Blacks/African Americans

Incorporating Professional Science Writing into High School STEM Research Projects

The goal of this project is to expand high school student participation in the peer-review process and in publishing in JEI, a science journal dedicated to mentoring pre-college students through peer-reviewed publication. By publishing pre-college research in an open access website, the project will build understanding of how engaging in these activities can change high school students' perceptions and practices of scientific inquiry.

Lead Organization(s): 
Award Number: 
2010333
Funding Period: 
Wed, 07/15/2020 to Fri, 06/30/2023
Project Evaluator: 
Maya Patel
Full Description: 

This exploratory project addresses important challenge of incorporating disciplinary literacy practices in scientific inquiry projects of high school students. The project will incorporate the peer-review process and publication in the Journal of Emerging Investigators (JEI). The Next Generation Science Standards emphasize constructs from disciplinary literacy such as engaging in argument from evidence, and evaluating and communicating information. However, there are few resources available to students and teachers that integrate these constructs in authentic forms that reflect the practices of professional scientists. High school student learners engage in scientific inquiry, but rarely participate in authentic forms of communication, forms that are reflective of how scientists communicate and participate in the primary literature of their fields. The project has three aims: 1) Generate knowledge of the impact of peer-review and publication on perceptions and skills of scientific inquiry and STEM identity, 2) Generate knowledge of how participation in peer-review and publication are impacted by contextual factors (differences in mentors and research contexts), and 3) Develop JEI field-guides across a range of contexts in which students conduct their research.

The goal of the project is to expand high school student participation in the peer-review process and in publishing in JEI, a science journal dedicated to mentoring pre-college students through peer-reviewed publication. By publishing pre-college research in an open access website, the project will build understanding of how engaging in these activities can change high school students' perceptions and practices of scientific inquiry. The project will investigate how participation in peer-reviewed publications will have an impact on student learning by administering a set of pre- and post-surveys to students who submit a paper to JEI. The project will expand student participation in JEI via outreach to teachers in under-resourced and remote areas by delivering virtual and in-person workshops which will serve to demystify peer review and publication, and explore ways to integrate these processes into existing inquiry projects. Other efforts will focus on understanding how student contextual experiences can impact their learning of scientific inquiry. These student experiences include the location of the project (school, home, university lab), the type of mentor they have, and how they became motivated to pursue publication of their research. The project will recruit students from under-resourced schools in New York through a collaboration with MathForAmerica and from rural areas through outreach with STEM coordinators in the Midwest. The resources created will be disseminated directly on the JEI website.

Creating a Model for Sustainable Ambitious Mathematics Programs in High-Need Settings: A Researcher-Practitioner Collaboration

This project will study a successful, ambitious mathematics reform effort in high-needs secondary schools. The goal is to develop resources and tools to support other high-needs schools and districts in transforming and sustaining  their mathematics programs. The model focuses on the resources required for change and the aspects of the organization that support or constrain change in mathematics teaching and learning.

Lead Organization(s): 
Award Number: 
2010111
Funding Period: 
Sat, 08/01/2020 to Wed, 07/31/2024
Full Description: 

A long-standing challenge in secondary mathematics education is broadening participation in STEM. Reform of schools and districts to support this goal can be challenging to sustain. This implementation and improvement project will study a successful, ambitious mathematics reform effort in high-needs secondary schools. The goal is to develop resources and tools to support other high-needs schools and districts in transforming and sustaining  their mathematics programs. The model focuses on the resources required for change and the aspects of the organization that support or constrain change in mathematics teaching and learning. The project team includes school district partners that have successfully transformed mathematics teaching to better support students' learning.

The project will develop a model for understanding the demands and resources from an organizational perspective that support ambitious mathematics teaching and learning reforms. Demands are requirements for physical resources or efforts that need to be met in the instructional system. Resources are the material, human, instructional, and organizational requirements needed to address demands. The project will develop the model through a collaboration of researchers, professional development leaders, students, teachers, coaches, and administrators to: (1) understand the demands created throughout a school or district when implementing an ambitious secondary mathematics program in a high-need context; (2) identify the resources and organizational dynamics necessary to address the demands and thus sustain the program; and (3) articulate a model for a sustainable ambitious secondary mathematics program in high-need settings that has validity across a range of implementation contexts. To develop the model over multiple iterations, the project will examine the demands and resources related to implementing an ambitious mathematics program, the perspectives of stakeholders, the organizational structure, and the program goals and implementation. The project will also conduct a systematic literature review to bring together findings from the successful district and other research findings. The data collection and analysis process will include interviews, document analysis, collection of artifacts, and observations across four phases of the project.  Participants will include students, teachers, instructional support personnel, and administrators (from schools and the district).

Reaching Across the Hallway: An Interdisciplinary Approach to Teaching Computer Science in Rural Schools

This project will develop, test, and refine a "train-the-trainer" professional development model for rural teacher-leaders. The project goal is to design and develop a professional development model that supports teachers integrating culturally relevant computer science skills and practices into their middle school social studies classrooms, thereby broadening rural students' participation in computer science.

Lead Organization(s): 
Award Number: 
2010256
Funding Period: 
Wed, 07/01/2020 to Sun, 06/30/2024
Full Description: 

Strengthening computer science (CS) and computational thinking (CT) education is a national priority with particular attention to increasing the number of teachers prepared to deliver computer science courses. For rural schools, that collectively serve more than 10 million students, it is especially challenging. Rural schools find it difficult to recruit and retain STEM teachers that are prepared to teach computer science and computational thinking. This project will develop, test, and refine a "train-the-trainer" professional development model for rural teacher-leaders. The project will build teachers' self-efficacy to deliver computer science concepts and practices into middle school social studies classrooms. The project is led by CodeVA (a statewide non-profit in Virginia), in partnership with TERC (a STEM-focused national research institution) and the University of South Florida College of Education, and in collaboration with six rural school districts in Virginia. The project goal is to design and develop a professional development model that supports teachers integrating culturally relevant computer science skills and practices into their middle school social studies classrooms, thereby broadening rural students' participation in computer science. The professional development model will be designed and developed around meeting rural teachers, where they are, geographically, economically, and culturally. The model will also be sustainable and will work within the resource constraints of the rural school district. The model will also be built on strategies that will broadly spread CS education while building rural capacity.

The project will use a mixed-methods research approach to understand the model's potential to build capacity for teaching CS in rural schools. The research design is broken down into four distinct phases; planning/development prototyping, piloting and initial dissemination, an efficacy study, and analysis, and dissemination. The project will recruit 45 teacher-leaders and one district-level instructional coach, 6th and 7th-grade teachers, and serve over 1900 6th and 7th-grade students. Participants will be recruited from the rural Virginia school districts of Buchanan, Russell, Charlotte, Halifax, and Northampton. The research question for phase 1 is what is each district's existing practice around computer science education (if any) and social studies education? Phases 2, 3 and 4 research will examine the effectiveness of professional development on teacher leadership and the CS curricular integration. Phase 4 research will examine teacher efficacy to implement the professional development independently, enabling district teachers to integrate CS into their social studies classes. Teacher data sources for each phase include interviews with administrators and teachers, teacher readiness surveys, observations, an examination of artifacts, and CS/CT content interviews. Student data will consist of classroom observation and student attitude surveys. Quantitative and qualitative data will be triangulated to address each set of research questions and provide a reliability check on findings. Qualitative data, such as observations/video, and interview data will be analyzed through codes that represent expected themes and patterns related to teachers' and coaches' experiences. Project results will be communicated through presentations at conferences such as Special Interest Group on Computer Science Education, the Computer Science Teachers Association (CSTA), the National Council for Social Studies (NCSS), and the American Educational Research Association. Lesson plans will be made available on the project website, and links will be provided through publications and newsletters such as the NCSS Middle-Level Learner, NCSS Social Education, CSTA the Voice, the NSF-funded CADREK12 website and the NSF-funded STEM Video Showcase.

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.

Pandemic Learning Loss in U.S. High Schools: A National Examination of Student Experiences

As a result of the COVID-19 pandemic, schools across much of the U.S. have been closed since mid-March of 2020 and many students have been attempting to continue their education away from schools. Student experiences across the country are likely to be highly variable depending on a variety of factors at the individual, home, school, district, and state levels. This project will use two, nationally representative, existing databases of high school students to study their experiences in STEM education during the COVID-19 pandemic.

Lead Organization(s): 
Award Number: 
2030436
Funding Period: 
Fri, 05/15/2020 to Fri, 04/30/2021
Full Description: 

As a result of the COVID-19 pandemic, schools across much of the U.S. have been closed since mid-March of 2020 and many students have been attempting to continue their education away from schools. Student experiences across the country are likely to be highly variable depending on a variety of factors at the individual, home, school, district, and state levels. This project will use two, nationally representative, existing databases of high school students to study their experiences in STEM education during the COVID-19 pandemic. The study intends to ascertain whether students are taking STEM courses in high school, the nature of the changes made to the courses, and their plans for the fall. The researchers will identify the electronic learning platforms in use, and other modifications made to STEM experiences in formal and informal settings. The study is particularly interested in finding patterns of inequities for students in various demographic groups underserved in STEM and who may be most likely to be affected by a hiatus in formal education.

This study will collect data using the AmeriSpeak Teen Panel of approximately 2,000 students aged 13 to 17 and the Infinite Campus Student Information System with a sample of approximately 2.5 million high school students. The data sets allow for relevant comparisons of student experiences prior to and during the COVID-19 pandemic and offer unique perspectives with nationally representative samples of U.S. high school students. New data collection will focus on formal and informal STEM learning opportunities, engagement, STEM course taking, the nature and frequency of instruction, interactions with teachers, interest in STEM, and career aspirations. Weighted data will be analyzed using descriptive statistics and within and between district analysis will be conducted to assess group differences. Estimates of between group pandemic learning loss will be provided with attention to demographic factors.

This RAPID award is made by the DRK-12 program in the Division of Research on Learning. The Discovery Research PreK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics by preK-12 students and teachers, through the research and development of new innovations and approaches. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for the projects.

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

 

 

 

 

Fostering Equitable Groupwork to Promote Conceptual Mathematics Learning

This exploratory study involves a long-term partnership between the principal investigator (PI) and a middle school teacher and her students. Two major goals of the study are to describe how students learn to collaborate with one another over time to make sense of mathematics, and how students and their teacher negotiate what constitutes equitable collaboration, with African American students' perspectives being prioritized. In this way, it adds to this body of literature by: a) prioritizing African American students?

Lead Organization(s): 
Award Number: 
2010172
Funding Period: 
Sat, 08/01/2020 to Mon, 07/31/2023
Full Description: 

When students work in small groups it can promote rich learning opportunities and teach them to collaborate in ways that are important for life and future work. Having students work in small groups, however, can also create opportunities for some students to be marginalized in implicit and explicit ways. Research on using equitable groupwork in which issues of status are consciously addressed by the teacher has shown that such work can have a positive impact on students? opportunities to learn (broadly defined as learning content but also students? developing positive mathematical identities and perspectives on what it means to know/do mathematics). Most of the work on equitable groupwork in mathematics education have had pre-determined definitions of what it means to collaborate. This exploratory study involves a long-term partnership between the principal investigator (PI) and a middle school teacher and her students. Two major goals of the study are to describe how students learn to collaborate with one another over time to make sense of mathematics, and how students and their teacher negotiate what constitutes equitable collaboration, with African American students' perspectives being prioritized. In this way, it adds to this body of literature by: a) prioritizing African American students? perspectives on collaboration from the outset; b) describing, longitudinally, how students learn to collaborate; c) documenting students' mathematics learning within the context of small groups; and d) developing a set of resources for teacher educators, teachers, and students that focus on equitable groupwork.

Using theories and methods from discursive psychology and discourse analysis, the PI of this project will collaborate with a middle grades mathematics teacher to examine equitable groupwork. The small private school enrolls mostly African American students from low income neighborhoods. The PI draws on research related to complex instruction and empirical studies on equitable groupwork and productive student interactions. The basis for the developing definition of equitable collaboration involves gathering information from students about the kinds of relationships and interactions they value, as well as drawing on asset-based and humanizing research related to African American students from mathematics education and education literatures. This information will be used to inform the partnership work as well as be used to analyze the data that will be collected. There are many novel aspects of the work, including, for example, a continual interaction between how students are interacting and the developing idea of ?equitable participation? and practices that might support that kind of participation.

The proposal includes three phases of work with the collaborating teacher to read, plan, reflect, and view videos and research cycles. In the three phases, groupworthy tasks will be developed, the teacher will use these tasks and other important aspects of complex instruction to enact the tasks, and data will be collected focused on these enactments. The work will begin with the 6th grade, then expand into additional years. The data sources will be gathered in the work with the practicing teacher (e.g., recordings of planning and reflection sessions), in the classroom enactments of the groupworthy tasks (e.g., video, audio, fieldnotes, written work), and outside of the classroom teaching time (e.g., interviews with students). The learning of mathematics involves understanding the changes that take place in how students talk about mathematics and how they collaborate over time. The PI will use particular discourse-analytic methods, including thematic analyses from systemic functional linguistics. Such strategies help to focus on the content of the discussions and how people put various ideas in relationship to one another over time. The PI will analyze the small group interactions to develop 12 vignettes that can be used to do focus group interviews with students and later be used in teacher education. These vignettes will include, for example, illustrations of equitable collaborations and variations of issues that come up (e.g, missed opportunities that might keep the interaction from being productive).

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

CAREER: Promoting Equitable and Inclusive STEM Contexts in High School

This project focuses on fostering equitable and inclusive STEM contexts with attention to documenting and reducing adolescents' experiences of harassment, bias, prejudice and stereotyping. This research will contribute to understanding of the current STEM educational climates in high schools and will help to identify factors that promote resilience in the STEM contexts, documenting how K-12 educators can structure their classrooms and schools to foster success of all students in STEM classes.

Award Number: 
1941992
Funding Period: 
Sat, 02/01/2020 to Fri, 01/31/2025
Full Description: 

This project focuses on fostering equitable and inclusive STEM contexts with attention to documenting and reducing adolescents' experiences of harassment, bias, prejudice and stereotyping. An important barrier to persistence in STEM fields for marginalized groups, including women and ethnic minorities, relates to a culture in many STEM organizations, such as academic institutions, that fosters discrimination, harassment and prejudicial treatment of those from underrepresented groups. This research will contribute to understanding of the current STEM educational climates in high schools and will help to identify factors that promote resilience in the STEM contexts, documenting how K-12 educators can structure their classrooms and schools to foster success of all students in STEM classes. Further, this work will explore how to create schools where students stand-up for each other and support each other so that any student who is interested will feel welcome in STEM classes and programs.

This research aims to examine cultures of discrimination and harassment in STEM contexts with attention to: 1) assessing STEM climates in high schools in order to identify the character of discrimination and harassment, 2) understanding how youth think about these instances of bias and discrimination; 3) identifying pathways to resilience for underrepresented youth pursuing STEM interests, and 4) testing an intervention to promote bystander intervention from those who witness discrimination and harassment in STEM contexts. This research will take an intersectional approach recognizing that those who are marginalized by multiple dimensions of their identity may experience STEM contexts differently than those who are marginalized by one dimension of their identity. Because adolescence is a critical developmental period during which youth are forming their attitudes, orientations and lifelong behaviors, this research will attend to issues of bias and discrimination well before individuals enter college STEM classrooms or the STEM workforce: namely, during high school. Further, this work will examine the creation of equitable STEM climates in both college-preparation classes as well as workforce development STEM programs offered though or in partnership with high schools. This research will provide clear evidence to document the current culture of STEM contexts in high schools, using mixed methods, including surveys, qualitative interviews and longitudinal measurement. Further, the project will involve development and implementation of an intervention, which will provide the first test of whether bystander intervention can be fostered in STEM students and will involve training STEM students in key 21st century skills, such as social-cognitive capacities and interpersonal skills, enabling them to speak up and support peers from marginalized backgrounds when they observe discrimination and harassment.

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.

 

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