quantitative data includes pre to post surveys administered at three-time points for each cohort. Inter-item correlations and scale reliabilities will be examined, and a repeated measures ANOVA will be used to assess mean change across time for each of five measures. Project results will be communicated via peer-reviewed journals, education newsletters, annual conferences, family and teacher meetups, and community art and culture events, as well as on social media, blogs, and education databases.

Comparing the Efficacy of Collaborative Professional Development Formats for Improving Student Outcomes of a Student-Teacher-Scientist Partnership Program

The goal of this project is to study how the integration of an online curriculum, scientist mentoring of students, and professional development for both teachers and scientist mentors can improve student outcomes. In this project, teachers and scientist mentors will engage collaboratively in a professional development module which focuses on photosynthesis and cellular respiration and is an example of a student-teacher-scientist partnership.

Lead Organization(s): 
Award Number: 
2010556
Funding Period: 
Tue, 09/01/2020 to Sun, 08/31/2025
Full Description: 

Science classrooms in the U.S. today increasingly expect students to engage in the practices of science in a way that help them form a deeper understanding of disciplinary core ideas and the practices by which science is done. To do this, students should learn how scientists work and communicate. It also calls for changes in how teachers teach science, which in turn creates a need for high-quality professional development so they can be more effective in the classroom. Professional scientists can also benefit from training preparing them to support teachers, motivate students, and model for students how scientists think and work. Preparing teachers and scientists through collaborative professional development can help maximize the impact they can have on student outcomes. To have the broadest impact, such professional development should be cost-effective and available to teachers in rural or underserved areas. This project focuses on high school life science (biology) teachers and their students. It will make use of an online mentoring platform, a student-teacher-scientist partnership program established in 2005. That study found that implementing in combination with high-quality, in-person collaborative teacher/scientist professional development resulted in positive and statistically significant effects on student achievement and attitudes versus business-as-usual methods of teaching the same science content. This project has two main components: 1) a replication study to determine if findings of the previous successful study hold true; and 2) adding an online format for delivering collaborative professional development to teachers and scientists enabling one to compare the effectiveness of online professional development and in-person professional development delivery formats for improving student outcomes.

The goal of this project is to study how the integration of an online curriculum, scientist mentoring of students, and professional development for both teachers and scientist mentors can improve student outcomes. In this project, teachers and scientist mentors will engage collaboratively in a professional development module which focuses on photosynthesis and cellular respiration and is an example of a student-teacher-scientist partnership. Teachers will use their training to teach the curriculum to their students with students receiving mentoring from the scientists through an online platform. Evaluation will examine whether this curriculum, professional development, and mentoring by scientists will improve student achievement on science content and attitudes toward scientists. The project will use mixed-methods approaches to explore potential factors underlying efficacy differences between in-person and online professional development. An important component of this project is comparing in-person professional development to an online delivery of professional development, which can be more cost-effective and accessible by teachers, especially those in rural and underserved areas.

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. 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

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

Evolving Minds: Promoting Causal-Explanatory Teaching and Learning of Biological Evolution in Elementary School

Adopting a teaching and curricular approach that will be novel in its integration of custom explanatory storybook materials with hands-on investigations, this project seeks to promote third grade students' understanding of small- and large-scale evolution by natural selection. By studying students across multiple school districts, this research will shed light on the benefits to diverse students of instruction that focuses on supporting children's capacities to cogently explain aspects of the biological world rather than learn disparate facts about it.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2009176
Funding Period: 
Mon, 06/01/2020 to Fri, 05/31/2024
Full Description: 

Natural selection is a fundamental mechanism of evolution, the unifying principle of biology. It is central to understanding the functional specialization of living things, the origin of species diversity and the inherent unity of biological life. Despite the early emergence of tendencies that can make evolution increasingly challenging to learn over time, natural selection is currently not taught until middle or high school. This is long after patterns of misunderstanding are likely to have become more entrenched. The current research responds to this situation. It targets elementary school as the time to initiate comprehensive instruction on biological evolution. Adopting a teaching and curricular approach that will be novel in its integration of custom explanatory storybook materials with hands-on investigations, it seeks to promote third grade students' understanding of small- and large-scale evolution by natural selection. By studying students across multiple school districts, this research will shed light on the benefits to diverse students of instruction that focuses on supporting children's capacities to cogently explain aspects of the biological world rather than learn disparate facts about it. It will also illuminate the value of simple tools, like explanatory storybooks, for elementary school teachers who are often expected to teach counterintuitive topics such as natural selection while not feeling confident in their own understanding.

This project will investigate changes in Grade 3 students' learning and reasoning about living things during implementation of a guided inquiry curriculum unit on evolution by natural selection that emphasizes causal-mechanistic explanation. Classroom inquiry activities and investigations into a range of real-world phenomena will be framed by engagement with a sequence of innovative custom causal-explanatory storybook, animation and writing prompt materials that were developed under prior NSF support to promote transferable, scientifically accurate theory- and evidence-based reasoning about natural selection. In response to the distinctive challenges of life science and evolution learning, the project will integrate and thematically unify currently disparate Next Generation Science Standards (NGSS) content and practice standards to create a comprehensive unit that addresses all three NGSS dimensions and is accompanied by evidence-based approaches to teacher professional development (PD). Using a design based research approach, and informed by cognitive developmental findings, this 4-year project will engage at least 700 students and their teachers and include partners from at least four school districts, Boston University, and TERC.

CAREER: Exploring Teacher Noticing of Students' Multimodal Algebraic Thinking

This project investigates and expands teachers' learning to notice in two important ways. First, the research expands beyond teachers' noticing of written and verbal thinking to attend to gesture and other aspects of embodied and multimodal thinking. Second, the project focuses on algebraic thinking and seeks specifically to understand how teacher noticing relates to the content of algebra. Bringing together multimodal thinking and the mathematical ideas in algebra has the potential to support teachers in providing broader access to algebraic thinking for more students.

Award Number: 
1942580
Funding Period: 
Mon, 06/01/2020 to Sat, 05/31/2025
Full Description: 

Effective teachers of mathematics attend to and respond to the substance of students' thinking in supporting classroom learning. Teacher professional development programs have supported teachers in learning to notice students' mathematical thinking and using that noticing to make instructional decisions in the classroom. This project investigates and expands teachers' learning to notice in two important ways. First, the research expands beyond teachers' noticing of written and verbal thinking to attend to gesture and other aspects of embodied and multimodal thinking. Second, the project focuses on algebraic thinking and seeks specifically to understand how teacher noticing relates to the content of algebra. Bringing together multimodal thinking and the mathematical ideas in algebra has the potential to support teachers in providing broader access to algebraic thinking for more students.

To study teacher noticing of multimodal algebraic thinking, this project will facilitate video club sessions in which teachers examine and annotate classroom video. The video will allow text-based and visual annotation of the videos to obtain rich portraits of the thinking that teachers notice as they examine algebra-related middle school practice. The research team will create a video library focused on three main algebraic thinking areas: equality, functional thinking, and proportional reasoning. Clips will be chosen that feature multimodal student thinking about these content areas, and provide moments that would be fruitful for advancing student thinking. Two cohorts of preservice teachers will engage in year-long video clubs using this video library, annotate videos using an advanced technological tool, and engage in reflective interviews about their noticing practices. Follow-up classroom observations will be conducted to see how teachers then notice multimodal algebraic thinking in their classrooms. Materials to conduct the video clubs in other contexts and the curated video library will be made available, along with analyses of the teacher learning that resulted from their implementation.

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

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

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

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

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

Crowdsourcing Neuroscience: An Interactive Cloud-based Citizen Science Platform for High School Students, Teachers, and Researchers

This project will develop a cloud-based platform that enables high school students, teachers, and scientists to conduct original neuroscience research in school classrooms.

Lead Organization(s): 
Award Number: 
1908482
Funding Period: 
Thu, 08/01/2019 to Mon, 07/31/2023
Full Description: 

Current priorities in school science education include engaging students in the practices of science as well as the ideas of science. This project will address this priority by developing a cloud-based platform that enables high school students, teachers, and scientists to conduct original neuroscience research in school classrooms. Before students and teachers initiate their own studies using the system, they will participate in existing research studies by contributing their own data and collaborating with researchers using the online, interactive system. When experienced with the system, students and teachers will become researchers by developing independent investigations and uploading them to the interactive platform. Both student-initiated and scientist-initiated proposals will be submitted to the platform, peer-reviewed by students and scientists, revised, and included in the online experimental bank. In addition to conducting their own studies using the platform, scientists will act as educators and mentors by populating the experiment bank with studies that can serve as models for students and provide science content for the educational resource center. This online system addresses a critical need in science education to involve students more fully and authentically in scientific inquiry where they gain experience in exploring the unknown rather than confirming what is already known.

This early stage design and development study is guided by three goals: 1) Develop an open-science citizen science platform for conducting human brain and behavior research in the classroom, 2) Develop a remote neuroscience Student-Teacher-Scientists (STS) partnership program for high schools, and 3) Evaluate the design, development, and implementation of the program and its impacts on students and tachers. In developing this project, the project team will link two quickly emerging trends, one in science education, and one in the sciences. Consistent with current priorities in science education, the project will engage students and their teachers in authentic, active inquiry where they learn scientific practices by using them to conduct authentic inquiry where a search for knowledge is grounded in finding evidence-based answers to original questions. On the science side, students and their science partners will participate in an open science approach by pre-registering their research and committing to an analysis plan before data are collected. In this project, students will primarily be using reaction time and online systems to do research that includes study of their own brain function. The project research is guided by three research questions. How does an online citizen neuroscience STS platform: a) impact students' understanding of, and abilities to apply neuroscience and experimental design concepts? b) Impact students' interests in, and attitudes toward science, including an awareness of science careers and applications? and c) Affect teachers' attitudes towards neuroscience teaching, and the use of inquiry-based strategies? A design-based research approach will be used to iteratively design a sustainable and scalable inquiry-based neuroscience curriculum with teachers as design partners.

Generalized Embodied Modeling to Support Science through Technology Enhanced Play (Collaborative Research: Danish)

The project will develop and research a new Mixed Reality environment (MR), called GEM-STEP, that leverages play and embodiment as resources for integrating computational modeling into the modeling cycle as part of science instruction for elementary students.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1908632
Funding Period: 
Thu, 08/01/2019 to Sun, 07/31/2022
Full Description: 

The project will develop and research a new Mixed Reality environment (MR), called GEM-STEP, that leverages play and embodiment as resources for integrating computational modeling into the modeling cycle as part of science instruction for elementary students. GEM stands for Generalized Embodied Modeling. Through these embodied, play-as-modeling activities, students will learn the core concepts of science, and the conceptual skills of modeling and systematic measurement. MR environments use new sensing technologies to help transform young children's physical actions during pretend play into a set of symbolic representations and parameters in a science simulation. As students physically move around the classroom, the computer will track their motion and interactions with selected objects and translate their physical activity into a shared display. For example, students pretend they are water particles and work together to model different states of matter. The children see their activity projected onto a computer simulation where a model of a water particle is displayed over the video of themselves. As students collectively reflect upon the nature of a water molecule, they refine their understanding of water as ice, a liquid or a gas. The proposed innovation allows the students to program and revise their own mixed reality simulations as part of their modeling cycle. Embodied and computational modeling will help students to reflect on their models in a unique way that will make their models more computationally accurate and enhance their understanding of the underlying concepts.

The project will research how using the body as a component of the modeling cycle differs from and interacts with the articulation of a scientific model through more structured computational means. The project will investigate the benefits of combining embodiment with computational elements in GEM:STEP by studying the range of concepts that students can learn in this manner. Lessons will be developed to address different disciplinary core ideas, such as states of matter, pollination as a complex system, or decomposition, as well as cross-cutting concepts of systems thinking, and energy/matter flow, all of which link directly to upper elementary science curriculum. Project research will gather data to understand what kinds of models students develop, what learning processes are supported using GEM:STEP, and what learning results. The data will include: (1) documenting and analyzing what students modeled and how accurate the models are; (2) recording student activity using audio and voice to code their activity to document learning processes and to look at how different forms of modeling interact with one another to promote learning; and (3) pre-post content measures to assess learning. All of the software that is developed for GEM:STEP will be made available as Open Source projects, allowing other researchers to build upon and extend this work. The results of the research will be disseminated in academic conferences and peer reviewed journals. The motion tracking software is already available on Github, a popular open-source repository. Once developed, the aim is to implement GEM:STEP in a wide range of classroom contexts, supported by a user-friendly interface, teacher guides, and professional development.

Generalized Embodied Modeling to Support Science through Technology Enhanced Play (Collaborative Research: Enyedy)

The project will develop and research a new Mixed Reality environment (MR), called GEM-STEP, that leverages play and embodiment as resources for integrating computational modeling into the modeling cycle as part of science instruction for elementary students.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
1908791
Funding Period: 
Thu, 08/01/2019 to Sun, 07/31/2022
Full Description: 

The project will develop and research a new Mixed Reality environment (MR), called GEM-STEP, that leverages play and embodiment as resources for integrating computational modeling into the modeling cycle as part of science instruction for elementary students. GEM stands for Generalized Embodied Modeling. Through these embodied, play-as-modeling activities, students will learn the core concepts of science, and the conceptual skills of modeling and systematic measurement. MR environments use new sensing technologies to help transform young children's physical actions during pretend play into a set of symbolic representations and parameters in a science simulation. As students physically move around the classroom, the computer will track their motion and interactions with selected objects and translate their physical activity into a shared display. For example, students pretend they are water particles and work together to model different states of matter. The children see their activity projected onto a computer simulation where a model of a water particle is displayed over the video of themselves. As students collectively reflect upon the nature of a water molecule, they refine their understanding of water as ice, a liquid or a gas. The proposed innovation allows the students to program and revise their own mixed reality simulations as part of their modeling cycle. Embodied and computational modeling will help students to reflect on their models in a unique way that will make their models more computationally accurate and enhance their understanding of the underlying concepts.

The project will research how using the body as a component of the modeling cycle differs from and interacts with the articulation of a scientific model through more structured computational means. The project will investigate the benefits of combining embodiment with computational elements in GEM:STEP by studying the range of concepts that students can learn in this manner. Lessons will be developed to address different disciplinary core ideas, such as states of matter, pollination as a complex system, or decomposition, as well as cross-cutting concepts of systems thinking, and energy/matter flow, all of which link directly to upper elementary science curriculum. Project research will gather data to understand what kinds of models students develop, what learning processes are supported using GEM:STEP, and what learning results. The data will include: (1) documenting and analyzing what students modeled and how accurate the models are; (2) recording student activity using audio and voice to code their activity to document learning processes and to look at how different forms of modeling interact with one another to promote learning; and (3) pre-post content measures to assess learning. All of the software that is developed for GEM:STEP will be made available as Open Source projects, allowing other researchers to build upon and extend this work. The results of the research will be disseminated in academic conferences and peer reviewed journals. The motion tracking software is already available on Github, a popular open-source repository. Once developed, the aim is to implement GEM:STEP in a wide range of classroom contexts, supported by a user-friendly interface, teacher guides, and professional development.

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