Computer Science

Supporting Teacher Understanding of Emergent Computational Thinking in Early Elementary Students

This project explores how to help teachers identify and support early elementary children’s emergent computational thinking. The project will engage researchers, professional development providers, and early elementary teachers (K-2) in a collaborative research and development process to design a scalable professional development experience for grade K-2 teachers.

Lead Organization(s): 
Award Number: 
2101547
Funding Period: 
Wed, 09/01/2021 to Sat, 08/31/2024
Full Description: 

There is an increasing focus and interest in teaching computer science and computational thinking in early elementary school. The project will engage researchers, professional development providers, and early elementary teachers (K-2) in a collaborative research and development process to design a scalable professional development experience for grade K-2 teachers. The project will field test and conduct research on the artifacts, facilitation strategies, and modes of interaction that effectively prepare K-2 teachers to learn about their students’ emergent use of computational thinking strategies. The teachers will collaborate using an online platform for sharing resources, and the project will also study how the online platform can help to reach and support more teachers. The teachers’ learning will be supported by instructional coaches who will help the teachers to integrate computer science into their teaching, and to interpret evidence of their students’ understanding of computational thinking.

The project explores how to help teachers identify and support early elementary children’s emergent computational thinking. The professional learning model for teachers includes a community of practice supported by an online platform and a coach with expertise in computational thinking. The work leverages models for professional development in early grades mathematics. The project focuses on creating systems and conditions for scalable professional learning including coherence, coaching, teacher networks, and engagement with school and district leadership. The research questions are: (1) What kind of professional development and guidance do teachers need to identify and support emergent computational thinking development in young students’ language and work process? (2) What kind of professional development and guidance do teachers need to identify emergent computational thinking development in young students’ work products? (3) How can a scalable professional learning system help teachers understand the development of emergent computational thinking in K-2 students? The teachers will develop lessons, use them with students, and reflect about their work with the coach and the other teachers in their community of practice. The data collection and analysis include interviews, surveys, observations, and documentation from the online platform to understand teachers’ professional learning and development.

Boosting Data Science Teaching and Learning in STEM

This project addresses a critical need to help middle school teachers learn to incorporate data science in their teaching. It uses an open-source platform called the Common Online Data Analysis Platform (CODAP) as a tool for teachers to learn about data science and develop resources for students’ learning. The project team will develop a framework for teachers’ knowledge of data science teaching and learning. Insights from the project will help develop effective practices for teaching data science and understanding how students learn data science.

Lead Organization(s): 
Award Number: 
2101049
Funding Period: 
Thu, 07/01/2021 to Mon, 06/30/2025
Full Description: 

Data fluency is the ability to navigate the world of data. This includes understanding the sources of data, structuring data for analysis, interpreting representations of data, inferring meaning from data, and explaining data and findings to diverse audiences. Data science is becoming more important as a career opportunity and a mechanism for addressing complex phenomena in STEM disciplines. This project addresses a critical need to help middle school teachers learn to incorporate data science in their teaching. It uses an open-source platform called the Common Online Data Analysis Platform (CODAP) as a tool for teachers to learn about data science and develop resources for students’ learning. We will develop a framework for teachers’ knowledge of data science teaching and learning. Insights from the project will help develop effective practices for teaching data science and understanding how students learn data science.

This project will result in two key products: a framework for teacher data fluency and a set of resources for teacher professional learning in data science, including cases of classroom practice that illustrate teaching and learning progressions in data science and surface common student roadblocks, materials for site-based Professional Learning Communities, and professional learning modules that engage teachers in the kind of data-rich learning called for by science education standards and STEM education more broadly. The project will include two stages. During stage one, the project will use a design-based research approach to develop a model of pedagogical content knowledge for data fluency in middle school. Stage one will answer the following questions: (1a) What do teachers need to know and be able to do to support students in becoming data fluent? (1b) What are common student misconceptions and roadblocks in students’ progress to data fluency? (1c) What are the core components of professional learning that boost teachers’ data fluency and their ability to support students becoming data fluent? During stage two, the project will use a mixed methods approach to study the model’s implementation. Stage two will address the following questions: (2a) What impact does professional learning with the core components identified in stage one have on the opportunities to learn teachers provide to their students and on their students’ data fluency? (2b) Are the professional learning innovations usable and feasible for the end users? (2c) In what ways do teachers’ and students’ classroom interactions reflect the model of pedagogical content knowledge developed in stage one? What evidence supports or refutes the hypothesis about the knowledge and skills teachers need to support students’ movement to data fluency?

Accessible Computational Thinking in Elementary Science Classes within and across Culturally and Linguistically Diverse Contexts (Collaborative Research: Nelson)

This research project aims to enhance elementary teacher education in science and computational thinking pedagogy through the use of Culturally Relevant Teaching, i.e. teaching in ways that are relevant to students from different cultural and linguistic backgrounds. The project will support 60 elementary teachers in summer professional development and consistent learning opportunities during the school year to learn about and enact culturally relevant computational thinking into their science instruction.

Lead Organization(s): 
Award Number: 
2101039
Funding Period: 
Sun, 08/15/2021 to Wed, 07/31/2024
Full Description: 

Currently, students who are white, affluent, and identify as male tend to develop a greater interest in and pursuit of science and computing-related careers compared to their Black, Latinx, Native American, and female-identifying peers. Yet, science, computing, and computational thinking drive societal decision-making and problem-solving. The lack of cultural and racial diversity in science and computing-related careers can lead to societal systems and decision-making structures that fail to consider a wide range of perspectives and expertise. Teachers play a critical role in preparing students to develop these skills and succeed in a technological and scientific world. For this reason, it is crucial to investigate how teachers can help culturally and linguistically diverse students develop a greater understanding of and interest in science and computers. This research project aims to enhance elementary teacher education in science and computational thinking pedagogy through the use of Culturally Relevant Teaching, i.e. teaching in ways that are relevant to students from different cultural and linguistic backgrounds. The project will support 60 elementary teachers in summer professional development and consistent learning opportunities during the school year to learn about and enact culturally relevant computational thinking into their science instruction. In doing so, the project aims to increase both the quantity and quality of computing experiences for all elementary students and support NSF’s commitment in broadening participation in the STEM workforce. The project will also produce resources, measures, and tools to support elementary teachers to do this kind of work, which will be shared with other STEM researchers and teacher educators.

The goal of this research project is to design and promote teaching practices that integrate computational thinking in the elementary science classroom in culturally relevant ways. This project will seek to empower practicing elementary teachers’ approaches to meaningfully and effectively integrate and adapt computational thinking into their regular science teaching practice so that all students can access the curriculum. It will also explore the impact of these approaches on student learning and self-efficacy. The scope of this project will include working with multiple highly distinct school settings in Maryland, Arizona, and Washington DC across three years, reaching approximately 60 elementary teachers and 1,200 students. To achieve the project objectives, the research team will leverage concurrent mixed methods approaches that include teacher and student interviews, reflections, observations, descriptive case study reports as well as regression and multilevel modeling. The project’s findings will inform the fields’ understanding of: (a) teachers’ conceptualization of computational thinking; (b) the barriers elementary teachers encounter when trying to integrate computational thinking with culturally relevant teaching practices; (c) the types of support that are effective in teacher professional development experiences  and throughout the school year; and (d) the development of a cohort of teachers that can maintain integration efforts in different districts.

Accessible Computational Thinking in Elementary Science Classes within and across Culturally and Linguistically Diverse Contexts (Collaborative Research: Ketelhut)

This research project aims to enhance elementary teacher education in science and computational thinking pedagogy through the use of Culturally Relevant Teaching, i.e. teaching in ways that are relevant to students from different cultural and linguistic backgrounds. The project will support 60 elementary teachers in summer professional development and consistent learning opportunities during the school year to learn about and enact culturally relevant computational thinking into their science instruction.

Partner Organization(s): 
Award Number: 
2101526
Funding Period: 
Sun, 08/15/2021 to Wed, 07/31/2024
Full Description: 

Currently, students who are white, affluent, and identify as male tend to develop a greater interest in and pursuit of science and computing-related careers compared to their Black, Latinx, Native American, and female-identifying peers. Yet, science, computing, and computational thinking drive societal decision-making and problem-solving. The lack of cultural and racial diversity in science and computing-related careers can lead to societal systems and decision-making structures that fail to consider a wide range of perspectives and expertise. Teachers play a critical role in preparing students to develop these skills and succeed in a technological and scientific world. For this reason, it is crucial to investigate how teachers can help culturally and linguistically diverse students develop a greater understanding of and interest in science and computers. This research project aims to enhance elementary teacher education in science and computational thinking pedagogy through the use of Culturally Relevant Teaching, i.e. teaching in ways that are relevant to students from different cultural and linguistic backgrounds. The project will support 60 elementary teachers in summer professional development and consistent learning opportunities during the school year to learn about and enact culturally relevant computational thinking into their science instruction. In doing so, the project aims to increase both the quantity and quality of computing experiences for all elementary students and support NSF’s commitment in broadening participation in the STEM workforce. The project will also produce resources, measures, and tools to support elementary teachers to do this kind of work, which will be shared with other STEM researchers and teacher educators.

The goal of this research project is to design and promote teaching practices that integrate computational thinking in the elementary science classroom in culturally relevant ways. This project will seek to empower practicing elementary teachers’ approaches to meaningfully and effectively integrate and adapt computational thinking into their regular science teaching practice so that all students can access the curriculum. It will also explore the impact of these approaches on student learning and self-efficacy. The scope of this project will include working with multiple highly distinct school settings in Maryland, Arizona, and Washington DC across three years, reaching approximately 60 elementary teachers and 1,200 students. To achieve the project objectives, the research team will leverage concurrent mixed methods approaches that include teacher and student interviews, reflections, observations, descriptive case study reports as well as regression and multilevel modeling. The project’s findings will inform the fields’ understanding of: (a) teachers’ conceptualization of computational thinking; (b) the barriers elementary teachers encounter when trying to integrate computational thinking with culturally relevant teaching practices; (c) the types of support that are effective in teacher professional development experiences  and throughout the school year; and (d) the development of a cohort of teachers that can maintain integration efforts in different districts.

Education and Experience: Do Teacher Qualifications in Career-Focused STEM Courses Make a Difference?

Using high school statewide longitudinal data from Maryland from 2012-2022, this study will first document who has taught STEM-CTE courses over this period. After exploring the teaching landscape, the study will then explore whether qualifications (i.e., education, credentials, teaching experience) of teachers in STEM-CTE high school courses were associated with their students’ success.

Lead Organization(s): 
Award Number: 
2101163
Funding Period: 
Sun, 08/01/2021 to Mon, 07/31/2023
Full Description: 

When high school students take “STEM-CTE” (i.e., career and technical education courses in science, technology, engineering, and mathematics fields), they have much stronger outcomes across the school-to-college/career pipeline, including lower dropout rates and better attendance in high school, stronger math achievement in 12th grade, and higher odds of pursuing advanced STEM courses in high school and college. Growing teacher research shows that teachers matter for students’ success, particularly in STEM. In particular, research has established that teacher education and credentials in STEM fields, as well as years of classroom teaching experiences are key teacher factors in supporting student outcomes. However, there has been limited prior research regarding (a) who teaches STEM-CTE courses and (b) whether the benefits of these courses and pathways are driven or influenced by specific characteristics of STEM-CTE teachers. This project will aim to explore these questions.

Using high school statewide longitudinal data from Maryland from 2012-2022, this study will first document who has taught STEM-CTE courses over this period. The dataset includes approximately 5,000 unique teacher observations and approximately 500,000 unique student observations. After exploring the teaching landscape, the study will then explore whether qualifications (i.e., education, credentials, teaching experience) of teachers in STEM-CTE high school courses were associated with their students’ success. Indicators of success in the dataset include end-of-course grades, STEM-CTE concentration/industry-recognized credentialing, advanced STEM coursetaking (e.g., honors, AP, IB, dual-enrollment), STEM standardized test scores, math SAT/ACT scores, attendance/suspension rates, on-time graduation, and reduced dropout. Data analysis includes multivariate regression analyses, supplemented with tests for nonrandom sorting of teachers to students.

From Access to Sustainability: Investigating Ways to Foster Sustainable Use of Computational Modeling in K-12 Science Classrooms

This project investigates how to support sustained engagement in computational modeling in middle school classrooms in two ways: 1) Design and develop an accessible modeling toolkit and accompanying thematically linked curricular units; and, 2) Examine how this toolkit and curriculum enable students to become sophisticated modelers and integrate modeling with other scientific practices such as physical experimentation and argumentation.

Award Number: 
2010413
Funding Period: 
Wed, 07/15/2020 to Fri, 06/30/2023
Full Description: 

Modeling is a core scientific activity in which a difficult-to-observe phenomenon is represented, e.g., visually or in a computer program. Research has shown that sustained experience with modeling contributes to sophisticated understanding, learning, and engagement of scientific practices. Computational modeling is a promising way to integrate computation and science learning. Yet computational modeling is not widely adopted in science classrooms over sustained periods of time because of difficulties such as the time required for students to become adept modelers, the need to better integrate computational modeling with other scientific practices, and the need for teachers to experience agency in using these modeling tools. This Design and Development project investigates how to support sustained engagement in computational modeling in middle school classrooms in two ways: 1) Design and develop an accessible modeling toolkit and accompanying thematically linked curricular units; and, 2) Examine how this toolkit and curriculum enable students to become sophisticated modelers and integrate modeling with other scientific practices such as physical experimentation and argumentation. The project will contribute to the conversation around how to support students and teachers to incorporate computational modeling together with valued scientific practices into their classrooms for sustained periods. For three years, the project will work with six sixth and seventh grade teachers and approximately 400 students.

Through iterative cycles of design-based research, the project will design a computational modeling tool and six curricular units for sixth and seventh-grade students. The team will work closely with two teacher co-designers to design and develop each of the six curricular units. The goal is to investigate: 1) How students become sophisticated modelers as they shift from using phenomenon-level primitives to unpacking and modifying these primitives for extended investigations; 2) How classroom norms around computational modeling develop over time. Specifically, how do student models become objects for classroom reflection and how students integrate modeling into other practices such as explanation and argumentation; 3) How data from physical experiments support students in constructing and refining models; and, 4) How sustained engagement supports students' conceptual learning and learning to model using computing tools. The team will collect and analyze video and written data, as well as log files and pre/posttests, to examine how communities of students and teachers adopt computational modeling as an integral practice in science learning. For video and text analysis, the team will use qualitative coding to detect patterns before, during, and after the activities. For the examination of logfiles from the software, the project will use learning analytics techniques such as the classification and clustering of students' sequences of actions. Finally, the team will also conduct pre/post-tests on both content and meta-modeling skills, analyzing the results with standard statistical tests.

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.

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.


 Project Videos

2021 STEM for All Video Showcase

Title: Integrating Computer Science and Social Studies in MS

Presenter(s): Bryan Wallace, Debra Bernstein, & Michael Berson


Internet of Things Pedagogical Ecosystem for Integrated Computer Science and Software Engineering Education for Grades 9-12

This project aims to develop, implement, and evaluate an Internet of Things (IoT) based educational curriculum and technology that provides grades 9-12 students with Computer Science (CS) and Software Engineering (SE) education.

Award Number: 
2010259
Funding Period: 
Wed, 07/01/2020 to Fri, 06/30/2023
Full Description: 

The Internet of Things (IoT) technology connects physical devices such as industrial machines, vehicles, kitchen appliances, medical devices to the internet to enable users, businesses, and computers to make data driven decisions. The IoT is a rapidly improving, transformative field that is bound to positively impact global job markets and industries. With continued advancement in science and technology comes the need to educate K-12 students in emerging technologies to better prepare them for future academic and professional pursuits. This project aims to develop, implement, and evaluate an Internet of Things (IoT) based educational curriculum and technology that provides grades 9-12 students with Computer Science (CS) and Software Engineering (SE) education. At its core, the IoT technology uses inexpensive microcomputers that run code to collect, analyze, and share data with other devices or users. Due to this inherent integration between hardware and software, IoT has the potential to serve as an excellent platform for teaching CS and SE to high school students. Grades 9-12 teachers and students from diverse and varied socioeconomic backgrounds will participate in this curricular experience through their STEM/CS/Engineering classes. Broad dissemination through online platforms, summer camps, and museums will be used to share content, testimonials, teaching strategies, and best practices to a wide audience in the K-12 education community. Over three hundred high school students and 27 teachers will be engaged directly in-class or via outreach activities. The skills and knowledge gained as part of this curricular experience will provide strong college and career readiness to high school students.

The proposed IoT pedagogical ecosystem features an innovative approach to bringing CS and SE education to grades 9-12 by immersing students in the technical challenges of building web-connected physical computing systems. This project will focus on identifying critical elements for effective instructional design for CS and SE education by understanding student and teacher motivation. A key innovation of this effort will be the low-cost, IoT-hardware kits for project-based learning to create a hands-on experience in the classroom. The curriculum will involve real-world projects inspired by the National Academy of Engineering grand challenges that have direct applications in the industry (e.g., urban infrastructure, wearable technology, connected vehicles, connected health, and cybersecurity). Continuous and methodical assessment via rubrics and focus groups will enable data collection on students' CS/SE/IoT knowledge and skills, teamwork skills, and overall engagement. Rigorous quantitative statistical analysis (parametric and non-parametric) and qualitative methods (first cycle and second cycle coding) will be used to answer three research questions: 1) What is the impact of IoT-based projects on students' CS, SE, and hardware skills and knowledge? 2)What is the effect of IoT-based projects on students' engagement and teamwork skills? 3) What factors of instructional design promote/hinder engagement? This project will cumulatively provide an evidence-based understanding of how effective IoT is as a means to provide high school students with critical and modern CS/SE skills and knowledge.

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