Post-secondary

Anchoring High School Students in Real-Life Issues that Integrate STEM Content and Literacy

Through the integration of STEM content and literacy, this project will study the ways teachers implement project practices integrating literacy activities into STEM learning. Teachers will facilitate instruction using scenarios that present students with everyday, STEM-related issues, presented as scenarios, that they read and write about. After reading and engaging with math and science content, students will write a source-based argument in which they state a claim, support the claim with evidence from the texts, and explain the multiple perspectives on the issue.

Lead Organization(s): 
Award Number: 
2010312
Funding Period: 
Sat, 08/15/2020 to Sun, 07/31/2022
Full Description: 

The STEM Literacy Project sets out to support student learning through developing teacher expertise in collaborative integration of STEM in student writing and literacy skills development. Facilitated by teachers, students will read, discuss, and then write about real-world STEM scenarios, such as water quality or health. The project will build on and research a professional development program first developed through a state-supported literacy program for middle and high school science and math teachers to improve literacy-integrated instruction. The goals of this project include the following: (1) Create a community of practice that recognizes high school teachers as content experts; (2) Implement high quality professional development for teachers on STEM/Literacy integration; (3) Develop assessments based on STEM and literacy standards that inform instruction; and (4) Conduct rigorous research to understand the impact of the professional development. The program is aligned with state and national standards for college and career readiness. Project resources will be widely shared through a regularly updated project website (stemliteracyproject.org), conference presentations, and publications reaching researchers, developers, and educators. These resources will include scenario-based assessment tools and instructional materials.

Through the integration of STEM content and literacy, the project will study the ways teachers implement project practices integrating literacy activities into STEM learning. Teachers will facilitate instruction using scenarios that present students with everyday, STEM-related issues, presented as scenarios, that they read and write about. After reading and engaging with math and science content, students will write a source-based argument in which they state a claim, support the claim with evidence from the texts, and explain the multiple perspectives on the issue. These scenarios provide students with agency as they craft an argument for an audience, such as presenting to a city council, a school board, or another group of stakeholders. Project research will use a mixed methods design. Based on the work completed through the initial designs and development of scenario-based assessments, rubrics, and scoring processes, the project will study the impact on instruction and student learning. Using a triangulation design convergence model, findings will be compared and contrasted in order for the data to inform one another and lead to further interpretation of the data. project will analyze the features of STEM content learning after program-related instruction. Data collected will include pre-post student scenario-based writing; pre-post interviews of up to 40 students each year; pre-post teacher interviews; and teacher-created scenario-based assessments and supporting instructional materials. Student learning reflected in the assessments paired with student and teacher interview responses will provide a deeper understanding of this approach of integrating STEM and literacy. The use of discourse analysis methods will allow growth in content learning to be measured through language use. Project research will build knowledge in the field concerning how participation in teacher professional development integrating STEM content in literacy practices impacts teacher practices and student learning.

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

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: 
2010265
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.

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.

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.

Learning to Teach During COVID-19: Leveraging Simulated Classrooms as Practice-based Spaces for Preservice Elementary Teachers within Online Teacher Education Courses

The COVID-19 pandemic has significantly disrupted the ability of teacher education programs to place their teacher candidates in typical K-12 teaching settings as a part of learning to teach. This project examines how simulated classroom field experiences for preservice teachers can be implemented in online and emergency remote teacher education courses.

Lead Organization(s): 
Award Number: 
2032179
Funding Period: 
Mon, 06/15/2020 to Mon, 05/31/2021
Full Description: 

School-based field experiences are a critical part of preservice teacher education. The COVID-19 pandemic has significantly disrupted the ability of teacher education programs to place their teacher candidates in typical K-12 teaching settings as a part of learning to teach. This project examines how simulated classroom field experiences for preservice teachers can be implemented in online and emergency remote teacher education courses. Elementary mathematics and science teacher educators are provided with opportunities to engage their preservice teachers in practice-based spaces using mixed-reality simulated classroom environments. These simulations are real-time lessons with animated student avatars that are voiced by an interactor who is responding to the teacher's lesson in real time in ways that represent authentic student thinking. This project aims to develop support materials for integrating simulated field experiences into elementary mathematics and science teacher education courses. The research will seek to understand what preservice teachers learn about teaching from these experiences, how teacher educators integrate the simulated field experiences into coursework, and how such simulated experiences can be integrated in remote, online courses in ways that support preservice teacher learning.

This project advances knowledge through the development and deployment of simulation-based tools that develop preservice elementary teachers' abilities to teach mathematics and science. Preservice teachers use performance tasks to deliver instruction in the simulated classroom. The project develops support materials for teacher educators to integrate this work into online and/or emergency remote teacher education courses (in response to COVID-19) in ways that support engagement in ambitious teaching practice. The project assesses impact on preservice teachers' ambitious teaching practice through artifacts of the simulated classroom practice, including observations and recordings of the simulated interactions and preservice teacher surveys and assessments of their use of ambitious teaching practices. The project evaluates the ways in which teacher educators integrate the simulated field experience into their emergency remote teacher education courses through surveys and interviews. The research addresses the immediate COVID-19 pandemic challenges in providing field experiences for students and provides long-term support for the ongoing challenge of finding field experience settings that are conducive to preparing highly-qualified elementary mathematics and science teachers.

CAREER: Spreading Computational Literacy Equitably via Integration of Computing in Preservice Teacher Preparation

This project will study the effect of integrating computing into preservice teacher programs. The project will use design-based research to explore how to connect computing concepts and integration activities to teachers' subject area knowledge and teaching practice, and which computing concepts are most valuable for general computational literacy.

Lead Organization(s): 
Award Number: 
1941642
Funding Period: 
Wed, 07/01/2020 to Mon, 06/30/2025
Full Description: 

Understanding and creating computer-powered solutions to professional and personal problems enables people to be safe, resourceful, and inventive in the technology-infused world. To empower society, K-12 education is rapidly changing to spread computational literacy. To spread literacy equitably, schools must give all students opportunities to understand and design computing solutions. However, school schedules are already packed with required coursework, and most teachers graduated from programs that did not offer computer science courses. To spread computational literacy within the K-12 system, this project will integrate computing into all preservice teacher programs at Georgia State University. This approach enables all teachers, regardless of primary discipline or grade band, to introduce their students to authentic computing solutions within their discipline and use these solutions as powerful tools for teaching disciplinary content and practices. In addition, this approach ensures equity because all preservice teachers will learn to use computing tools through their regular coursework, rather than a self-selected group that chooses to engage in elective courses or professional development on the topic. The project will also require preservice teachers to use computing-integrated activities in their student teaching experiences. This requirement helps teachers gain the confidence to use the activities in their future classrooms and immediately benefits students in the Atlanta area, who are primarily from groups that are underrepresented in computing, including women, people of color and those who are from low-income families.

This project will study the effect of computing integration in preservice teacher programs on computational literacy. Preservice teacher programs, like K-12 school schedules, are loaded with subject area, pedagogy, and licensure requirements. Therefore, research needs to examine the most sustainable methods for integrating computing into these programs. The proposed project will use design-based research to explore 1) how to connect computing concepts and integration activities to teachers' subject area knowledge and teaching practice, and 2) which computing concepts are most valuable for general computational literacy. Because computational literacy is a relatively new literacy, the computing education community still debates which concepts are foundational for all citizens. By studying computing integration in a range of grade bands and subject areas, this project will explore which computing concepts are applicable in a wide range of subjects. These research activities will feed directly into the teaching objective of this project ? to provide computing education and computational literacy to all preservice teachers. This project will prepare about 1500 preservice teachers (more than half of them will be women) across all grades and subject areas who can teach computing integrated activities.

 

Fourteenth International Congress on Mathematical Education (ICME14) Travel Grant

This project will support the participation of 53 US K-12 mathematics teachers, graduate students, community college/university mathematicians, mathematics teacher educators, and mathematics education researchers to attend the Fourteenth International Congress for Mathematical Education (ICME-14) in Shanghai, China.

Project Email: 
Lead Organization(s): 
Award Number: 
1908084
Funding Period: 
Sun, 09/01/2019 to Mon, 02/28/2022
Project Evaluator: 
Full Description: 

This project will support the participation of 53 US K-12 mathematics teachers, graduate students, community college/university mathematicians, mathematics teacher educators, and mathematics education researchers to attend the Fourteenth International Congress for Mathematical Education (ICME-14) to be held in Shanghai, China July 9-16, 2020. While mathematics education in the United States has its own culture and expectations, the work and conversations of mathematics educators across the world might contribute to our understanding of issues facing our community today such as curriculum development, the use of technology, strategies for reaching all students, teacher education and professional development. The questions we have as a nation about our own mathematics education might be informed and enlightened by international conversations with others confronting similar issues. A research team led by Sharon McCrone, University of New Hampshire, will prepare a 2020 Fact Book on US mathematics education, building on reports for prior ICMEs. The travel grant will increase the number and diversity of the US mathematics education community attending the international congress, which will enable a broader representation from the US to benefit from interaction with the world's leading mathematics educators.

Through a careful selection process, experts in the field will identify travel recipients most likely to benefit from attending ICME-14 and well-positioned to disseminate insights from their experience. Fostering understanding of international issues and practices among educators and researchers in the US may enhance their capacity to take an informed, global perspective in their work, which, in turn, may benefit their local communities. Digital media will allow educators and classrooms to make and maintain contact across the world, enabling ICME-14 grantees to maintain connections initiated at the meeting and have an impact on large numbers of school children and teachers, both preservice and practicing, in the US. At ICME-14 these educators will engage in learning about the "state of the art" with respect to research and practice in mathematics education from a wide variety of perspectives and will be able to discuss common challenges in teaching and learning mathematics.

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Looking Back and Looking Forward: Increasing the Impact of Educational Research on Practice

The focus of this conference is to carefully examine past and current research with an eye toward improving its impact on practice and to create concrete steps that could shape the nature and impact of mathematics education research.

Lead Organization(s): 
Award Number: 
1941494
Funding Period: 
Sun, 09/01/2019 to Mon, 08/31/2020
Full Description: 

The focus of the proposed conference is to carefully examine past and current research with an eye toward improving its impact on practice. This conference is designed to create concrete steps that could shape the nature and impact of mathematics education research for years to come. A diverse group of 50 participants will be invited to participate. Participants include 10 experienced K-12 educators whose perspectives will be used to anchor the conference in problems of practice. Other participants represent senior through more junior scholars who have demonstrated a commitment to addressing the disconnect between research and practice, along with technology experts to advise participants on capabilities and innovative uses of modern technologies for instruction, assessment and data management.

The overarching goal for the conference is to help the field of mathematics education think deeply about the most productive ways to answer the following questions: [1] Why hasn't past research had a more direct impact on practice? What can be learned from this historical analysis for future research? [2] What is a possible vision for research that would have a more direct impact on practice? What questions should be asked? What methods should be used? What concrete steps can be taken to launch the new research programs? [3] What are the implications of adopting new kinds of research programs? If they gain traction, how will such changes affect the broader education community and infrastructure, including preservice teacher education, teacher professional development, and the training of future researchers? How should the roles of researchers and teachers change? What incentive structures might motivate these changes? How will new programs of research interact with existing programs?

Improving Evaluations of R&D in STEM Education

The primary goal of this set of workshops is to provide STEM education researchers with the framework, skills, and community they need to implement new developments in causal inference methods into their research.

Project Email: 
Lead Organization(s): 
Award Number: 
1937719
Funding Period: 
Sun, 09/01/2019 to Wed, 08/31/2022
Project Evaluator: 
Full Description: 

The primary goal of this set of workshops is to provide STEM education researchers with the framework, skills, and community they need to implement new developments in causal inference methods into their research. These methods will be immediately implementable in their current (or near future) studies and will result in stronger causal findings, providing higher-quality evidence regarding the potential of new innovations to improve STEM education broadly. Additionally, a secondary goal is to provide the graduate assistants at the workshop (students in statistics) with a strong foundation in the real-world problems facing researchers in STEM education today. By being immersed in this community, the goal is to improve their communication skills, while also providing them with opportunities to develop new methods that address problems facing the STEM education community today.

STEM education research and development studies often focus on the development and iterative refinement of interventions meant to increase STEM participation and skills. Since large-scale randomized experiments are not often possible, researchers typically use correlational methods instead to explore the effects of interventions. Over the past several years, however, statisticians have developed a broad array of methods for understanding causality that do not require these large-scale randomized trials. While these causal inference methods are now common in fields like medicine and education policy, they are much less commonly found in STEM education fields. The purpose of this set of workshops is to introduce STEM education researchers to these methods and how they relate to three research designs they already use: (1) matching on a single variable (e.g., age, gender), (2) pre-test post-test comparisons, and (3) lab experiments. In addition to introducing these new developments, broader discussions of confounding, validity types and trade-offs, design sensitivity, effect size reporting, and questionable research practices (e.g., p-hacking) will also be included.

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