Blacks/African Americans

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?

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.

Validation of the Equity and Access Rubrics for Mathematics Instruction (VEAR-MI)

The main goal of this project is to validate a set of rubrics that attend to the existence and the quality of instructional practices that support equity and access in mathematics classes. The project team will clarify the relationships between the practices outlined in the rubrics and aspects of teachers' perspectives and knowledge as well as student learning outcomes.

Award Number: 
1908481
Funding Period: 
Mon, 07/15/2019 to Fri, 06/30/2023
Full Description: 

High-quality mathematics instruction remains uncommon and opportunities for students to develop the mathematical understanding are not distributed equally. This is particularly true for students of color and students for whom English is not their first language. While educational research has made progress in identifying practices that are considered high-quality, little attention has been given to specific instructional practices that support historically marginalized groups of students particularly as they participate in more rigorous mathematics. The main goal is to validate a set of rubrics that attend to the existence and the quality of instructional practices that support equity and access in mathematics classes. In addition, the project team will clarify the relationships between the practices outlined in the rubrics and aspects of teachers' perspectives and knowledge as well as student learning outcomes.

This project will make use of two existing large-scale datasets focusing on mathematics teachers to develop rubrics on mathematics instructional quality. The datasets include nearly 3,000 video-recorded mathematics lessons and student achievement records from students in Grades 3 through 8. The four phases of this research and development project include training material development, an observation and rubric generalizability study, a coder reliability study, and structural analysis. Data analysis plans involve case studies, exploratory and confirmatory factor analyses, and cognitive interviews. 

Case Studies of a Suite of Next Generation Science Instructional, Assessment, and Professional Development Materials in Diverse Middle School Settings

This project addresses a gap between vision and implementation of state science standards by designing a coordinated suite of instructional, assessment and teacher professional learning materials that attempt to enact the vision behind the Next Generation Science Standards. The study focuses on using state-of-the-art technology to create an 8-week long, immersive, life science field experience organized around three investigations.

Lead Organization(s): 
Award Number: 
1907944
Funding Period: 
Mon, 07/01/2019 to Fri, 06/30/2023
Full Description: 

New state science standards are ambitious and require important changes to instructional practices, accompanied by a coordinated system of curriculum, assessment, and professional development materials. This project addresses a gap between vision and implementation of such standards by designing a coordinated suite of instructional, assessment and teacher professional learning materials that attempt to enact the vision behind the Next Generation Science Standards. The study focuses on the design of such materials using state-of-the-art technology to create an 8-week long, immersive, life science field experience organized around three investigations. Classes of urban students in two states will collect data on local insect species with the goal of understanding, sharing, and critiquing environmental management solutions. An integrated learning technology system, the Learning Navigator, draws on big data to organize student-gathered data, dialogue, lessons, an assessment information. The Learning Navigator will also amplify the teacher's role in guiding and fostering next generation science learning. This project advances the field through an in-depth exploration of the goals for the standards documents. The study begins to address questions about what works when, where, and for whom in the context of the Next Generation Science Standards.

The project uses a series of case studies to create, test, evaluate and refine the system of instructional, assessment and professional development materials as they are enacted in two distinct urban school settings. It is designed with 330 students and 22 teachers in culturally, racially and linguistically diverse, under-resourced schools in Pennsylvania and California. These schools are located in neighborhoods that are economically challenged and have students who demonstrate patterns of underperformance on state standardized tests. It will document the process of team co-construction of Next Generation Science-fostering instructional materials; develop assessment tasks for an instructional unit that are valid and reliable; and, track the patterns of use of the instructional and assessment materials by teachers. The study will also record if new misconceptions are revealed as students develop Next Generation Science knowledge,  comparing findings across two diverse school locations in two states. Data collection will include: (a) multiple types of data to establish validity and reliability of educational assessments, (b) the design, evaluation and use of a classroom observation protocol to gather information on both frequency and categorical degree of classroom practices that support the vision, and (c) consecutive years of ten individual classroom enactments through case studies analyzed through cross-case analyses. This should lead to stronger and better developed understandings about what constitutes strong Next Generation Science learning and the classroom conditions, instructional materials, assessments and teacher development that foster it.

CAREER: Black Youth Development and Curricular Supports for Robust Identities in Mathematics

This study seeks to describe trajectories that describe the ways in which Black learners develop as particular kinds of mathematical learners. The study takes place in the context of an established, multi-year college bridge program that has as its goals to increase the representation of historically marginalized groups in the university community.

Lead Organization(s): 
Award Number: 
1845841
Funding Period: 
Wed, 05/01/2019 to Tue, 04/30/2024
Full Description: 

Student success in mathematics correlates with positive identities, dispositions, and relationships towards the subject. As mathematics education research strives to understand historic inequities in mathematics for Black learners, small-scale research has described the relationships between identity, subjectivity, and positionality in Black learners as it relates to their achievement and interest in mathematics. This study builds on that descriptive work by seeking to describe trajectories that describe the ways in which Black learners develop as particular kinds of mathematical learners. The study takes place in the context of an established, multi-year college bridge program that has as its goals to increase the representation of historically marginalized groups in the university community. Students in the bridge program from three communities in the greater Detroit area with strong academic achievement in mathematics will be recruited. Their experiences in the bridge program will be traced to identify trajectories that describe the development of Black learners relative to mathematics, and document the design features of classroom activities that support learners in moving through those trajectories.

At the center of the project is the study of cohorts of students in grades 8-11 as they move through the summer bridge program. The bridge project's current curriculum features a series of lessons focused on identity development related to mathematics. These lessons will be implemented, studied, revised, and redeployed across the duration of the project across the summer sessions. Teacher focus groups and surveys will assess the implementation of the activities and aggregate feedback on the design. Three cohorts of students will be recruited to participate in the broader project activities from three metro areas with distinctly different demographic profiles. Student mathematical efficacy will be assessed for all participating students. Within each of the three metro areas, students will be recruited that represent differing levels of mathematics efficacy to ensure that focus students are likely to experience different trajectories through their engagement with the study. The students will be interviewed three times in each academic year to describe their trajectories. Student achievement data will also be collected for all participating students along with narrative descriptions and autobiographies about the messages students receive about mathematics. These messages include their own internal thinking about how they see themselves as mathematics learners, and messages that are sent to them by other students, teachers, and the community. Products of the study will be case studies that describe trajectories of identity development in Black mathematics learners, and a disseminated curriculum for a mathematics identity-focused bridge program supporting Black learners.

Supporting Teachers in Responsive Instruction for Developing Expertise in Science (Collaborative Research: Linn)

This project takes advantage of advanced technologies to support science teachers to rapidly respond to diverse student ideas in their classrooms. Students will use web-based curriculum units to engage with models, simulations, and virtual experiments to write multiple explanations for standards-based science topics. The project will also design planning tools for teachers that will make suggestions relevant research-proven instructional strategies based on the real-time analysis of student responses.

Partner Organization(s): 
Award Number: 
1813713
Funding Period: 
Sat, 09/01/2018 to Wed, 08/31/2022
Full Description: 

Many teachers want to adapt their instruction to meet student learning needs, yet lack the time to regularly assess and analyze students' developing understandings. The Supporting Teachers in Responsive Instruction for Developing Expertise in Science (STRIDES) project takes advantage of advanced technologies to support science teachers to rapidly respond to diverse student ideas in their classrooms. In this project students will use web-based curriculum units to engage with models, simulations, and virtual experiments to write multiple explanations for standards-based science topics. Advanced technologies (including natural language processing) will be used to assess students' written responses and summaries their science understanding in real-time. The project will also design planning tools for teachers that will make suggestions relevant research-proven instructional strategies based on the real-time analysis of student responses. Research will examine how teachers make use of the feedback and suggestions to customize their instruction. Further we will study how these instructional changes help students develop coherent understanding of complex science topics and ability to make sense of models and graphs. The findings will be used to refine the tools that analyze the student essays and generate the summaries; improve the research-based instructional suggestions in the planning tool; and strengthen the online interface for teachers. The tools will be incorporated into open-source, freely available online curriculum units. STRIDES will directly benefit up to 30 teachers and 24,000 students from diverse school settings over four years.

Leveraging advances in natural language processing methods, the project will analyze student written explanations to provide fine-grained summaries to teachers about strengths and weaknesses in student work. Based on the linguistic analysis and logs of student navigation, the project will then provide instructional customizations based on learning science research, and study how teachers use them to improve student progress. Researchers will annually conduct at least 10 design or comparison studies, each involving up to 6 teachers and 300-600 students per year. Insights from this research will be captured in automated scoring algorithms, empirically tested and refined customization activities, and data logging techniques that can be used by other research and curriculum design programs to enable teacher customization.

Supporting Teachers in Responsive Instruction for Developing Expertise in Science (Collaborative Research: Riordan)

This project takes advantage of advanced technologies to support science teachers to rapidly respond to diverse student ideas in their classrooms. Students will use web-based curriculum units to engage with models, simulations, and virtual experiments to write multiple explanations for standards-based science topics. The project will also design planning tools for teachers that will make suggestions relevant research-proven instructional strategies based on the real-time analysis of student responses.

Lead Organization(s): 
Award Number: 
1812660
Funding Period: 
Sat, 09/01/2018 to Wed, 08/31/2022
Full Description: 

Many teachers want to adapt their instruction to meet student learning needs, yet lack the time to regularly assess and analyze students' developing understandings. The Supporting Teachers in Responsive Instruction for Developing Expertise in Science (STRIDES) project takes advantage of advanced technologies to support science teachers to rapidly respond to diverse student ideas in their classrooms. In this project students will use web-based curriculum units to engage with models, simulations, and virtual experiments to write multiple explanations for standards-based science topics. Advanced technologies (including natural language processing) will be used to assess students' written responses and summaries their science understanding in real-time. The project will also design planning tools for teachers that will make suggestions relevant research-proven instructional strategies based on the real-time analysis of student responses. Research will examine how teachers make use of the feedback and suggestions to customize their instruction. Further we will study how these instructional changes help students develop coherent understanding of complex science topics and ability to make sense of models and graphs. The findings will be used to refine the tools that analyze the student essays and generate the summaries; improve the research-based instructional suggestions in the planning tool; and strengthen the online interface for teachers. The tools will be incorporated into open-source, freely available online curriculum units. STRIDES will directly benefit up to 30 teachers and 24,000 students from diverse school settings over four years.

Leveraging advances in natural language processing methods, the project will analyze student written explanations to provide fine-grained summaries to teachers about strengths and weaknesses in student work. Based on the linguistic analysis and logs of student navigation, the project will then provide instructional customizations based on learning science research, and study how teachers use them to improve student progress. Researchers will annually conduct at least 10 design or comparison studies, each involving up to 6 teachers and 300-600 students per year. Insights from this research will be captured in automated scoring algorithms, empirically tested and refined customization activities, and data logging techniques that can be used by other research and curriculum design programs to enable teacher customization.

Engaging High School Students in Computer Science with Co-Creative Learning Companions (Collaborative Research: Magerko)

This research investigates how state-of-the-art creative and pedagogical agents can improve students' learning, attitudes, and engagement with computer science. The project will be conducted in high school classrooms using EarSketch, an online computer science learning environments that engages learners in making music with JavaScript or Python code.

Award Number: 
1814083
Funding Period: 
Sat, 09/15/2018 to Wed, 08/31/2022
Full Description: 
This research investigates how state-of-the-art creative and pedagogical agents can improve students' learning, attitudes, and engagement with computer science. The project will be conducted in high school classrooms using EarSketch, an online computer science learning environments that engages over 160,000 learners worldwide in making music with JavaScript or Python code. The researchers will build the first co-creative learning companion, Cai, that will scaffold students with pedagogical strategies that include making use of learner code to illustrate abstraction and modularity, suggesting new code to scaffold new concepts, providing help and hints, and explaining its decisions. This work will directly address the national need to develop computing literacy as a core STEM skill.
 
The proposed work brings together an experienced interdisciplinary team to investigate the hypothesis that adding a co-creative learning companion to an expressive computer science learning environment will improve students' computer science learning (as measured by code sophistication and concept knowledge), positive attitudes towards computing (self-efficacy and motivation), and engagement (focused attention and involvement during learning). The iterative design and development of the co-creative learning companion will be based on studies of human collaboration in EarSketch classrooms, the findings in the co-creative literature and virtual agents research, and the researchers' observations of EarSketch use in classrooms. This work will address the following research questions: 1) What are the foundational pedagogical moves that a co-creative learning companion for expressive programming should perform?; 2) What educational strategies for a co-creative learning companion most effectively scaffold learning, favorable attitudes toward computing, and engagement?; and 3) In what ways does a co-creative learning companion in EarSketch increase computer science learning, engagement, and positive attitudes toward computer science when deployed within the sociocultural context of a high school classroom? The proposed research has the potential to transform our understanding of how to support student learning in and broaden participation through expressive computing environments.

Methods for Assessing Replication

The goal of this project is to formalize subjective ideas about the important concept of replication, provide statistical analyses for evaluating replication studies, provide properties for evaluating the conclusiveness of replication studies, and provide principles for designing conclusive and efficient programs of replication studies.

Lead Organization(s): 
Award Number: 
1841075
Funding Period: 
Sat, 09/01/2018 to Tue, 08/31/2021
Full Description: 

Replication of prior findings and results is a fundamental feature of science and is part of the logic supporting the claim that science is self-correcting. However, there is little prior research on the methodology for studying replication. Research involving meta-analysis and systematic reviews that summarizes a collection of research studies is more common. However, the question of whether the findings from a set of experimental studies replicate one another has received less attention. There is no clearly defined and widely accepted definition of a successful replication study or statistical literature providing methodological guidelines on how to design single replication studies or a set of replication studies. The research proposed here builds this much needed methodology.

The goal of this project is to formalize subjective ideas about the important concept of replication, provide statistical analyses for evaluating replication studies, provide properties for evaluating the conclusiveness of replication studies, and provide principles for designing conclusive and efficient programs of replication studies. It addresses three fundamental problems. The first is how to define replication: What, precisely, should it mean to say that the results in a collection of studies replicate one another? Second, given a definition of replication, what statistical analyses should be done to decide whether the collection of studies replicate one another and what are the properties of these analyses (e.g., sensitivity or statistical power)? Third, how should one or more replication studies be designed to provide conclusive answers to questions of replication? The project has the potential for impact on a range of empirical sciences by providing statistical tools to evaluate the replicability of experimental findings, assessing the conclusiveness of replication attempts, and developing software to help plan programs of replication studies that can provide conclusive evidence of replicability of scientific findings.

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