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Supported by the National Science Foundation's Improving Undergraduate STEM Education: Hispanic-Serving Institutions (IUSE-HSI) Program, a collaborative summer research internship initiative united a public four-year institution with two local community colleges to offer community college students significant engineering research opportunities and hands-on experiences. In summer 2023, four students from the community college in computer science and engineering participated in a eight-week research internship project in a research lab at the four-year university. This internship project aimed to develop and implement of real-time computer vision on energy-efficient cortex-m microprocessor. This projet explores a unique approach to engage community college students in the realm of artificial intelligence research. By focusing on the development and implementation of real-time computer vision on energy-efficient Cortex-M microprocessors, we offer a practical and educational avenue for students to delve into the burgeoning field of AI. Through a combination of theoretical understanding and practical application, students are empowered to explore AI concepts, gain proficiency in low-power computing, and contribute to real-world AI projects. Furthermore, the project offered student interns a valuable opportunity to refine their research capabilities, particularly in the realms of scientific writing and presentation, while simultaneously boosting their self-assurance and enthusiasm for pursuing STEM careers in the field of AI. 

In this poster, we present our efforts to engage elementary teachers with learning trajectories as a tool for developing both their own and their students’ comprehension of computational thinking (CT) and strategies for integrating CT learning in their classroom. Eleven teachers, who voluntarily joined a teacher professional development (PD) program to develop teacher leaders for CT integration in the elementary context, attended a one-day PD session aimed at reviewing their knowledge of CT, participating in CT-infused lessons, and engaging with CT learning trajectories. Over the next year, teachers will participate in monthly virtual PD to continue to grow both their CT content knowledge and pedagogical knowledge. Our goal is to develop these teachers as teacher leaders who will support others as they integrate CT. This poster will show our current progress on CT learning trajectories and teacher leaders’ responses to the tool. 

In this poster, we present our efforts to engage elementary teachers with learning trajectories as a tool for developing both their own and their students’ comprehension of computational think-ing (CT) and strategies for integrating CT learning in their class-room. Eleven teachers, who voluntarily joined a teacher professional development (PD) program to develop teacher leaders for CT integration in the elementary context, attended a one-day PD session aimed at reviewing their knowledge of CT, participating in CT-infused lessons, and engaging with CT learning trajectories. Over the next year, teachers will participate in monthly virtual PD to continue to grow both their CT content knowledge and pedagogical knowledge. Our goal is to develop these teachers as teacher leaders who will support others as they integrate CT. This poster will show our current progress on CT learning trajectories and teacher leaders’ responses to the tool. 

Smart Structures Technologies (SST) is receiving considerable attention as the demands for high performance in structural systems is increasing in recent years. Although both the academic and industrial worlds are seeking ways to utilize SST, there is a significant gap between engineering science in academia and engineering practice in the industry. To bridge the gap and facilitate the research infusion, San Francisco State University (SFSU) and the University of South Carolina (UofSC) collaborate with industrial partners to establish a Research Experiences for Undergraduates (REU) Site program, which provides undergraduate students a unique opportunity to experience research in both academic and industrial settings through cooperative research projects. In this paper, the development of the program, the two years implementation, as well as the lesson-learned, are discussed. 

The recent development in transportation, such as energy-efficient and autonomous vehicles, defines a condition for the students in transportation engineering. Students in the field of transportation engineering should be ready upon their graduation with new knowledge and skills that are compatible with the need of the industry and sustainable engineering practices. During summers of2018 and 2019, we developed and implemented an eight-week program to increase the knowledge and skills of students coming from multidisciplinary fields related to autonomous vehicles. Problem of “How much will platooning reduce fuel consumption and emissions per vehicle mile traveled?” was instrumentalized in subsequent activities to introduce the comprehensive knowledge structure of autonomous vehicles. The engineering concept of reducing the cost and sustainability was embedded in the leading research question that helped us to develop and implement activities on an overall knowledge structure in autonomous vehicles. The goal of using problem-based learning activities was not to encourage the students to focus on reaching the solution merely. We aimed to introduce the multidisciplinary knowledge and critical skills aspects of learning about disruptive technologies. In this paper, we will discuss how a multidisciplinary research approach was incorporated into a problem-based learning activity. The students were introduced the subjects related to math, physics, computer science, and biology as the integration of the knowledge structure of autonomous vehicles. We will also present the results on students’ use of critical skills such as machine learning and computer programming. 

With increasing demands for high performance in structural systems, Smart Structures Technologies (SST) is receiving considerable attention as it has the potential to transform many fields in engineering, including civil, mechanical, aerospace, and geotechnical engineering. Both the academic and industrial worlds are seeking ways to utilize SST, however, there is a significant gap between the engineering science in academia and engineering practice in the industry. To respond to this challenge, San Francisco State University and the University of South Carolina collaborated with industrial partners to establish a Research Experiences for Undergraduates (REU) Site program, focusing on academia-industry collaborations in SST. This REU program intends to train undergraduate students to serve as the catalysts to facilitate the research infusion between academic and industrial partners. This student-driven joint venture between academia and industry is expected to establish a virtuous circle for knowledge exchange and contribute to advancing fundamental research and implementation of SST. The program features: formal training, workshops, and supplemental activities in the conduct of research in academia and industry; innovative research experience through engagement in projects with scientific and practical merits in both academic and industrial environments; experience in conducting laboratory experiments; and opportunities to present the research outcomes to the broader community at professional settings. This REU program provides engineering undergraduate students with unique research experience in both academic and industrial settings through cooperative research projects. Experiencing research in both worlds is expected to help students transition from a relatively dependent status to an independent status as their competence level increases. The joint efforts among two institutions and industry partners provide the project team with extensive access to valuable resources, such as expertise to offer a wider-range of informative training workshops, advanced equipment, valuable data sets, experienced mentors for the undergraduate researchers, and professional connections, that would facilitate a meaningful REU experience. Recruitment of participants targeted 20 collaborating minority and primarily undergraduate institutions (15 of them are Hispanic-Serving Institutions, HSI) with limited science, technology, engineering, and mathematics (STEM) research capabilities. The model developed through this program may help to exemplify the establishment of a sustainable collaboration model between academia and industry that helps address the nation's need for mature, independent, informed, and globally competitive STEM professionals and could be adapted to other disciplines. In this paper, the details of the first-year program are described. The challenges and lessons-learned on the collaboration between the two participating universities, communications with industrial partners, recruitment of the students, set up of the evaluation plans, and development and implementation of the program are discussed. The preliminary evaluation results and recommendations are also shared. 

With increasing demands for high performance in structural systems, Smart Structures Technologies (SST) is receiving considerable attention as it has the potential to transform many fields in engineering, including civil, mechanical, aerospace, and geotechnical engineering. Both the academic and industrial worlds are seeking ways to utilize SST, however, there is a significant gap between the engineering science in academia and engineering practice in the industry. To respond to this challenge, San Francisco State University and the University of South Carolina collaborated with industrial partners to establish a Research Experiences for Undergraduates (REU) Site program, focusing on academia-industry collaborations in SST. This REU program intends to train undergraduate students to serve as the catalysts to facilitate the research infusion between academic and industrial partners. This student-driven joint venture between academia and industry is expected to establish a virtuous circle for knowledge exchange and contribute to advancing fundamental research and implementation of SST. The program features: formal training, workshops, and supplemental activities in the conduct of research in academia and industry; innovative research experience through engagement in projects with scientific and practical merits in both academic and industrial environments; experience in conducting laboratory experiments; and opportunities to present the research outcomes to the broader community at professional settings. This REU program provides engineering undergraduate students with unique research experience in both academic and industrial settings through cooperative research projects. Experiencing research in both worlds is expected to help students transition from a relatively dependent status to an independent status as their competence level increases. The joint efforts among two institutions and industry partners provide the project team with extensive access to valuable resources, such as expertise to offer a wider-range of informative training workshops, advanced equipment, valuable data sets, experienced undergraduate mentors, and professional connections, that would facilitate a meaningful REU experience. Recruitment of participants targeted 20 collaborating minority and primarily undergraduate institutions (15 of them are Hispanic-Serving Institutions, HSI) with limited science, technology, engineering, and mathematics (STEM) research capabilities. The model developed through this program may help to exemplify the establishment of a sustainable collaboration model between academia and industry that helps address the nation's need for mature, independent, informed, and globally competitive STEM professionals and could be adapted to other disciplines. In this paper, the details of the first-year program will be described. The challenges and lesson-learned on the collaboration between the two participating universities, communications with industrial partners, recruitment of the students, set up of the evaluation plans, and development and implementation of the program will be discussed. The preliminary evaluation results and recommendations will also be shared. 

Abstract This paper presents the development and validation of the 17-item mathematics Graduate Student Instructor Observation Protocol (GSIOP) at two universities. The development of this instrument attended to some unique needs of novice undergraduate mathematics instructors while building on an existing instrument that focused on classroom interactions particularly relevant for students’ development of conceptual understanding, called the Mathematical Classroom Observation Protocol for Practices (MCOP²). Instrument validation involved content input from mathematics education researchers and upper-level mathematics graduate student instructors at two universities, internal consistency analysis, interrater reliability analysis, and structure analyses via scree plot analysis and exploratory factor analysis. A Cronbach-Alpha level of 0.868 illustrated a viable level for internal consistency. Crosstabulation and correlations illustrate high level of interrater reliability for all but one item, and high levels across all subsections. Collaborating a scree plot with the exploratory factor analysis illustrated three critical groupings aligning with the factors from the MCOP²(student engagement and teacher facilitation) while adding a third factor, lesson design practices. Taken collectively, these results indicate that the GSIOP measures the degree to which instructors’ and students’ actions in undergraduate mathematics classrooms align with practices recommended by the Mathematical Association of America (MAA) using a three-factor structure of teacher facilitation, student engagement, and design practices.