Content Knowledge

SimSnap: Orchestrating Collaborative Learning in Biology through Reconfigurable Simulations (Collective Research: Tissenbaum)

This project will develop and research collaborative learning in biology using tablet-style computers that support simulations of biological systems and that can be used individually or linked together. The project will be implemented over 4 years in middle school life science classes, in which students will solve important socio-scientific problems, such as growing healthy plants in community gardens to address the need to grow sufficient produce to fulfill ever increasing and varying demands.

Award Number: 
2010456
Funding Period: 
Sat, 08/01/2020 to Wed, 07/31/2024
Full Description: 

The project will develop and research collaborative learning in biology using tablet-style computers that support simulations of biological systems and that can be used individually or linked together. The project will be implemented over 4 years in middle school life science classes, in which students will solve important socio-scientific problems, such as growing healthy plants in community gardens to address the need to grow sufficient produce to fulfill ever increasing and varying demands. Working within a digital plant habitat simulation, students will investigate how different environmental and genetic factors affect the health of a variety of plants and vegetables. As students engage in design tasks, they will be able to seamlessly move between individual and collaborative work with peers by "snapping" their tablets together (by placing them next to each other) to create a single shared simulation that spans all their devices. Students will be able to drop elements of their individual inquiry activities (e.g., plant types, soil compositions) into their shared simulation, providing opportunities for collaborative discussion and knowledge integration. When students "unsnap" their tablets, their collaborative work will stay with them in a digital journal, for individual reflection or as a resource for future collaborative activities (with potentially new group members). Project curriculum units will help students see the connections between the science concepts and principles they are learning, as they iteratively work on their designs through a combination of individual, collaborative and whole class learning. This work will also develop new approaches that help teachers understand the state of the class when students are taking part open-ended biology investigations, and support the teacher classroom orchestration and facilitation. Project research findings, materials and software will be made available to interested teachers, administrators, policymakers, and researchers nationwide on the project website.

The project will research collaborative learning along three planesindividual, small group and whole classwith technologies and classroom teachers supporting learning in innovative ways. Research has shown that technology can support collaborative learning, but there is limited research on how it can support transitions between individual and collaborative learning. While research has also shown that collaborative or individual learning may be more beneficial depending on the task or learning goal, there are relatively few studies that examine the potential for learning when students move between these social planes. Further, as these configurations become increasingly complex, there is also the challenge of how to support teachers' orchestration and facilitation. Studies will focus around four main research questions: 1) How does engaging in personally relevant biology curriculum through user-driven investigations help students understand the underlying science content? 2) How are students using and sharing the work of others to develop their own understanding about the underlying science concepts? 3) How do designs that allow for the movement between individual, small group, and whole class configurations allow students to work as a learning community? 4) How does the technology platform support teachers in orchestrating and facilitating classroom activities? Project studies will follow a design-based research methodology, guided by the premise that learning in naturalistic settings is the product of multiple interacting variables that cannot be reduced to a small set of controlled factors. The research will be broken down across four main developmental arcs: Technology design and iteration; Facilitation, user testing, and co-design; Classroom implementation; and Research and analysis. Each of the designed technologies will be user tested in the lab prior to being deployed in the classroom. Part of the analysis will focus on how the different technologies (i.e., individual and connected tablets, the teacher orchestration tablet) support learning and collaboration in naturalistic settings. The project research framework provides a way to examine the usability, usefulness and impact of interactions in a multi-user collaborative context using a mixed-method approach with various quantitative measures and qualitative indicators. Teachers will be prepared to use the system through 2-week summer institutes, during which they will also participate in co-design of the curriculum and the technology. Project research findings, materials and software will be made available to interested teachers, administrators, policymakers, and researchers nationwide on the project website, as well as being disseminated to appropriate audiences via conference presentations and publications.

Developing a Modeling Orientation to Science: Teaching and Learning Variability and Change in Ecosystems (Collaborative Research: Miller)

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

Lead Organization(s): 
Award Number: 
2010155
Funding Period: 
Tue, 09/01/2020 to Thu, 08/31/2023
Full Description: 

There is an ongoing need to find ways to make science relevant for school students and an increasing need to support student interpretation of large data sets. This project addresses these needs by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts. Students construct and critique models that they and peers invent and, through the lens of models, develop foundational knowledge about the roles of variability and change in ecosystem functioning, as well as the roles of models and argumentation in scientific practice. The context for students' work is a set of citizen science-based investigations of changes in ecosystems in Maine conducted in twelve collaborating classrooms. The project studies how and to what extent students' use of different forms of modeling emerges from and informs how they investigate ecosystems. A parallel research effort investigates how and to what extent the development of teachers' comfort and proficiency with modeling changes students' engagement in these forms of modeling and students' understandings of ecosystems. A key contribution of the project is capitalizing on the Gulf of Maine Research Institutes's Ecosystem Investigation Network's citizen science field research to ground for middle school students the need to invent, revise, and contest models about real ecosystems. The understandings that result from the project's research provide evidence toward first, scaling the learning experiences to the network of 500+ teachers who are part of the Ecosystem Investigation Network, and, second, replication by programs nationally that aim to engage students in data-rich, field-based ecological investigations.

The investigation takes place in twelve collaborating middle-school classrooms, drawn from the network of 500+ Maine teachers trained in Maine's Ecosystem Investigation Network. Over the course of their field investigations, students engage in the construction, critique, and revision of three forms of modeling that play central roles in ecology: microcosms, system dynamics, and data modeling. Two innovations are introduced over the course of the project. The first is focused on enriching classroom supports for engaging in multiple forms of modeling. The second involves enhancing middle school teachers' learning about modeling, especially in the context of large data citizen science investigations. The study uses a mixed methods approach to explore the impact of the innovations on the experiences and understandings of both teachers and students. Instruments include teacher interviews and questionnaires, student interviews, and classroom observation. The understandings that result from the project's research will inform the design of professional development for teachers around data analysis and interpretation, and around how student understanding of modeling develops with sustained support, both of which are practices at the heart of scientific literacy.

Developing a Modeling Orientation to Science: Teaching and Learning Variability and Change in Ecosystems (Collaborative Research: Lehrer)

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

Lead Organization(s): 
Award Number: 
2010207
Funding Period: 
Tue, 09/01/2020 to Thu, 08/31/2023
Full Description: 

There is an ongoing need to find ways to make science relevant for school students and an increasing need to support student interpretation of large data sets. This project addresses these needs by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts. Students construct and critique models that they and peers invent and, through the lens of models, develop foundational knowledge about the roles of variability and change in ecosystem functioning, as well as the roles of models and argumentation in scientific practice. The context for students' work is a set of citizen science-based investigations of changes in ecosystems in Maine conducted in twelve collaborating classrooms. The project studies how and to what extent students' use of different forms of modeling emerges from and informs how they investigate ecosystems. A parallel research effort investigates how and to what extent the development of teachers' comfort and proficiency with modeling changes students' engagement in these forms of modeling and students' understandings of ecosystems. A key contribution of the project is capitalizing on the Gulf of Maine Research Institutes's Ecosystem Investigation Network's citizen science field research to ground for middle school students the need to invent, revise, and contest models about real ecosystems. The understandings that result from the project's research provide evidence toward first, scaling the learning experiences to the network of 500+ teachers who are part of the Ecosystem Investigation Network, and, second, replication by programs nationally that aim to engage students in data-rich, field-based ecological investigations.

The investigation takes place in twelve collaborating middle-school classrooms, drawn from the network of 500+ Maine teachers trained in Maine's Ecosystem Investigation Network. Over the course of their field investigations, students engage in the construction, critique, and revision of three forms of modeling that play central roles in ecology: microcosms, system dynamics, and data modeling. Two innovations are introduced over the course of the project. The first is focused on enriching classroom supports for engaging in multiple forms of modeling. The second involves enhancing middle school teachers' learning about modeling, especially in the context of large data citizen science investigations. The study uses a mixed methods approach to explore the impact of the innovations on the experiences and understandings of both teachers and students. Instruments include teacher interviews and questionnaires, student interviews, and classroom observation. The understandings that result from the project's research will inform the design of professional development for teachers around data analysis and interpretation, and around how student understanding of modeling develops with sustained support, both of which are practices at the heart of scientific literacy.

Developing a Modeling Orientation to Science: Teaching and Learning Variability and Change in Ecosystems (Collaborative Research: Peake)

This project addresses the need to make science relevant for school students and to support student interpretation of large data sets by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts.

Partner Organization(s): 
Award Number: 
2010119
Funding Period: 
Tue, 09/01/2020 to Thu, 08/31/2023
Full Description: 

There is an ongoing need to find ways to make science relevant for school students and an increasing need to support student interpretation of large data sets. This project addresses these needs by leveraging citizen science data about ecology and developing instruction to support student analyses of these data. This collaboration between Gulf of Maine Research Institute, Bowdoin College and Vanderbilt University engages middle-school students in building and revising models of variability and change in ecosystems and studies the learning and instruction in these classroom contexts. Students construct and critique models that they and peers invent and, through the lens of models, develop foundational knowledge about the roles of variability and change in ecosystem functioning, as well as the roles of models and argumentation in scientific practice. The context for students' work is a set of citizen science-based investigations of changes in ecosystems in Maine conducted in twelve collaborating classrooms. The project studies how and to what extent students' use of different forms of modeling emerges from and informs how they investigate ecosystems. A parallel research effort investigates how and to what extent the development of teachers' comfort and proficiency with modeling changes students' engagement in these forms of modeling and students' understandings of ecosystems. A key contribution of the project is capitalizing on the Gulf of Maine Research Institutes's Ecosystem Investigation Network's citizen science field research to ground for middle school students the need to invent, revise, and contest models about real ecosystems. The understandings that result from the project's research provide evidence toward first, scaling the learning experiences to the network of 500+ teachers who are part of the Ecosystem Investigation Network, and, second, replication by programs nationally that aim to engage students in data-rich, field-based ecological investigations.

The investigation takes place in twelve collaborating middle-school classrooms, drawn from the network of 500+ Maine teachers trained in Maine's Ecosystem Investigation Network. Over the course of their field investigations, students engage in the construction, critique, and revision of three forms of modeling that play central roles in ecology: microcosms, system dynamics, and data modeling. Two innovations are introduced over the course of the project. The first is focused on enriching classroom supports for engaging in multiple forms of modeling. The second involves enhancing middle school teachers' learning about modeling, especially in the context of large data citizen science investigations. The study uses a mixed methods approach to explore the impact of the innovations on the experiences and understandings of both teachers and students. Instruments include teacher interviews and questionnaires, student interviews, and classroom observation. The understandings that result from the project's research will inform the design of professional development for teachers around data analysis and interpretation, and around how student understanding of modeling develops with sustained support, both of which are practices at the heart of scientific literacy.

Assessing College-Ready Computational Thinking (Collaborative Research: Wilson)

The goal of this project is to develop learning progressions and assessment items targeting computational thinking. The items will be used for a test of college-ready critical reasoning skills and will be integrated into an existing online assessment system, the Berkeley Assessment System Software.

Award Number: 
2010314
Funding Period: 
Tue, 09/01/2020 to Sat, 08/31/2024
Full Description: 

Because of the growing need for students to be college and career ready, high-quality assessments of college readiness skills are in high demand. To realize the goal of preparing students for college and careers, assessments must measure important competencies and provide rapid feedback to teachers. It is necessary to go beyond the limits of multiple-choice testing and foster the skills and thinking that lie at the core of college and career ready skills, such as computational thinking. Computational thinking is a set of valuable skills that can be used to solve problems, design systems, and understand human behavior, and is thus essential to developing a more STEM-literate public. Computational thinking is increasingly seen as a fundamental analytical skill that everyone, not just computer scientists, can use. The goal of this project is to develop learning progressions and assessment items targeting computational thinking. The items will be used for a test of college-ready critical reasoning skills and will be integrated into an existing online assessment system, the Berkeley Assessment System Software.

The project will address a set of research questions focused on 1) clarifying computational thinking constructs, 2) usability, reliability of validity of assessment items and the information they provide, 3) teachers' use of assessments, and 4) relationships to student performance. The study sample of 2,700 used for the pilot and field tests will include all levels of students in 10th through 12th grade and first year college students (both community college and university level). The target population is students in schools which are implementing the College Readiness Program (CRP) of the National Mathematics and Science Institute. In the 2020-21 academic year 54 high schools across 11 states (CA, GA, FL, ID, LA, NC, NM, OH, TX, VA, and WA) will participate. This will include high school students in Advanced Placement classes as well as non-Advanced Placement classes.  The team will use the BEAR Assessment System to develop and refine assessment materials. This system is an integrated approach to developing assessments that seeks to provide meaningful interpretations of student work relative to cognitive and developmental goals. The researchers will gather empirical evidence to develop and improve the assessment materials, and then gather reliability and validity evidence to support their use. In total, item response data will be collected from several thousand students. Student response data will be analyzed using multidimensional item response theory models.

Geological Construction of Rock Arrangements from Tectonics: Systems Modeling Across Scales

This project will create two curriculum units that use sophisticated simulations designed for students in secondary schools that integrate the study of the tectonic system and the rock genesis system. The project seeks to overcome the more typical approaches taken in earth science classrooms where such geologic processes are treated as discrete and highly predictable, rather than intertwined and dynamic.

Lead Organization(s): 
Award Number: 
2006144
Funding Period: 
Thu, 10/01/2020 to Mon, 09/30/2024
Full Description: 

Plate tectonics is the fundamental theory of geology that underlies almost all geological processes, including land and rock formation. However, the geologic processes and immense timeframes involved are often misunderstood. This study will create two curriculum units that use sophisticated simulations designed for students in secondary schools. The simulations will integrate the study of the tectonic system and the rock genesis system. Data from the simulations would be students' sources of evidence. For instance, the Tectonic Rock Explorer would use a sophisticated modeling engine that uses the physics involved in geodynamic data to represent compressional and tensional forces and calculate pressure and temperature in rock forming environments. This project seeks to overcome the more typical approaches taken in earth science classrooms where such geologic processes are treated as discrete and highly predictable, rather than intertwined and dynamic. In addition, this study would include work on students with disabilities in earth science classrooms and explore the practices that seem to be particularly useful in helping understand these systems. By working with simulations, the researchers intend to engage students in scientific practices that are more authentic to the ways that geologists work. The researchers will study if and how these simulations and the computer-based tools allow students to observe and manipulate processes that would be may otherwise be inaccessible.

This work follows on from prior work done by the Concord Consortium on simulations of earth systems. The design and development progression in Years 1 and 2 would create two units. The first module focuses on the relationship between tectonic movement and rock formation. The second would investigate geochronology and dating of rock formations. The researchers would work with 3 teachers (and classes), and then 15 teachers (and classes) using automated data logs, class observations, and video of students working in groups in Years 1 and 2. Professional development for teachers would be followed by the creation of educative materials. Researchers will also develop the framework for an assessment tool that includes understanding of geologic terms and embedded assessments. The researchers will used a mixed methods approach to analyze student data, including analyses cycles of analysis of students pre- and post-test scores on targeted concepts, reports of student performances on tasks embedded in the simulations, and the coding of videos to analyze discourse between partners and the supports provided by teachers. Teacher data will be analyzed using interviews, surveys and journals, with some special focus on how they are seeing students with identified disabilities respond to the materials and simulations. The research team intends to make materials widely available to thousands of students through their networks and webpages, and pursue outreach and dissemination in scholarly and practitioner conferences and publications.

Supporting Elementary Teacher Learning for Effective School-Based Citizen Science (TL4CS)

This project will develop two forms of support for teachers: guidance embedded in citizen science project materials and teacher professional development. The overarching goal of the project is to generate knowledge about teacher learning that enables elementary school citizen science to support students' engagement with authentic science content and practices through data collection and sense making.

Lead Organization(s): 
Award Number: 
2009212
Funding Period: 
Wed, 07/01/2020 to Sun, 06/30/2024
Full Description: 

Citizen science involves individuals, who are not professional scientists, in authentic scientific research, typically in collaboration with professional scientists. When implemented well in elementary schools, citizen science projects immerse students in science content and engage them with scientific practices. These projects can also create opportunities for students to connect with their local natural surroundings, which is needed, as some research has suggested that children are becoming increasingly detached from nature. The classroom teacher plays a critical role in ensuring that school-based citizen science projects are implemented in a way that maximizes the benefits. However, these projects typically do not include substantial guidance for teachers who want to implement the projects for instructional purposes. This project will develop two forms of support for teachers: (1) guidance embedded in citizen science project materials and (2) teacher professional development. It will develop materials and professional development experiences to support teacher learning for 80 5th grade teachers impacting students in 40 diverse elementary schools.

The overarching goal of this project is to generate knowledge about teacher learning that enables elementary school citizen science to support students' engagement with authentic science content and practices through data collection and sense making. Specifically, the study is designed to address the following research questions: (1) What kinds of support foster teacher learning for enacting effective school-based citizen science? (2) How do supports for teacher learning shape the way teachers enact school-based citizen science? and (3) What is the potential of school-based citizen science for positively influencing student learning and student attitudes toward nature and science? Data collected during project implementation will include teacher surveys, student surveys and assessments, and case study protocols.

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.

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