Technology

AI-based Assessment in STEM Education Conference

The Framework for K-12 Science Education has set forth an ambitious vision for science learning by integrating disciplinary science ideas, scientific and engineering practices, and crosscutting concepts, so that students could develop competence to meet the STEM challenges of the 21st century. Achieving this vision requires transformation of assessment practices from relying on multiple-choice items to performance-based knowledge-in-use tasks.

Lead Organization(s): 
Award Number: 
2138854
Funding Period: 
Sun, 08/01/2021 to Sun, 07/31/2022
Full Description: 

The Framework for K-12 Science Education has set forth an ambitious vision for science learning by integrating disciplinary science ideas, scientific and engineering practices, and crosscutting concepts, so that students could develop competence to meet the STEM challenges of the 21st century. Achieving this vision requires transformation of assessment practices from relying on multiple-choice items to performance-based knowledge-in-use tasks. Such novel assessment tasks serve the purpose of both engaging students in using knowledge to solve problems and tracking students’ learning progression so that teachers could adjust instruction to meet students’ needs. However, these performance-based constructed-response items often prohibit timely feedback, which, in turn, has hindered science teachers from using these assessments. Artificial Intelligence (AI) has demonstrated great potential to meet this assessment challenge. To tackle this challenge, experts in assessment, AI, and science education will gather for a two-day conference at University of Georgia to generate knowledge of integrating AI in science assessment.

The conference is organized around four themes: (a) AI and Domain Specific Learning Theory; (b) AI and validity theory and assessment design principles; (c) AI and technology integration theory; and (d) AI and pedagogical theory focusing on assessment practices. It allows participants to share theoretical perspectives, empirical findings, as well as research experiences. It can also help identify challenges and future research directions to increase the broad use of AI-based assessments in science education. The conference will be open to other researchers, postdocs, and students via Zoom. It is expected that conference participants establish a network in this emergent area of science assessment. Another outcome of the conference, Applying AI in STEM Assessment, will be published as an edited volume by Harvard Education Press.

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.

Evidence Quality and Reach Hub for the DRK-12 Community

Understanding the impact of STEM education efforts requires researchers to have cutting-edge knowledge of advanced research methods and the ability to translate research knowledge to multiple and diverse stakeholder audiences. The Evidence Quality and Reach (EQR) Hub project will work explicitly to strengthen these two competencies through focused work with the Discovery Research PreK-12 research community.

Award Number: 
2101162
Funding Period: 
Thu, 07/01/2021 to Tue, 12/31/2024
Full Description: 

Understanding the impact of STEM education efforts requires researchers to have cutting-edge knowledge of advanced research methods and the ability to translate research knowledge to multiple and diverse stakeholder audiences. The Evidence Quality and Reach (EQR) Hub project will work explicitly to strengthen these two competencies through focused work with the Discovery Research PreK-12 research community. The hub will develop and implement workshops and learning opportunities for researchers in the community, convene communities of practice to discuss specific research methods, and engage in individualized consultations with DRK-12 projects. These activities are designed to strengthen current and future work in PreK-12 STEM education research.

This project will work at multiple levels to support the DRK-12 research community. Universal activities such as webinars will be developed and deployed to support researchers in learning about new research methods and strategies for translating research for a broad set of stakeholder communities. Collective activities will involve a small number of DRK-12 projects in discussing particular research and dissemination issues common to their work in communities of practice and via virtual workshops. Individual projects will also be offered consultations on their current work. The project will begin with needs-sensing activities that will identify important themes and areas of focus for the universal, collective, and individual work. The project will collect data about the efficacy of their endeavors through surveys, user analytics from online collaboration spaces, and interviews with approximately 10 projects per year.

Using Natural Language Processing to Inform Science Instruction (Collaborative Research: Linn)

This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. The project will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic.

Partner Organization(s): 
Award Number: 
2101669
Funding Period: 
Thu, 07/01/2021 to Mon, 06/30/2025
Full Description: 

Often, middle school science classes do not benefit from participation of underrepresented students because of language and cultural barriers. This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. This work continues a partnership among the University of California, Berkeley, Educational Testing Service, and science teachers and paraprofessionals from six middle schools enrolling students from diverse racial, ethnic, and language groups whose cultural experiences may be neglected in science instruction. The partnership will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic. The partnership leverages a web-based platform to implement adaptive guidance designed by teachers that feature dialog and peer interaction. Further, the platform features teacher tools that can detect when a student needs additional help and alert the teacher. Teachers using the technology will be able to track and respond to individual student ideas, especially from students who would not often participate because of language and cultural barriers.

This project develops AI-based technology to help science teachers increase their impact on student science learning. The technology is aimed to provide accurate analysis of students' initial ideas and adaptive guidance that gets each student started on reconsidering their ideas and pursuing deeper understanding. Current methods in automated scoring primarily focus on detecting incorrect responses on test questions and estimating the overall knowledge level in a student explanation. This project leverages advances in natural language processing (NLP) to identify the specific ideas in student explanations for open-ended science questions. The investigators will conduct a comprehensive research program that pairs new NLP-based AI methods for analyzing student ideas with adaptive guidance that, in combination, will empower students to use their ideas as starting points for improving science understanding. To evaluate the idea detection process, the researchers will conduct studies that investigate the accuracy and impact of idea detection in classrooms. To evaluate the guidance, the researchers will conduct comparison studies that randomly assign students to conditions to identify the most promising adaptive guidance designs for detected ideas. All materials are customizable using open platform authoring tools.

Using Natural Language Processing to Inform Science Instruction (Collaborative Research: Riordan)

This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. The project will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic.

Lead Organization(s): 
Award Number: 
2101670
Funding Period: 
Thu, 07/01/2021 to Mon, 06/30/2025
Full Description: 

Often, middle school science classes do not benefit from participation of underrepresented students because of language and cultural barriers. This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. This work continues a partnership among the University of California, Berkeley, Educational Testing Service, and science teachers and paraprofessionals from six middle schools enrolling students from diverse racial, ethnic, and language groups whose cultural experiences may be neglected in science instruction. The partnership will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic. The partnership leverages a web-based platform to implement adaptive guidance designed by teachers that feature dialog and peer interaction. Further, the platform features teacher tools that can detect when a student needs additional help and alert the teacher. Teachers using the technology will be able to track and respond to individual student ideas, especially from students who would not often participate because of language and cultural barriers.

This project develops AI-based technology to help science teachers increase their impact on student science learning. The technology is aimed to provide accurate analysis of students' initial ideas and adaptive guidance that gets each student started on reconsidering their ideas and pursuing deeper understanding. Current methods in automated scoring primarily focus on detecting incorrect responses on test questions and estimating the overall knowledge level in a student explanation. This project leverages advances in natural language processing (NLP) to identify the specific ideas in student explanations for open-ended science questions. The investigators will conduct a comprehensive research program that pairs new NLP-based AI methods for analyzing student ideas with adaptive guidance that, in combination, will empower students to use their ideas as starting points for improving science understanding. To evaluate the idea detection process, the researchers will conduct studies that investigate the accuracy and impact of idea detection in classrooms. To evaluate the guidance, the researchers will conduct comparison studies that randomly assign students to conditions to identify the most promising adaptive guidance designs for detected ideas. All materials are customizable using open platform authoring tools.

Developing and Evaluating Assessments of Problem-Solving in Computer Adaptive Testing Environments (Collaborative Research: Bostic)

The Common Core State Standards for Mathematics (CCSSM) problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2100988
Funding Period: 
Sun, 08/01/2021 to Fri, 07/31/2026
Full Description: 

Problem solving has been a priority within K-12 mathematics education for over four decades and is reflected throughout the Common Core State Standards for Mathematics (CCSSM) initiative, which have been adopted in some form by 41 states. Broadly defined, problem solving involves the mathematical practices in which students engage as they solve intellectually-challenging mathematical tasks. In prior research, problem-solving measures aligned to CCSSM for grades 3-5 were developed and validated to supplement previously established problem-solving measures in grades 6-8. The problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests. Few measures designed to assess students’ mathematical problem-solving ability use this technology. Shorter tests require less in-class time for assessment than current paper-pencil problem-solving measures and increase classroom instruction time. The computer-adaptive problem-solving measures have sufficient reliability and strong validity evidence, and may limit test-taker fatigue. Finally, the project will benchmark current grades 6-8 instruments using an objective standard-setting method, which allows for improved score interpretations with content-related feedback. Immediate results of student- and class-level reports will be produced through the computer adaptive testing system allowing for teachers to modify instruction to improve students’ learning.

This five-year project aims to advance the use of computer adaptive testing and assessment development for use in mathematics instruction. The project applies an iterative and stakeholder-informed design science-based methodology as well as employs the use of Rasch modeling for the psychometric analysis during item development and validation. The project aims to: (a) benchmark the previously established grades 6-8 problem-solving measures; (b) develop, calibrate, and validate criterion-referenced computer adaptive testing for each measure; (c) construct student- and class-level score reports for integration into the computer adaptive testing system; and (d) investigate teachers’ capacity for implementing, interpreting, and using the assessments and results in STEM learning settings. The project addresses the following set of research questions: (RQ1) What benchmark performance standards define different proficiency levels on problem-solving measures for each grade level? (RQ2) What are the psychometric properties of new problem-solving measures items developed for the computer adaptive testing item bank? (RQ3) Is there significant item drift across student populations on the new problem-solving measure items? (RQ4) To what extent are problem-solving measures item calibrations stable within the computer adaptive testing system? (RQ5) What recommendations for improvements do teachers and students have for the new problem-solving measures items, computer adaptive testing platform and reporting system, if any? (RQ6) To what extent do teachers interact with, perceive, and make sense of the assessment information generated for use in practice? and (RQ7) Does an online learning module build teacher capacity for problem solving measures, computer adaptive testing implementation, interpretation, and use of student assessment outcomes in STEM learning settings? An experimental design will be utilized to investigate teachers’ capacity for implementing, interpreting, and using problem solving measures in a computer adaptive testing system. The project has the potential to impact the field by providing school districts and researchers a means to assess students’ mathematical problem-solving performance at one time or growth over time efficiently and effectively; address future online learning needs; and improve classroom teaching through more precise information about students’ strengths with less class time focused on assessment.

Developing and Evaluating Assessments of Problem-Solving in Computer Adaptive Testing Environments (Collaborative Research: Sondergeld)

The Common Core State Standards for Mathematics (CCSSM) problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests.

Lead Organization(s): 
Partner Organization(s): 
Award Number: 
2101026
Funding Period: 
Sun, 08/01/2021 to Fri, 07/31/2026
Full Description: 

Problem solving has been a priority within K-12 mathematics education for over four decades and is reflected throughout the Common Core State Standards for Mathematics (CCSSM) initiative, which have been adopted in some form by 41 states. Broadly defined, problem solving involves the mathematical practices in which students engage as they solve intellectually-challenging mathematical tasks. In prior research, problem-solving measures aligned to CCSSM for grades 3-5 were developed and validated to supplement previously established problem-solving measures in grades 6-8. The problem-solving measures assess students’ problem-solving performance within the context of CCSSM math content and practices. This project expands the scope of the problem-solving measures use and score interpretation. The project work advances mathematical problem-solving assessments into computer adaptive testing. Computer adaptive testing allows for more precise and efficient targeting of student ability compared to static tests. Few measures designed to assess students’ mathematical problem-solving ability use this technology. Shorter tests require less in-class time for assessment than current paper-pencil problem-solving measures and increase classroom instruction time. The computer-adaptive problem-solving measures have sufficient reliability and strong validity evidence, and may limit test-taker fatigue. Finally, the project will benchmark current grades 6-8 instruments using an objective standard-setting method, which allows for improved score interpretations with content-related feedback. Immediate results of student- and class-level reports will be produced through the computer adaptive testing system allowing for teachers to modify instruction to improve students’ learning.

This five-year project aims to advance the use of computer adaptive testing and assessment development for use in mathematics instruction. The project applies an iterative and stakeholder-informed design science-based methodology as well as employs the use of Rasch modeling for the psychometric analysis during item development and validation. The project aims to: (a) benchmark the previously established grades 6-8 problem-solving measures; (b) develop, calibrate, and validate criterion-referenced computer adaptive testing for each measure; (c) construct student- and class-level score reports for integration into the computer adaptive testing system; and (d) investigate teachers’ capacity for implementing, interpreting, and using the assessments and results in STEM learning settings. The project addresses the following set of research questions: (RQ1) What benchmark performance standards define different proficiency levels on problem-solving measures for each grade level? (RQ2) What are the psychometric properties of new problem-solving measures items developed for the computer adaptive testing item bank? (RQ3) Is there significant item drift across student populations on the new problem-solving measure items? (RQ4) To what extent are problem-solving measures item calibrations stable within the computer adaptive testing system? (RQ5) What recommendations for improvements do teachers and students have for the new problem-solving measures items, computer adaptive testing platform and reporting system, if any? (RQ6) To what extent do teachers interact with, perceive, and make sense of the assessment information generated for use in practice? and (RQ7) Does an online learning module build teacher capacity for problem solving measures, computer adaptive testing implementation, interpretation, and use of student assessment outcomes in STEM learning settings? An experimental design will be utilized to investigate teachers’ capacity for implementing, interpreting, and using problem solving measures in a computer adaptive testing system. The project has the potential to impact the field by providing school districts and researchers a means to assess students’ mathematical problem-solving performance at one time or growth over time efficiently and effectively; address future online learning needs; and improve classroom teaching through more precise information about students’ strengths with less class time focused on assessment.

Learning by Evaluating: Engaging Students in Evaluation as a Pedagogical Strategy to Improve Design Thinking

The Learning by Evaluating (LbE) project will develop, refine, and test an educational innovation in which 9th grade students evaluate sample work as a starting point in engineering design cycles. Students will compare and discuss the quality and fit to context of completed design artifacts. Teachers will collaboratively review and refine the LbE approaches and map the LbE materials into the curriculum.

Lead Organization(s): 
Award Number: 
2101235
Funding Period: 
Sun, 08/01/2021 to Wed, 07/31/2024
Full Description: 

The Learning by Evaluating (LbE) project will develop, refine, and test an educational innovation in which 9th grade students evaluate sample work as a starting point in engineering design cycles. Students will compare and discuss the quality and fit to context of completed design artifacts. Teachers will collaboratively review and refine the LbE approaches and map the LbE materials into the curriculum. Prior work suggests this will allow students to improve understanding of the content, context, and ways of thinking for an assigned project; identify strengths and weaknesses of existing approaches; and recognize key features related to work quality before working on an assignment. The project will work directly with DeKalb County School District in Atlanta, Georgia, and connect to an internationally implemented 9th grade course offered through the International Technology and Engineering Educators Association STEM Center. The pedagogical strategies emerging from this project could be embedded in other STEM Center courses offered in K-12 classrooms internationally, or incorporated by individual teachers in a variety of disciplines through the dissemination of freely available instructional resources.

This three-year exploratory project consists of two years of design-based qualitative research, followed by one year of quasi-experimental mixed-methods research to test the hypothesis that LbE will significantly improve student learning. The theoretical foundation of this inquiry is based on Collins, Brown, & Newman’s “cognitive apprenticeship” approach: students learn from models, articulating knowledge, and reflecting on personal experience. The design phase research questions are: What quality of examples should be used in LbE? How related should examples be to the students’ project? What is the teachers’ role in LbE? What timing provides optimal impact for LbE? The quasi-experiment will randomly assign participating teachers’ class sections to an LbE or a comparison condition, and assess three outcome variables: student design thinking mindset, student critical thinking and reasoning, and student performance. The project leadership team combines design education researchers from Purdue, Brigham Young, and the University of Georgia, the director of the International Technology and Engineering Education Association’s STEM Center, and the Career Technical and Agricultural Education Instructional Coordinator for the DeKalb County School District.

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