Purdue University established Freshman Engineering (now known as First-Year Engineering) in 1953, the first program in the U.S. to do so. Over the years, First-Year Engineering (FYE) programs have been established at several institutions, but not all, across the country. In the early 1990s, the National Science Foundation (NSF) provided funding for what were called the Engineering Education Coalitions. They funded a total of eight coalitions that involved more than 40 institutions of higher education over the period from 1990-2005. In addition, NSF created the Action Agenda program in the late 1990s aimed at individual institutions that wanted to adapt and adopt the findings from the existing Coalitions. A strong focus of the Coalitions was on introductory engineering courses, with the rationale that engineering was losing too many students through attrition, and we needed to pay more attention to the formative years. Nearly every Coalition created some version of an FYE program through this funding mechanism. The number of FYE programs across the nation has increased dramatically based on these investments, largely in response to curricular efforts aimed at retaining engineering students by providing them with meaningful career-oriented experiences early in their college educations.

Many of these first-year programs were called “common first-year engineering programs,” meaning that all students enrolled in the same courses at the same time. It is a one-size-fits-all, cookie-cutter approach to education. Despite the laudable goals espoused by most FYE programs, there has been an unintended consequence: curricular rigidity and inflexibility. Thus, students have little agency to shape their own pathway toward an engineering degree. Recently, a midwestern R1 institution obtained a grant from the NSF to develop the next generation of first-year programs: FYE2.0. The program we envision will provide students with essential skills, while at the same time providing them with opportunities for customization and flexibility in charting their own engineering journey. This paper outlines the logistical progress made in implementing FYE2.0 to date and discusses plans for the future.

Authored by
Dr. Sheryl A. Sorby (University of Cincinnati), Dr. Muhammad Asghar P.E. (University of Cincinnati), Dr. Gregory Warren Bucks (University of Cincinnati), Jeremy Michael Olivar Hill (University of Cincinnati), Dr. Jeff Kastner (University of Cincinnati), Prof. Teri J Murphy (University of Cincinnati), and Dr. David Reeping (University of Cincinnati)

We are facing a wide range of grand challenges across the world, such as the continuous increase of energy demand, more frequent extreme weather disasters, and uncertainties associated with climate change. Wind energy, an affordable renewable resource—achieving costs as low as $0.04 per kilowatt-hour in a growing number of regions (Gielen et al., 2019; Wiser et al., 2008)—holds great potential to meet rising energy demands, mitigate the causes of climate change, and contribute to a cleaner, sustainable, and domestic energy generation portfolio. To fully realize the potential of wind energy, we must address research challenges arising from the complex, coupled phenomena that span spatial and temporal scales relevant to both wind energy and the broader power system. This necessitates a diverse, talented pool of future scientists and engineers in wind energy research and industries to tackle these issues. Despite the national and global demand for wind energy research and development (Veers et al., 2019), many undergraduate students of non-R1 universities lack opportunities to participate in active wind energy research. To tap the nation’s diverse student talent pool and broaden participation in science and engineering, there is a critical need to provide these students access to wind energy research early in their career and motivate them to pursue graduate education or research-oriented jobs in the wind energy sector.

The Great Lakes Wind Energy Challenges (GLWind) REU site funded by the NSF Division of Engineering Education and Centers (EEC), co-hosted by CSU and CWRU in Cleveland, Ohio, has supported nine undergraduate students in a ten-week intensive summer research program during 2023 and 2024 (Years 1 and 2). The REU student cohort actively engaged in ongoing wind energy research within existing learning and research communities at CSU and CWRU. They attended weekly seminars on the latest wind energy research and professional development, visited utility-scale wind turbines on the CWRU campus, and presented their research posters at the Summer Intersection Symposium and professional conferences beyond the program. REU students had enjoyed the rich diverse culture and historical landmarks of Cleveland. Annual program evaluations via surveys and focus group interviews were carried out to inform PIs and faculty about potential improvements and ensure continuous program enhancement. The GLWind REU program focused on recruiting women, underrepresented minorities, and students from community colleges, liberal arts colleges, and non-R1 universities without established research programs in engineering. In the summer 2023, 56% of participants were from non-R1 universities, and 67% belonged to underrepresented minority groups (UMG) in STEM. In the summer 2024, 67% of participants were from non-R1 universities, and 44% were UMG. The educational outcome aims at a diverse group of talented US students who are motivated and prepared to apply the research and communication skills developed in the REU program to succeed in broader STEM fields, and a structured, personalized mentoring mechanism for undergraduate students that will be disseminated to a larger engineering community via the ASEE conference.

Authored by
Dr. Wei Zhang (Texas Tech University) and Prof. Xiong Yu (Affiliation unknown)

This International Research Experience for Students (IRES) NSF project (IRES: Artificial Intelligence and Data Science for the Understanding, Prediction and Prevention of Disease (AI-UPP)) focuses on creating an immersive international summer research experience in Stockholm for students enrolled in a primarily undergraduate institution (PUI). Over the course of a three-year grant period, this IRES site seeks to: (1) develop and disseminate artificial intelligence (AI) and data science tools aimed at understanding, predicting and preventing disease; (2) train and mentor 24 diverse undergraduate students from PUIs in AI and/or data science research in a collaborative and international setting; (3) encourage and prepare undergraduate students from PUIs for enrollment in graduate programs in computer science, artificial intelligence, bioengineering, bioinformatics, or related fields; (4) foster existing collaborations and develop new research collaborations between [Blinded] and scientists in Sweden; and (5) develop a diverse cohort of globally engaged scientists/engineers that seek career opportunities and collaborators throughout the world.

This program represents a new international research experience for undergraduates who attend a PUI in the United States. Each year, eight undergraduate students enrolled in bioengineering, biomedical engineering, computer science, bioinformatics and/or computational/mathematical biology programs will be sent to Stockholm for 10 weeks to work with one of seven host labs at [Blinded]. The students will work on projects which have specifically been co-designed by host lab researchers and the program director to provide students with a structured research project at the appropriate academic level which also meaningfully contributes to the host lab’s research program. These projects will all center around a common theme of developing AI and data science tools for the analysis of large biological datasets to aid in the understanding, prediction and prevention of disease. In addition, the students will participate in pre-departure research training, pre-departure cultural training, professional development workshops, a visit to the European Centre for Disease Prevention and Control (ECDC), an open forum on the use of AI in health, cultural outings in Stockholm, a final research presentation symposium, a career readiness workshop series, and be able to present their work at the Biomedical Engineering Society (BMES) annual meeting. This paper reports on the first year of the grant, program structure and discusses the efforts by the PI to properly prepare students to conduct their summer research project.

Authored by
Prof. Mark A Chapman (University of San Diego)

Providing equitable, meaningful, and ambitious STEM learning opportunities for all students requires an extremely high level of professional skill and judgment from teachers (e.g., Educate to Innovate, 2016; National Research Council, 2012; U.S. Department of Education, 2022). Broader systemic issues of educational inequity disproportionately exclude students from minoritized backgrounds from high-quality STEM learning environments (e.g., Calabrese Barton, 2003; Calabrese Barton & Yang, 2000; Carter, 2016; National Research Council, 2012; Windschitl & Calabrese Barton, 2016), making recruitment and retention of STEM teachers trained in socially just teaching practices critical.

In this paper, we present findings out of our Improving Undergraduate STEM Education (IUSE) funded research effort called “Improving STEM undergraduate teacher education and developing the STEM profession through institutional transformation.” We present the outcomes and ongoing development effort on a re-envisioning of STEM teaching and learning that takes place within a larger partnership effort—a cradle-to-career learning campus, part of a large urban school district, serving primarily Black and Brown-identifying children and youth from preschool through grade 12. The vision of the learning campus “Leaders Designing Change,” which emphasizes the two focus areas of human-centered design and Engineering and social engagement for justice. Further, this campus houses a Teaching School, which, like a Teaching Hospital, hosts an intergenerational teaming structure from preservice educator development through residency for certified teachers in the first three years of teaching that seeks to extend the time that university partners can continue to educate and support early career teachers to enact ambitious instruction.

This design and research effort seeks to create and refine the Teaching School model to offer a proof-of-concept for the multi-institutional transformation project. Our findings provide insight into the development of a translatable model of STEM teacher development and pathways and how this may support STEM learning along historically underrepresented students. Our findings describe how project- and place-based learning in STEM, and specifically engineering learning, increased educational and standardized testing outcomes for youth across domains, how STEM teachers engage in curriculum co-development, and how our team (of university collaborators and K-12 teachers) supported enactments of teaching through the Teaching School model. We present data on the ways that intergenerational and extended teaming supported STEM teachers, and specifically engineering teachers, within this high school context. We describe the human-centered design and engineering curriculum developed through this collaboration. This curriculum and STEM teaching showed increases in high school students’ literacy, mathematics, and science learning outcomes on standardized tests. The data also suggest that students are developing a sense of belonging in STEM and seeing opportunities to take action as powerful actors in their communities and the world.

Authored by
Dr. Bridget L. Maher (University of Michigan Marsal School of Education)

There is a growing need to equip engineers with sociotechnical skills that integrate technical expertise with an understanding of social and contextual factors to effectively address design problems. Although ABET and national reports highlight the importance of accounting for social and contextual factors, these skills are often underemphasized in engineering curricula. The Social Engagement Toolkit (SET) was developed to provide students with the skills needed to identify and address social and contextual aspects of engineering work. The SET includes instructional materials on a wide range of topics and the content is grounded in research that incorporates recommended socially engaged engineering design practices and strategies. This work has received funding from the NSF Improving Undergraduate STEM Education program.

This study implemented several SET modules in a two-semester mechanical engineering senior design course and examined how these modules influenced students’ capstone design projects. Several modules including those focused on design requirements and specifications, design interviewing, concept generation and development, and concept selection and prototyping were used to provide instruction. To understand how these SET modules informed students’ project work, we drew on student reflection assignments collected throughout the semester. These reflections asked students to identify how each module influenced their design projects. Students described that the design requirements and specifications module helped them have guidelines for their design projects and supported communication with stakeholders. The design interviewing module provided guidance on developing structured conversations with stakeholders for information gathering and building rapport with stakeholders. The concept generation and development module aided them in considering diverse concepts that can be shared with stakeholders and synthesizing multiple concepts into their final design. The concept selection and prototyping module supported design iterations with their stakeholders. Using multiple SET modules to complement the learning objectives in a senior capstone design course provided valuable support for mechanical engineering students working on their projects to leverage recommended design approaches and incorporate sociotechnical thinking

Authored by
Dr. Jin Woo Lee (California State University, Fullerton), Carlos Gunera (California State University, Fullerton), Dr. Erika Mosyjowski (University of Michigan), and Dr. Shanna R. Daly (University of Michigan)

The ASEE Faculty Teaching Excellence Task Force has just completed its NSF IUSE ICT Capacity Building grant and started its next NSF grant (IUSE ICT Level 1). Both grants support the building of a recognition framework for engineering and engineering technology (EET) faculty in the US for their efforts and achievements in professional development (PD) as it relates to teaching.

Various arguments can and have been made about the need for EET faculty to receive training in how to teach. Such arguments are more than a century old and have continued to more recent calls. Indeed, the changing nature of the job responsibilities of a professor requires skill set (e.g., identifying mental health challenges of students) that were not considered in previous generations.

To promote the recognition of EET faculty PD in teaching, ASEE charged a task force with developing a plan to achieve such an outcome. The first step in this plan was to engage various diverse ASEE constituencies on the value of such a recognition system, as well as barriers to implementation. Such efforts were supported by a grant from the NSF IUSE ICT Capacity Building program. One of the key outcomes from this work, which was completed in August 2024, was a three-level recognition framework: (1) Registered Engineering Educator, (2) Certified Engineering Educator, and (3) Leading Engineering Education.

The first level, Registered Engineering Educator, is focused on the acquisition (through training) of key competencies. Such draft competencies were identified through literature investigation, focus groups, and surveys. The second level, Certified Engineering Educator, is focused on the implementation of some of the competencies learned during the previous level. The third level, Leading Engineering Educator, looks to recognize individuals who are improving engineering education outside of their own classroom.

The next stage of the larger project, which was recently funded via an NSF IUSE ICT Level 1 grant, will pilot the Registered Engineering Educator level at eight diverse partner institutions, aided by 24 additional partner institutions that serve as an Evaluation Team. Centers for Teaching and Learning at the partner institutions, plus nationally recognized content providers (e.g., NETI), will provide most of the content as EET faculty at the pilot institutions work towards achieving the requirements of the Registered Engineering Educator level.

Research for this grant will begin with how individuals are recruited to participate in faculty development programs and how they make their PD selections. Additionally, we explore items associated with value, access, and barriers to PD (both perceived and actual) for faculty, plus how this might vary by institution type. Finally, we explore how conversations and practices might change as a result of participation in PD.

Authored by
Dr. Donald P. Visco Jr. (The University of Akron), Dr. Jenna P. Carpenter (Campbell University), Dr. Elizabeth Litzler (University of Washington), Dr. Douglas Bohl (Clarkson University), Dr. Charles Henderson (Western Michigan University ), Dr. Alan Cheville (Bucknell University), and Dr. Rae Jing Han (University of Washington)

This paper describes the experiences and outcomes of undergraduates enrolled in the new Women in Science & Engineering (WISE) Honors’ curriculum, supported by NSF DUE #2012339. Employers consistently rate communication skills and teamwork as critical in the candidate selection process (NACE, 2023) and look for problem solving skills and group projects on resumes (Grey, 2024). To address these needs, we focus on two courses designed to prepare future leaders of the STEM workforce: Service Learning in STEM and Women’s Leadership in STEM. These courses were developed through a unique partnership involving the WISE Program (academic affairs), and Career Center (student affairs), that connects students with real world projects through the Career Center’s community partnerships, and with industry leaders through the Industry Connections Program. Class sizes are small, with 20-35 students per section, to ensure that students have every opportunity to have a voice in class, and be open about their overall experience as women in STEM. Research has shown that such [science] meritocracy myths cause declines in self-esteem, increases in dropout rates, and substantial psycho-logical costs, such as self-blame and imposter syndrome” (Graves et al., 2022, p.5).

In addition to continuous feedback loops in classes, formal assessment was collected through course surveys and focus groups each semester, leading to adjustments to course readings, assignments, and in-class activities. Being in the fourth year of the program, we have integrated lessons learned from the students’ experiences and honed two highly valued courses which provide: active engagement with content, solutions-oriented class discussions, practice having difficult conversations, vision board design, connections to industry and alumni mentors, deconstruction of their experiences working with community partners and/or student organization leadership roles, and significant self reflection. Strategies to overcome discriminatory or biased behavior, a framework for having difficult conversations, language to help them articulate their leadership vision, philosophy, and skills, and a support network of peers and alumni mentors from industry equips students with the skills and confidence they need to thrive in their future career as STEM leaders.

A limitation of the project is the challenge of gathering post-graduate outcomes. To address this, long-term plans include implementation of more targeted alumni outreach strategies, maintenance of updated contact information, leveraging social media, and collaborations with alumni career services. We will also explore existing partnerships with industry and use surveys to collect long-term career data from alumni.

References:

Georgetown University Center on Education and the Workforce, Workplace basics: The competencies employers want, 2020. https://cew.georgetown.edu/cew-reports/competencies/

Graves Jr, J. L., Kearney, M., Barabino, G., & Malcom, S. (2022). Inequality in science and the case for a new agenda. Proceedings of the National Academy of Sciences, 119(10), e2117831119.

Gray, K. (2024, January 16). The key attributes employers are looking for on graduates’ resumes.
https://www.naceweb.org/talent-acquisition/candidate-selection/the-key-attributes-employers-are-looking-for-on-graduates-resumes#:~:text=Beyond%20these%20attributes%2C%20it%20is,candidates%20for%20a%20job%20opening.

NACE (2023, November). Job Outlook. Bethlehem, PA.

Authored by
Dr. Marianna Savoca (Stony Brook University), Dr. Monica Bugallo (Stony Brook University), Diana Voss (Stony Brook University), and Urszula Zalewski (Stony Brook University)

Need: Attrition is a significant issue for STEM undergraduate majors: on average 49% of students transfer to another major or leave college completely by their 8th year of study, with even greater rates for STEM majors who are under-represented minorities or women. Barring financial barriers to retention, the most significant drivers of attrition are reported to be difficulty in adjusting to academic and life needs and resolving educational and occupational goals, and feelings of isolation. We posit that the former impediments are closely related to ineffective Self-Regulation of Learning (SRL), since SRL addresses an individual’s behaviors and strategies as an independent and reflective learner, and their motivation to sustain effort when challenged. We posit that the latter impediment is closely related to a lack of sense of belonging (SOB), since SOB addresses an individual’s cognition, affects, and behavior around their perceived legitimacy as a member of a community who is included, involved, valued, and accepted. Further, it documented that many students enter college with ineffective SRL, and that under-represented students like minorities and females have fewer relatable peers and so are more at risk of having a low sense of belonging in college.
So, can retention be improved by systematically training students in effective SRL strategies?

This NSF IUSE project draws upon published research of educational psychology social-cognitive frameworks around SOB (Strayhorn, 2019) and SRL (Zimmerman, 2000 and 2002), and the findings of a prior NSF-funded study and a pilot study, to uniquely develop and refine an intervention that synergistically interweaves the learning of STEM topics with developing effective SRL and building SOB.

Project: This 3-year IUSE:HER Level 1 project is completing its first year. In the first year, 80 sophomore civil engineering students received training in SRL to improve their metacognitive knowledge, awareness, and experience, and develop personalized and adaptable strategies for building effective SRL. Key findings include student perception of the importance and helpfulness of the intervention, and statistics regarding uptake of SRL, SRL effectiveness, SOB, and performance in major courses taken alongside the intervention.

Broader impacts: This project creatively incorporates evidence-based advances in educational psychology and education into undergraduate STEM education and lays the groundwork for significant institutional improvement in associates and baccalaureate STEM programs by offering a replicable, transferable, and adaptable design.

National Academies of Sciences, Engineering, and Medicine. (2016). Barriers and Opportunities for 2-Year and 4-Year STEM Degrees (S. Malcom & M. Feder, Eds.). National Academies Press. http://dx.doi.org/10.17226/21739
National Academies of Sciences, Engineering, and Medicine. (2021). Addressing Diversity, Equity, Inclusion, and Anti-Racism in 21st Century STEMM Organizations (L. Scherer, Ed.). National Academies Press. http://dx.doi.org/10.17226/26294
Strayhorn, T. L. (2019). College Students' Sense of Belonging: A Key to Educational Success for All Students (2nd ed.). Routledge: New York, NY.
Zimmerman, B. J. (2000). Self-Efficacy: An Essential Motive to Learn. Contemporary Educational Psychology, 1, 82–91. https://doi.org/10.1006/ceps.1999.1016
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 42(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2.

Authored by
Dr. Ann (Beth) Wittig (City College of New York at City University of New York (CUNY) )

The capacity-building project builds and strengthens faculty communities and develops a culture of inquiry and conversations that are evidence-based and data-informed – with the goal of creating readiness for transformation. It is funded by the NSF Improving Undergraduate STEM Education (IUSE: EDU) program, Institutional and Community Transformation (ICT) track.

This capacity-building project is designed to motivates faculty to consider evidence-based teaching strategies as they collaboratively explore questions on student learning and success in introductory and foundational undergraduate STEM courses, including early engineering courses and its prerequisite math and science courses. The change framework for intentional capacity building by creating faculty communities and course-level data dashboards to inform changes in instructional practices and curriculum is described in Chan Hilton (2024) and Chan Hilton and Blunt (2022). This project explores engineering education change in the context of a public, regional, primarily undergraduate institution (PUI) in the Midwest.

The project provides opportunities for faculty engagement at multiple “doses”, including semester-long faculty learning communities and working groups, annual workshops, and mini-activities. This paper and poster summarize the mini-activities that were developed and implemented each semester. Each mini-activity is a brief (about 15 minutes in duration), interactive activity in which a brief scenario is used as the context for small group discussions using provided prompts. The purpose of the mini-activities is introduce instructional strategies and facilitate discussion and sharing and generate interest in the faculty communities. One example mini-activities was exploring how aggregate data for students enrolled in an introductory course inform teaching considerations and strategies. Another example is reviewing sample syllabus language to identify opportunities support inclusive and growth mindset learning environments. The mini-activities were implemented at the college-wide semester kick-off meeting each semester and have been institutionalized as a result of this project.

Authored by
Dr. Amy B Chan Hilton (University of Southern Indiana), Shelly B. Blunt (University of Southern Indiana), and William Elliott (University of Southern Indiana)

The paper overviews a new IUSE:EDU project to develop a social network analysis (SNA) instrument that will allow STEM education centers to assess the otherwise intangible concept of STEM education capacity. STEM education capacity refers to the ability and empowerment of STEM educators to adapt to changing needs and collectively achieve shared objectives of their organizations. STEM education capacity is an important property of any academic system in STEM disciplines. It characterizes the readiness of the system’s members, communities, and the organization as a whole to adapt educational practices effectively to changing circumstances. However, it is also a latent system property, meaning that STEM education capacity can only be observed when it is in action. Most commonly, academic units see capacity in action during times of crisis like the COVID-19 pandemic. In such times of crisis, it is too late to intervene and develop capacity to more effectively deal with the crisis. We argue that STEM education capacity can be more proactively be observed in mundane interactions between peers. SNA is a promising tool to be able to capture and quantify these interactions, allowing STEM education leaders to anticipate capacity development opportunities to better prepare for times of crisis or change.

The project is in its first phase of three, in which we use qualitative interviewing to identify the kinds of relationships and interactions that matter to STEM education capacity building. We interviewed fifteen engineering faculty and staff involved in the teaching and learning process in one university’s College of Engineering. We aimed to understand who they talked to about teaching and learning, how their relationships developed, and what kinds of conversation they have most often. These interviews helped us deduce how engineering educators grow, learn, change, and help others through their interactions with other educators. The results of this phase of research yielded important insights about the ways networks of educators grow and solidify in STEM higher education. They also revealed the kinds of interactions relevant to individual growth and systemic capacity building.

The next project phase will develop a SNA instrument that can capture the kinds of interactions relevant to STEM education capacity building. The final project phase will validate the instrument via its deployment across the entire College of Engineering. At the end of the project, STEM education leaders will have a tool they can use to assess, study, and grow STEM education capacity in their contexts. We call this tool the CATENA Instrument (Capacity Assessment, Tracking, & Enhancement through Network Analysis). This paper and NSF grantees poster will introduce the project as a whole, and also describe our Phase 1 results.

Authored by
Dr. John Ray Morelock (University of Georgia) and VARUN KATHPALIA (University of Georgia)

This study explores how arts-based practices—speculative design, remixing, and futurisms—can deepen faculty and institutional leaders’ engagement in diversity, equity, inclusion, and justice change efforts through our GATHER Community of Transformation. Amid escalating political attacks on DEI initiatives and the unlawful termination of our NSF grant, we demonstrate how creative methodologies made complex theoretical frameworks accessible and actionable. Findings show that arts-based activities fostered critical reflection, strengthened relational support, and empowered participants to envision transformative futures. We offer a replicable model for sustaining inclusive institutional change even under conditions of political suppression.

Authored by
Kristen Ferris (University of New Mexico), Dr. Susannah C. Davis (University of New Mexico), Dr. Vanessa Svihla (University of New Mexico), Dr. Earl E Lee (Arizona State University), Katharine Getz (Pennsylvania State University), Cinthia Viviana Rojas Palacio (Arizona State University), and Dr. Nadia N. Kellam (Arizona State University)

Background
Soft robotics is a novel and emerging field of robotics. Unlike traditional robot mechanics, soft robotics uses flexible, compliant materials instead of rigid parts and linkages. This soft aspect of the robots has led to their increasing use in organic applications such as biomimicry, orthotic devices, and surgical robots. This field has seen rapid growth recently, which is only projected to continue as the field expands into new engineering settings. However, this rapid growth has led to a lack of up-to-date, accessible educational material surrounding soft robotics.

Overview
Our project, funded by the National Science Foundation Improving Undergraduate STEM Education program, takes place in the context of a modified version of capstone design experiences called engineering clinics at our institution. Clinics are offered to third- and fourth-year students, where they engage in multi-disciplinary, team-based projects in collaboration with faculty. In the soft robotics clinic, students are tasked with identifying and designing new soft robotics prototypes, developing modules that support student learning in connection with their prototypes, and sharing these results on a public-facing, freely accessible website. As students design the prototypes and modules, they work with faculty to identify courses or outreach programs where the modules could be implemented with the long-term goal of the project being an implementation of the learning modules in multiple courses and programs at our institution. In this manner, the project has the potential to impact the learning and interest in soft robotics of both the students designing the modules and the students participating in the modules once they are developed. By having projects made by students, for students, our goal is to benefit both through soft robotics. At the end of each semester, student module designers are asked to self-report their growth in soft robotics learning and interest and reflect on their experience in the clinic. Students participating as learners are also asked to respond to a post-module survey to gauge their learning and soft robotics interest in relation to their module participation.

Results
We have previously reported on the learning outcomes of students in the first semester of the clinic project. This project has since continued, engaging students in another two semesters of project work (Spring 2024 and Fall 2024) as well as summer work for two students (Summer 2024). The results of this paper will focus on the continued observation of learning patterns that emerged in the first semester of the project as well as new patterns that were noted. The effects on student growth from factors that differed in each subsequent semester will also be analyzed. This includes placing a greater emphasis on understanding the potential applications of projects in our engineering curriculum, changing team sizes, as well as other factors.

Conclusion
This work offers valuable insights into the continuation of a novel initiative to involve students in the design of educational modules and encapsulates the results of those modules being implemented.

Authored by
Joseph Richard Midiri (Rowan University), Dr. Cassandra Sue Ellen Jamison (Rowan University), Dr. Smitesh Bakrania (Rowan University), Prof. Wei Xue (Rowan University), and Dr. Mitja Trkov (Rowan University)

Thirty-one exemplary elementary teachers participated in the NSF RET Site: Culturally Responsive Energy Engineering Education in Rural/Reservation Elementary Schools, hosted by the Montana Engineering Education Research Center (MEERC) at Montana State University (Award #2055138, July 2021 – June 2025). These teachers, comprising 8 pre-service and 23 in-service educators, represented diverse rural and reservation communities across Montana, including single-room K-8 schools and all seven American Indian reservations.
Motivated by MSU’s land grant institutional mission, Montana’s Indian Education for All (IEFA) law, and the benefits of early STEM identity formation in students, the MEERC RET Site sought to integrate IEFA and engineering education for elementary teachers through professional development activities and authentic research experiences. Over the first three Site years, the T-STEM survey revealed a significant increase in the participating teachers’ self-efficacy in teaching engineering through culturally responsive pedagogy. Focus groups indicated that the teachers felt a strong sense of belonging and impact, with each cohort continuing to support one another in refining RET-related classroom activities.
The RET Site team gained invaluable insights over successive years, culminating in a reunion of teacher participants during the third year to inform the Site’s future direction. Key lessons included the importance of appropriately paced and scaffolded orientations to energy science and engineering research, which enhanced participant confidence and engagement. Additionally, time spent together as a cohort was found to be crucial for professional development, and more time was needed to co-construct legacy curriculum materials for broader dissemination.
Ultimately, the MEERC RET Site demonstrated that through respect, relationships, connection, and reflection, teacher participants could be empowered to engage in authentic energy and engineering research experiences. This empowerment not only improved their self-efficacy in teaching engineering but also fostered inclusive engineering identity development among their rural and reservation students.

Authored by
Prof. Paul Gannon (Montana State University - Bozeman), Dr. Rebekah J Hammack (Purdue University at West Lafayette (PPI)), Dr. Nick Lux (Montana State University), Sweeney Windchief (Montana State University - Bozeman), Dr. Abigail M. Richards (Montana State University - Bozeman), and Suzanne G. Taylor (Montana State University - Bozeman)

Engineering students require the ability to critically analyze social systems in order to act for the betterment of global society. Our design-based research project aims to support the development of aerospace engineering students’ critical consciousness, a Freirean construct describing the ability to critically analyze systemic oppression from a social justice lens. To emphasize the sociotechnical nature of engineering, we investigate how macroethics lessons can be effectively integrated into aerospace engineering science courses and pursue educational research in this context. We pose two research questions to inform the curriculum: RQ1) What are undergraduate students’ current awareness and perceptions of macroethical issues in aerospace engineering?, and RQ2) In what ways do students feel their education is or is not preparing them to address macroethical issues?

Over the past year, we have made progress on addressing RQ1 and 2 through parallel quantitative and qualitative methods. Quantitatively, we continued to develop a survey and began analysis of preliminary results from 98 students at a large, public, research university in the Midwestern U.S. The survey contains 28 Likert-scale questions about students’ perceptions of the current state of aerospace engineering and 13 questions about their idealized vision for the aerospace engineering field. We are conducting factor analyses of this data to identify the degree to which they conform to the five factors identified in the pilot version of the survey: 1) a belief in the criticality of the relationship between aerospace engineering and society, 2) the ease or difficulty of being an ethical aerospace engineer, 3) the connection between technological determinism and aerospace career paths, 4) an emphasis on macroethics in aerospace engineering coursework via discussions, and 5) the ability of faculty to facilitate conversations on the macroethics of aerospace. The preliminary results of the survey offer interesting insights into how students morally grapple with macroethical issues in aerospace.

We are also conducting qualitative research focused on students’ perceptions of macroethical issues specifically related to their future engineering careers. In our work, we have seen that students recognize macroethics is particularly salient when choosing a career. To address this, we have designed lessons and focus groups based on the Theory of Planned Behavior. This theory hypothesizes that one’s intentions and behaviors (i.e., students’ career aspirations and intentions) are influenced by one’s attitudes (i.e., their attitudes toward the macroethics of certain careers), cultural norms (i.e., their perceptions about the way others value certain careers), and perceived behavioral control (i.e., their beliefs about their ability to obtain a job that aligns with their values). In this research, we will first investigate students’ attitudes through individual interviews. We will then conduct focus groups with the same pool of subjects investigating cultural norms and perceived behavior control. For these focus groups, we will group students of similar academic years and with similar personal attitudes. The results of these focus groups will inform changes to our macroethics lessons and the aerospace engineering curriculum to better support the development of critical consciousness.

Authored by
Dr. Aaron W. Johnson (University of Michigan) and Dr. Corin L. Bowen (California State University, Los Angeles)

The NSF Research Experiences for Teachers (RET) Site in Manufacturing Simulation and Automation has recently completed its third year, continuing its mission to enhance STEM education for high school teachers and community college faculty. Hosted by the University of Louisville, the RET program offers an immersive, six-week research experience that equips educators with the latest advancements in manufacturing technology and pedagogical strategies. During this transformative program, participants engage in hands-on research projects focused on manufacturing simulation, automation, and integration of digital twins into manufacturing processes. Educators work in state-of-the-art laboratories alongside faculty and students, gaining practical insights into modern manufacturing practices and emerging technologies. Through collaborative workshops and interactive sessions, participants develop innovative curriculum modules that bridge the gap between theoretical knowledge and real-world applications, which enables them to effectively teach these concepts in their classrooms. The RET Site also features plant tours and guest lectures to provide educators with a comprehensive understanding of the manufacturing landscape and the skills needed for today’s workforce. As a result of their experiences, educators return to their institutions equipped with new teaching resources and a deeper understanding of the manufacturing industry, which results in a greater interest in STEM careers among their students. This program has made significant contributions to enhancing STEM engagement, building a robust educational pipeline, and strengthening the connection between academia and industry, ultimately preparing the next generation of innovators in advanced manufacturing.

Authored by
Dr. Faisal Aqlan (University of Louisville)

Objective and Motivation:
The Research Experiences for Undergraduates (REU) program fosters research interests among students, motivates them to pursue advanced degrees in Science, Technology, Engineering, and Mathematics (STEM) fields, and develops a diverse, skilled workforce for STEM careers. A strong STEM identity has been linked to a higher likelihood of pursuing a STEM-related career. Understanding how these identities develop and are nurtured - through formal institutional education and informal programs like the REU—is critical. This study aims to examine the impact of REU training experiences on their STEM identification and related career paths.

Methods:
REU training and development activities were planned to provide authentic learning experiences and an enhanced sense of belonging to the REU site. A learning-practice-service cycle was integrated into the REU activities to strengthen the sense of belonging to professional societies. Post-REU surveys were conducted each year at the end of the REU program. After the REU students left the REU site, the PI followed up with REU participants' academic progress and their career choice post-REU training. Trainees' working attitudes and progress on research were evaluated and quantified by the REU mentors based on REU presentations and progress reports to identify the significant factors impacting REU participants' career choices.

Results:
Thirty-eight undergraduate students were trained with the joint support of an NSF REU site and hosting institute during 2021-2024. About 22 REU trainees are from underrepresented groups. The post-REU survey showed a positive response to the sense of belonging and interest in continuing the STEM career. The research findings have led to 29 posters/oral presentations at international conferences, 20 manuscripts in conference proceedings, and 14 journal manuscripts. A total of 12 REU trainees joined graduate schools post-REU training. All 12 REU trainees who joined graduate school worked with their REU mentors on publications such as abstracts, conference proceedings, or journal articles.

Conclusion:
Responses from post-REU surveys and the outcomes of REU training confirmed the critical role played by authentic learning experiences and a sense of belonging in shaping STEM identity. Our findings provide valuable insights for designing REU activities that effectively strengthen the STEM identity of REU participants.

Authored by
Dr. Yu-Fang Jin (The University of Texas at San Antonio) and Jianwei Niu (The University of Texas at San Antonio)

In response to calls for an ever-growing, well-rounded STEM workforce, universities must seek new ways to provide hands-on, experiential learning opportunities to properly equip their students with the knowledge and skills necessary for their futures. Community engagement has been utilized as one means to achieve this goal, pushing students to step beyond the bounds of their campus as they co-construct mutually beneficial, collaborative relationships with community members to address real-world problems.

This work centers math circles as a means of integrating community engagement within the undergraduate curriculum at [Institution]. Math circles are informal learning environments in which groups of children are presented with novel, engaging math problems unlike those which they may see in their regular classrooms (Wiegers & White 2016). Though traditionally led by math experts, our math circles place a pair of upper-level undergraduates at a community partner site (public elementary school, public library, or Boys and Girls Club) for one semester. This program is formalized through concurrent enrollment in a three-credit community engagement course offered through [Math Department]. As part of the course, math circle leaders (MCLs) also meet weekly to learn the fundamentals of conducting math circles and best practices to facilitate learning for elementary-aged children.

Prior studies have detailed the positive effects of math circles, including those which look at children belonging to groups historically excluded from STEM (Auckly et al, 2016; Kennedy & Smolinsky, 2016). One tenet of community engagement, however, is the notion of reciprocity; that is, positive outcomes for all parties involved. In this exploratory study, we shift our focus to examine the impact(s) of this program on the MCLs themselves. Specifically, we employed a qualitative approach to answer the following research questions:

How do MCLs view the role of math in society?
What skills do MCLs view as transferable to their future goals?
How do MCLs engage math circle participants?

Data collection included multiple observations of the math circles, end-of-semester interviews, and student work, such as weekly reflection assignments, in order to gather a holistic insight into the MCLs’ experiences. Our results from two cohorts (N=19) suggest that the MCLs’ participation has been a positive and enriching experience. Nearly all of our participants described math as ubiquitous in society and emphasized the need for widespread math literacy; for some, this idea was mirrored in their interactions with children. Further, the MCLs reported that the experience enabled them to refine the skills they considered essential to their future careers—namely, interpersonal skills such as public speaking and tailoring language to the individual. Finally, observations revealed that MCLs use a wide variety of questions and feedback strategies depending on the context. However, interviews also suggest that there is a personal component to their instructional choices, which often relate to their own experiences as students.

The remainder of this paper will examine these results more closely and provide an overview of the program and the critical components for its implementation. This research is conducted jointly with [Co-Authors] and supported by NSF-IUSE Grant No. [#].

References:

Auckly, D., Klein, B., Serenevy, A., & Shubin, T. (2016). Baa Hózhó Math: Math Circles for Navajo Students and Teachers. Notices of the AMS, Volume 63, Number 7.

Kennedy, E., and Smolinsky, L. (2016) “Math circles: A tool for promoting engagement among middle school minority males.” EURASIA Journal of Mathematics, Science and Technology Education 12.4 (2016): 717–732.

Wiegers, B., & White, D. (2016). "The establishment and growth of Math Circles in America." Research in History and Philosophy of Mathematics. Birkhäuser, Cham, pg., 237–248.

Authored by
Dr. Emily L Atieh (Stevens Institute of Technology), Jan Cannizzo (Stevens Institute of Technology), and Andrey Nikolaev (Stevens Institute of Technology (School of Engineering and Science))

Research in this study investigated the impact of CAVE (Cave Automatic Virtual Environment) virtual reality systems on developing computational thinking skills in engineering students. Using a quasi-experimental design, 37 students were divided into control (17) and experimental (20) groups. This research focused on five key components: Creative Thinking, Algorithmic Thinking, Cooperative Thinking, Critical Thinking, and Problem Solving. Both groups were given a pre-test and post-test assessments using the modified (Korkmaz et al, 2017) Computational Thinking Scale developed by (Ojajuni, 2024). The experimental group engaged with VR-based modules in a CAVE environment, while the control group received traditional instruction. Data analysis included reliability testing, normality tests, and a within-group and between-group comparisons.

Despite low internal consistency in the measurement tool and lack of statistically significant changes (p > 0.05), the results revealed promising trends in the experimental group. Effect size calculations indicated small to medium positive effects in Algorithmic Thinking (Cohen's d = 0.538) and Problem Solving (Cohen's d = 0.557). Creative Thinking and Cooperative Thinking showed small positive effects, while Critical Thinking remained largely unaffected. These findings suggest that CAVE virtual reality environments have the potential for enhancing computational thinking skills in engineering education, particularly in algorithmic thinking and problem-solving. The immersive nature of CAVE systems appeared to facilitate deeper engagement with complex concepts, making abstract ideas more tangible and accessible.

Because of the study’s small sample size and low internal consistency in the assessment tool, the research team concluded that further research is needed to bring a deeper understanding to the impact of knowledge acquisition in a CAVE on engineering students CT skills level. The results of this research have significant implications for engineering education, suggesting that integrating VR technologies could lead to more dynamic and enhanced engineering curriculums. While promising, the variability in critical thinking scores indicates that further refinement of VR content may be necessary to effectively target all aspects of computational thinking.

Research in this study is supported by NSF Project #1915520: Enhancing Additive Manufacturing Education with Virtual Reality and Cybersecurity.

Authored by
Dr. Opeyemi Peter Ojajuni (Southern University and A&M College), Yasser Ismail (Southern University and A&M College), Fareed Dawan (Southern University and Agricultural & Mechanical College), Dr. Albertha Hilton Lawson (Southern University and A&M College), and brian Warren (Southern University and Agricultural & Mechanical College)

Achieving futuristic modes of transportation and energy generation like hypersonic flight and carbon neutrality requires a research-ready workforce with multidisciplinary interests and awareness. The authors’ university educates and develops these next engineers by leveraging its prime location in the heart of the space and technology industry, strong ties with the local engineering industry, and prevailing student interest in engineering and technology. It hosts the jointly funded NSF (through the Division of Engineering Education Centers) and DoD REU Site Advanced Technologies for HYpersonic, Propulsive, Energetic, and Reusable Platforms (HYPER), which cultivates and unites multidisciplinary interests to study advanced structures and systems with application to hypersonics, space, propulsion, and energy. Participants engage in a 10-week experience, conducting graduate-level research under a faculty mentor and alongside a graduate student teammate. In addition to the core research experience, HYPER incorporates a series of professional development seminars, technology training sessions, faculty mentor presentations, and social events.

The HYPER team has sought and developed partnerships with external institutions to amplify the REU impact by approximately 50%, training 73 students across 5 cohorts. In planning for these additional participants, the HYPER team defines 12-14 projects each summer. Most projects are crafted so participants will conduct research via several techniques, such as physical experiments, numerical simulations, or analytical models. All applicants express their project preferences, which drive the participant-mentor pairing process. Even with the extra participation slots, HYPER can accept only 4% of its applicants on average. Recognizing the outstanding applicant potential, the team continues to seek additional avenues to support a greater number of participants.

HYPER has seven core objectives: (1) technically prepare students for graduate school and/or research oriented careers, (2) escalate students’ abilities to simulate phenomena using multi-physics software, (3) improve participants’ oral/written communication skills, (4) enhance participants’ research skill/attitudes, (5) present an REU Site that is diverse in terms of student participation, (6) present an REU site involving students with fewer STEM opportunities, and (7) provide high-quality mentoring. The Program Evaluation and Education Research Group (PEER) provides external evaluation of HYPER. Results over the five cohorts demonstrate the experience encourages participants to pursue their interests, teaches them about multiple research approaches, and provides them a better understanding of how to conduct research. Notably, almost every student expressed satisfaction with their experience. In self-assessed abilities and attitudes, participants noted broad pre- to post-experience increases, with especially strong gains in interdisciplinary experience and aerospace knowledge.

Authored by
Prof. Jeffrey L Kauffman (University of Central Florida)

Background
The JEDI Ambassador Program fosters student advocacy through an inclusive learning community focused on research, outreach, and leadership development to transform the College of Engineering at Southern Public University (SPU), one of the largest Hispanic Serving Institutions (HSI) in the South Eastern United States. This initiative leverages existing programmatic infrastructure to plan for more large-scale and translatable institutional transformation. Grounded in liberatory pedagogy (Freire, 1970) and Youth Participatory Action Research (YPAR, Cammarota & Fine, 2010), the initiative empowers students by fostering critical consciousness and active engagement while addressing systemic barriers to success.
Purpose
This poster presents the strategies, processes, and implementation of the project in the first year of its National Science Foundation (NSF) funding.
Method
The poster presents findings from multiple sources, including post-training surveys and end-of-year evaluation interviews that assess students’ perceptions and experiences of being a JEDI Ambassador. Additionally, we present reflections from the JEDI leadership team and stakeholders, which provide valuable insights into the effectiveness of programmatic practices and overall program impact.
Findings
Our findings show significant shifts in students’ understanding of their roles as advocates for diversity, equity, and inclusion within their academic community. Participants reported increased confidence in their leadership abilities and a deeper understanding of the systemic barriers faced by underrepresented groups in engineering. Feedback from the JEDI leadership team indicates that programmatic practices, such as mentorship and community-building activities, have fostered expansions in their agency and a sense of belonging and engagement among ambassadors.
Implications
The insights gained from this first year highlight the potential for the JEDI Ambassador Program to serve as a model for institutional transformation in engineering education. By leveraging the voices and experiences of student ambassadors, the initiative aims to create a more inclusive and equitable academic environment that benefits students and enhances the broader institutional culture. Future efforts will focus on refining strategies based on stakeholder feedback and expanding outreach to ensure the sustainability and impact of the program.

Authored by
Mx. Nivedita Kumar (Florida International University), Dr. Stephen Secules (Florida International University), and Tekla Nicholas (Florida International University)

COVID-19 emerged in early 2020 killing an estimated 7 million people by April 2025. Wearing masks and maintaining social distance can help limit the spread of infection. However, uncertainty, changing scientific insights, politics, cultural norms and values, knowledge, education, and more influence adherence to government masking mandates. First responders and emergency managers are at the forefront of response operations. Despite their critical role, little research has addressed the willingness of such personnel to wear non-medical masks during a pandemic. Accordingly, this 10-week qualitative student-led Research Experience for Undergraduates study aims to critically evaluate the perceptions of non-medical mask usage amongst first responders and emergency managers during the COVID-19 pandemic.

Authored by
Hector Rogelio Prieto (Valdosta State College), Lisa Wier (Oklahoma State University), and Dr. Tony McAleavy (Oklahoma State University)

This Research Experiences for Undergraduates (REU) program supports active research participation by community college (CC) students with a focus on Smart Engineering, including: Artificial Intelligence/Machine Learning, Smart Health, Smart Materials, and Smart Infrastructure. The program is offered as 10-week non-residential summer research experience for local community college students. The program also features high-quality interactions of students with faculty and/or other research mentors, access to appropriate facilities and professional development opportunities, an opportunity to tap the nation's diverse student talent pool, and broaden participation in science and engineering. Th participating CC students are also introduced to and encouraged to transfer to 4-year college education to enhance their future employment and increase participation of the underreported in STEM education.

This extended abstract presents the results from a retrospective pre-post exit survey by the grant external evaluator of participants in the xxx University Pathways REU in engineering, as funded by the National Science Foundation. This project supports regional community college students at xx University for summer work in engineering research, placing the participants in research labs working on cutting edge problems in AI/machine learning, smart infrastructures, smart materials, and smart health.

The evaluation research questions include:

• To what extent was the project successful at engendering growth across the four target constructs?
• What aspects of the summer experience are reported as being most beneficial?
• What do participating students say about their summer experience more generally?
• How do the 2024 cohort’s results compare with those of the previous two cohorts?

The key findings include:

• The REU exit pre-post surveys again showed consistently positive results across the intended outcomes with moderate to large effect sizes.
• The importance of conducting research under the guidance of a faculty mentor was viewed by the students as essential to the summer’s experience.
• The 2024 REU group expressed a strong association between gains in self-confidence, understanding of engineering and a commitment to continue in engineering.
• The 2024 evaluation findings are generally consistent with those of the previous two years, with the 2024 group reporting the most positive effects of participation among the three cohorts.

If accepted, the extended abstract will delve into the details of the survey questions, statistical sampling and methods, and the statistical results analysis.

Authored by
Dr. Ibrahim F. Zeid (Northeastern University) and Mrs. Claire Duggan (Northeastern University)

Funded by the Improving Undergraduate STEM Education program of National Science Foundation, our project is focused on developing and implementing computerized adaptive testing (CAT) in a freely accessible online platform system named LASSO that encompasses several conceptual inventories across STEM. CAT is an adaptive assessment method that selection of test items based on students’ real-time performance. This adaptive approach allows for precise and efficient measurement of student proficiency (sometimes also referred to as ability). By selecting questions at the appropriate difficulty level for students, the assessment system in LASSO is able to apply several algorithmic models to derive information about student skill mastery, content area learning, and student conceptual profiles. By developing an in-depth and detailed profile for each student, the adaptive testing system is able to provide instructors with individualized insights into student learning, which is particularly valuable for large enrollment introductory STEM courses where instructors are not able to collect this data in real time.

The core of our adaptive testing system uses Item Response Theory (IRT) and Cognitive Diagnostic Models (CDMs) to provide detailed analyses of student proficiency and skill mastery. IRT offers precise metrics by modeling the relationship between item characteristics and student abilities, providing a fine-tuned understanding of how students interact with assessment items. CDMs further enhance this process by identifying the underlying skills students have mastered. CDMs are also able to model content area mastery for content areas such as momentum, energy conservation, two-dimensional kinematics, etc. Further, Transition Diagnostic Classification Models (TDCMs) offer the ability to develop conceptual profiles using the specific incorrect answers students select to identify student misconceptions. These models offer a granular view of the cognitive strengths and weaknesses of students and allows instructors to identify the specific areas where their student need improvement.

While adaptive testing provides instructors with a powerful tool for assessing students, large enrollment classes still present a challenge for providing in the moment instructional interventions at scale. By integrating adaptive learning processes into an adaptive testing platform, our work aims to present a more complete framework for optimizing student outcomes in large enrollment STEM courses. This work in the process explores the next step in our project, which involves transitioning from CAT to adaptive learning. By leveraging the diagnostic insights from IRT and CDMs, we are developing an adaptive learning system that curates personalized learning pathways for each student. This system will select video-based content and instructional materials tailored to individual skill gaps according to their skill mastery profile and abilities. We aim for the outcome to be an engaging, time-efficient, and effective learning experience, with content tailored to each student's ability level and mastery profile. By integrating CAT with adaptive learning, we can create a continuous feedback loop where assessment informs instruction in real-time. This adaptability ensures that each student’s learning path evolves according to their progress, leading to improved academic outcomes and a more personalized educational journey.

Authored by
Dr. Jason Morphew (Purdue University at West Lafayette (PPI)), Amirreza Mehrabi (Purdue Engineering Education), and Ben Van Dusen (Iowa State University of Science and Technology)

This work outlines the course mapping structure of a training program focused on helping undergraduate peer mentors effectively assist first-year students in academic makerspaces, design courses, and laboratory classrooms. Student learning in unconventional learning environments such as makerspaces can be challenging, particularly if the learning requires the students to engage in teams to complete the non-traditional learning activities associated with project and problem-based learning.

The goal of the peer mentor preparation program is to provide undergraduate peer mentors with the knowledge, tools, feedback, and practice to develop skills to facilitate students enrolled in a first-year makerspace design course and support their learning in critical social and developmental areas.

The peer mentor preparation course contains six modules. For each module, this work maps out the taxonomy-based learning objectives, case scenario videos, and active learning exercises in support of the overall course goals. The digital learning modules developed and refined throughout this research project for University of Florida first-year engineering mentors will be openly shared with the larger STEM community for instructors at other institutions to utilize in their peer mentor preparation programs.

This material is based upon work supported by the National Science Foundation, IUSE Improving Undergraduate STEM Education under Award Nos. 2315229 and 2315230.

Authored by
Dr. Pamela L Dickrell (University of Florida) and Dr. Louis S. Nadelson (University of Central Arkansas)

The COVID-19 pandemic required institutions to offer high-impact practices like undergraduate research experiences in the online modality. Undergraduate research is a high-impact activity with significant benefits to students, faculty, and institutions. Engaging in research enhances students' disciplinary knowledge, critical thinking skills, and professional development, while also fostering long-term mentoring relationships and networking opportunities. However, there is not yet attention on how the global disruption from the pandemic influenced the online student population’s interest to engage in undergraduate research.

The Research Scholars Program was established and expanded at the university with funding from two National Science Foundation (NSF) Improving Undergraduate STEM Education (IUSE) grants. The primary goal is to build a framework to support online undergraduate students’ engagement in research, in combination with mentorship, peer connection, and supervision. The program's launch aligned with the onset of the pandemic.

This descriptive study featured the use of online surveys to explore undergraduate interest in research experiences. Participants were invited to complete the research survey through students enrolled in an undergraduate 3-credit hour upper-division course on ethics. This course was selected because it is required of nearly all majors, ensuring a broad representative sample across the online campus at the university. Key findings reveal that the type of research opportunity significantly influences student engagement. Opportunities embedded within coursework are more likely to attract participation compared to independent research projects. Additionally, barriers such as time constraints and financial considerations were identified as critical factors affecting students' willingness to engage in research activities. Despite these challenges, the study found that students maintained a strong interest in research, indicating a potential for increased engagement if institutions can effectively address these barriers.

Results were compared to the previously published data from the same institution before the COVID-19 pandemic. In all cases of awareness, opportunities and interest in undergraduate research were unchanged from pre- to post-COVID time period.

Authored by
Emily Faulconer (Monash University), Dr. Robert Deters (Embry-Riddle Aeronautical University), Kelly A George (Embry-Riddle Aeronautical University - Worldwide), Brent Terwilliger (Embry-Riddle Aeronautical University - Worldwide), and Dr. Darryl Jim Chamberlain Jr. (Embry-Riddle Aeronautical University - Worldwide)

This paper presents one of the findings from a National Science Foundation (NSF) funded research project aimed at enhancing engineering and mathematics (EM) education. The research project specifically focuses on the role that students’ self-regulation in action (SRA) and metacognitive knowledge about tasks (MKT) play during problem-solving activities. To gain insight into academic problem-solving practices, the study examines how undergraduate students enrolled in second-year engineering and mathematics (EM) courses (Engineering Statics and Ordinary Differential Equations) use their MKT to navigate problem-solving challenges while concurrently monitoring and evaluating their cognitive processes.
Twenty undergraduate students (7 female and 13 male) from a land-grant university in the western United States participated in one-on-one semi-structured interviews and practiced the think-aloud protocol (TAP) during problem-solving sessions to generate qualitative data. The students were tasked to solve four subject-specific problems in engineering and mathematics (EM) courses. In total, twenty students generated eighty problems throughout the study, comprising forty problems (20 easy and 20 difficult) produced at the beginning of the semester and an additional forty problems (20 easy and 20 difficult) generated at the end of the semester that were analyzed using constant comparative analysis (CCA) technique.
The analysis included two coding phases: initial codes that represent the raw data and focused codes that reveal the seven key problem-solving patterns within the dataset. Based on this analysis, the seven patterns were organized into four quadrants, classified according to high/low levels of metacognitive knowledge about the task (MKT) and high/low levels of self-regulation in action (SRA). This paper specifically examines the first quadrant, which is indicative of impeccable learning episodes.
Students in this quadrant demonstrated a deep understanding of the tasks, accurate self-evaluation, and effective monitoring, leading to appropriate problem-solving strategies and successful results. These findings highlight that student who engaged in impeccable learning episodes demonstrated a thorough comprehension of the tasks, enabling them to effectively self-regulate their actions. These results have important implications for educational strategies meant to develop students’ MKT and SRA to improve their problem-solving abilities. A brief discussion is included at the end of the paper.

Authored by
Dr. Oenardi Lawanto (Utah State University), Dr. Angela Minichiello P.E. (Utah State University), and Zain ul Abideen (Utah State University)

While Internet of Things (IoT) has become more common in everyday applications and products, many of these same IoT-based applications do not consider cybersecurity issues and associated concerns. In addition, with more access to cloud computing resources and more readily available graphics processing units (GPUs) being utilized for artificial intelligence (AI), AI-based processing continues to disrupt multiple technical fields and associated products. As a result, AI and cybersecurity were identified as areas that need to be introduced or included for the students participating in IoT-based projects.

In this project a team at two Hispanic Serving Institutions (HSIs), Texas A&M University-Kingsville and Texas A&M University-Corpus Christi, has developed new materials addressing the use of AI and taking into account cybersecurity. Using Python and appropriate Python libraries, pre-trained AI algorithms are added to Python coding which will allow more advanced features such as image recognition to be included in the student projects. Information on cybersecurity standards have been added to student project-related materials to introduce the student to an ongoing design and operational concern for IoT-enhanced products and projects. An exercise using AI has been added to the IoT tutorial materials previously developed as part of the work supported by this grant.

Authored by
Dr. Lifford McLauchlan (Texas A&M University - Kingsville), Dr. David Hicks (Texas A&M University-Kingsville ), and Dr. Mehrube Mehrubeoglu (Texas A&M University - Corpus Christi)

The lack of adequate foundational mathematics and physics skills among many underrepresented in STEM students poses a significant challenge to retaining students in engineering professions. To tackle this challenge, Marymount University (MU) is implementing Project DREAM (Diversity Recruited into Engineering through Advanced Making) through the support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (IUSE HSI Program) NSF grant. We present the initial findings on student perceptions from a year-long introductory engineering course that employs low-cost makerspace technologies, such as 3D printing, Arduino, Python programming, and virtual reality, in project-based experiences to enhance foundational engineering skills. In the first semester students receive instruction in basic math and physics to supplement those introductory courses as well as comprehensive training in how to use 3D design software, operate 3D printers, program circuits, write basic code in Python and interact within VR environments, culminating in a capstone project experience. In the second semester, students apply these skills in teams to complete two guided capstone-style projects (e.g. 3D printed battle robots, programming AI dogs, fabricating magnetic stirrers and assembling a 3D printed SeaPerch subersible) Overall, students reported a positive experience with significant increases in confidence in areas of teamwork, problem solving, data management, engineering design, data analysis and technical skills. While students’ perception of the relationship between the skills developed through this course and themselves as engineering professionals improved through the duration of this course, this perception must be further strengthened. We suggest that this low-cost and accessible model can be utilized by any educational institution to provide in-demand foundational engineering skills to improve engagement and retention of underrepresented in STEM students using common makerspace technology.

Authored by
Shama Rajan Iyer (Marymount University) and Eric J Bubar (Marymount University)

The goal of this project is to ensure the next generation of Civil and Environmental (CE) engineers are prepared to design, build, operate, monitor, and maintain the vast and complex civil infrastructure systems required for a sustainable & resilient society in the decades to come. This will be accomplished by developing a series of educational modules that will shift students’ focus from a siloed understanding of the traditional CE domains, towards a holistic and advanced Civil Infrastructure Systems and Analytics mindset and skillset. Specifically, the two objectives of this project are to: (1) develop a system-level thinking module that introduces the students to the core concepts of systems engineering followed by a series of analytic modules that provide students with a foundational understanding of the analytical mindsets required for 21st century CE Engineers, and (2) implement these modules across undergraduate CE engineering courses at Manhattan University to evaluate and assess the systemic changes in STEM educational outcomes that will result from the innovative modules developed for this project. We hypothesize that analytics-based system thinking can be infused into any existing undergraduate CE engineering curricula without requiring a complete redesign of these programs. Also, our assessment results resulted in new knowledge of the effectiveness and the transferability of our research, clearly demonstrating what works and what does not in undergraduate CE Engineering and STEM education.

Authored by
Christina Cercone (Manhattan College), Medya Fathi (Manhattan University), Matthew Volovski (Manhattan College), Dr. JUNESEOK LEE (Manhattan University), and Peter K Sweeney (Manhattan College)

As generative AI becomes ubiquitous, writers must decide if, when, and how to incorporate generative AI into their writing process. Educators must sort through their role in preparing students to make these decisions in a quickly evolving technological landscape.

We created a writing tool that provides scaffolded use of a large language model as part of a research study on integrating generative AI into an upper division engineering writing and communication course. Drawing on decades of research on integrating digital tools into instruction and writing research, we discuss the framework that drove our initial design considerations.

We then describe data from the iterative design and piloting of our tool and related resources during our first year of implementation. Based on our work with instructors over the past year, we have identified some indications of what is emerging as best practices for both using generative AI productively in academic writing and for integrating generative AI into writing intensive-courses. With respect to writing with AI, a process in which students think first, use "good enough" prompting to initiate their AI interaction, then follow up asserting their own agency by iteratively directing the AI to provide modified output, carefully corroborate and interrogate the AI output for accuracy and bias, and finally reflect on the interaction and future uses of the AI. To create an AI-infused curriculum, we are learning that instructors focus on (and center) student learning objectives before analyzing their course to determine where the AI can appropriately play a role without offloading essential learning activities. Instructors then revise the activities and after enacting the revised curriculum, reflect on student learning to ensure all objectives are met. In some instances, instructors have after reflection decided that they need to reintroduce learning through revised activities. We end with suggestions for those implementing generative AI into other writing-intensive courses and an indication of our future direction.

Authored by
Dr. Tamara Powell Tate (University of California, Irvine), Beth Harnick-Shapiro (University of California, Irvine), Mark Warschauer (University of California, Irvine), and Waverly Tseng (University of California, Irvine)

This research investigates the intricate relationship between undergraduate students' task-related knowledge and self-regulation strategies during problem-solving, resulting in a conceptual framework that illustrates the simultaneous use of metacognitive knowledge about tasks (MKT) and self-regulation (SR). The study was conducted in two second-year engineering mathematics courses—Engineering Statics and Ordinary Differential Equations—with a sample of 20 students (7 females, 13 males; 11 from mathematics and 9 from engineering).

Through a combination of pre- and post-problem-solving interviews and think-aloud protocols, the study examined participants’ problem-solving processes in real time. Each student tackled two problems of varying difficulty, generating 40 problem-solving sessions. The central research question focused on how the interplay between students’ metacognitive awareness and self-regulatory strategies affects their performance.

The pre-solution interviews provided insights into participants’ task comprehension and confidence, while think-aloud protocols highlighted their self-regulation tactics in action. Post-solution interviews offered reflections on their problem-solving experiences. Additionally, participants' problem solutions were evaluated by course instructors and graduate students with subject matter expertise.

Through qualitative analysis, seven distinct problem-solving episodes were identified, each demonstrating a unique way in which students’ metacognitive understanding and self-regulation intersected to impact task outcomes. These findings emphasize the importance of both cognitive and self-regulatory factors in problem-solving, offering valuable insights for improving instructional practices. The study concludes by discussing the broader implications for teaching strategies and student learning.

Authored by
Dr. Oenardi Lawanto (Utah State University)

Undergraduate participation in research provides opportunities for students to develop their research and technical skills, network with other students/professors, raise their awareness of graduate studies, and understand the social context of research. While undergraduate students are often able to participate in research at their own institution or nationally in the US (through available Research Experiences for Undergraduates sites), it is also possible for undergraduates to complete research internationally.

In addition to the domestic benefits of research experiences, this provides an opportunity to network with international students/professors, learn about a different country and culture, and learn new perspectives on how professionals from other countries approach research. In support of this mission an International Research Experiences for Undergraduates (IRES) site is providing 12-week summer research experiences for students from the University of Alabama at the Brno University of Technology in the Czech Republic.

To evaluate the effect of this international research experience on the intercultural communication competence of the participations, the second cohort of students (N=8) completed the Intercultural Sensitivity Scale (ISS) immediately before and immediately after their 12-week research experience in the Czech Republic. The ISS is a survey with 24-items across five factors: interaction engagement, respect for cultural differences, interaction confidence, interaction enjoyment, and interaction attentiveness.

This work provides an overview of the elements in the IRES site, students responses to the ISS before and after their participation, and analysis of which factors had significant changes before and after the program. These details are expected to help evaluate how students intercultural communication competence is (or is not) altered from participation in this specific program and inform recommendations for site revisions for future iterations.

Authored by
Dr. Todd Freeborn (The University of Alabama)

The Sketchtivity project explores the teaching of freehand sketching using an intelligent tutoring software in engineering design education through providing individualized feedback on sketching fundamentals and 2-point perspective techniques. Project findings indicate that Sketchtivity enhances spatial visualization skills, creativity, and student confidence, with measurable improvements observed in as little as 3-5 weeks across 4 university implementations. The research has raised further questions regarding the specific sketching knowledge and skills engineers need to learn to optimize visual communication and engineering education. One study aims to begin to address these questions by investigating the role of sketch styles in influencing engineer preferences and perceptions of creativity and functionality in sketched product ideas. This study and future research on sketching instruction will use surveys to evaluate perceived characteristics including preferences, highlighting the importance of examining the distinctions between ranking and rating scales as tools for assessing these perceptions.
A comparative study was conducted across two graduate level mechanical engineering courses to assess the validity of rating versus ranking scale methods in evaluating sketch preferences. Participants evaluated 10 products across 3 categories (spacecrafts, chairs, and toasters) presented in varying line styles including single line, variable thickness, feathered and heavily feathered line work. One course completed a ranking based survey while the other completed a rating-based survey. The analysis compared the two data sets to determine whether ranking or rating scales better capture aggregate preferences in design sketch assessments. The results indicate that ranking and rating scales reveal similar mean preferences, with a clear favorability towards the single line style especially for more complex engineering products indicating that the clean, single line style typically seen in industrial design should be taught to engineers. Ranking scales demonstrated greater differentiation between sketch styles, while rating scales showed more compressed responses, with participants often assigning equivalent scores to multiple sketches. The study suggests that while both methods are valid for engineering design education contexts, ratings may be more suitable when finer distinctions between preferences are sought, whereas ranking is more effective for gauging general trends. This work informs best practices in survey design for engineering education research and for future work, emphasizing the importance of selecting scale types based on study objectives. The findings contribute to a broader understanding of how sketching styles influence engineering design perception and provide insights into instructional strategies for developing effective sketching skills among engineering students.

Authored by
Shiho Nakamura (Georgia Institute of Technology) and Dr. Julie S Linsey (Georgia Institute of Technology)

The rapid evolution of advanced manufacturing systems requires a workforce adept in solving the problem but also understanding the impact of their solution on others. To address this critical need, we propose integrating design thinking as an intervention into a college-level engineering course. This project aims to equip students with abilities to develop empathy while solving authentic problems, which are essential in the increasingly complex world of advanced manufacturing.
An experiential learning design in a college-level engineering course was structured around design thinking processes, a human-centered approach to innovation that balances technical constraints with user needs. Students engaged in iterative problem-solving processes including defining the problem, ideation, and creating simulation prototypes and design briefs. We applied pre- and post-surveys to measure students’ cognitive empathy.
Statistical analysis confirmed the design thinking intervention improved student’s cognitive empathy in solving engineering problems. This project demonstrates the potential of integrating design thinking with engineering education to improve students’ understanding of advanced manufacturing but also developing critical soft skills such as empathy. This model provides a replicable framework for fostering a well-rounded skill set in future engineers and help them to be better prepared to address the challenges of modern manufacturing systems.

Authored by
Hengtao Tang (University of South Carolina) and Dr. Ramy Harik (Clemson University)

As the Industry 4.0 revolution reshapes manufacturing, design, and engineering processes, the role of mechanical engineers is expanding beyond traditional boundaries. Since mechanical systems are increasingly interconnected with digital technologies, training the next-generation mechanical engineers in Internet-of Things (IoT) programming and software engineering methodology is critical for meeting industry demands. However, these critical skills are often missing in traditional mechanical engineering curriculum. To address this challenge, we have modernized our curriculum by integrating IoT technologies and software engineering concepts in a new course. Specifically, we couple project-based learning and Agile methods, which represent the best practices in the IoT industry, to provide mechanical engineering students with a highly practical, hands-on learning experiences. In this paper, we present details of the Agile method component of the new course.

Authored by
Dr. Xinghui Zhao (Washington State University), Prof. Hakan Gurocak (Washington State University-Vancouver), and Dr. Kristin Lesseig (Affiliation unknown)

Makerspaces are becoming a staple at engineering institutions of all sizes worldwide. These spaces allow for conceptual design problems and solutions to be practically realized by making the tools and resources needed accessible to students. Due to their widespread adoption, it is prudent to understand the characteristics of effective and ineffective student and tool usage patterns. Makerspace researchers have adopted network analysis techniques commonly used by ecologists to quantify mutualistic ecosystem usage patterns, for example interactions between plants and pollinators, to both understand and analyze student-tool interactions. In line with prior studies where makerspaces were visualized as bipartite networks (student users and tools are treated as two distinct actor groups and the interactions between them captured visually in a network graph and quantitatively in an interaction matrix), the work here further projects this user-tool bipartite network onto itself to arrive at a tool-tool makerspace network model. The goal for this approach is to highlight the interactions between tools as determined by common users. Makerspaces at two large universities in the United States are used as case studies for this analysis, with one space only allowing course projects (School A) and the other allowing both course and personal projects (School B). Prior work calculated a participation coefficient and within-module degree for both schools, network graph metrics that were found to reveal how adeptly actors interact with others inside and outside of their tool clusters or modules. This work concluded that School B, characterized by its allowance of personal projects, saw a higher level of interactions between modules. Building on that work here, tool-tool network densities provide deeper insights into tool usage patterns both during and recovering from COVID-19 restrictions. In the context of the tool-tool networks, a higher network density indicates that more tools are being used by the same users, possibly implying a greater potential for collaboration between users. The analysis finds that density values are consistently higher at School B both during and after the disturbance. A potential tool usage trend emerges, possibly due to personal projects being allowed and encouraged at School B, where common tool usage may imply more users are collaborating (another important skill for engineering students entering the workforce). Additional work is required to clarify these conclusions regarding the relationship that project types may have on intra-network connectivity and collaboration in engineering makerspaces. This project was funded by the National Science Foundation’s Improving Undergraduate STEM Education (IUSE) program.

Authored by
Pepito Thelly (Texas A&M University), Dr. Julie S Linsey (Georgia Institute of Technology), and Dr. Astrid Layton (Texas A&M University)

Engaging teams of computing students, working over one or more terms to develop software systems that contribute or improve some aspect of their local community is a valuable high-impact educational practice [1-3]. However, this form of community-based service learning can be an intimidating practice to implement [4,5]. This NSF IUSE-sponsored project, Scaffolded Projects for the Social Good (SPSG), introduces a framework for a software studio approach designed to seamlessly integrate service learning into the computing curricula [6,7]. SPSG offers a low-adoption threshold solution for educators, providing a comprehensive toolkit to guide the process of selecting appropriate projects, and providing structure for all deliverables that fosters collaboration between students and community organizations, from the point of initial contact through project hand off. The project focuses on creating a flexible, easy-to-adopt framework that allows instructors to easily embed externally sourced community-based service learning projects into their courses. The ultimate goal is to leverage this high-impact educational practice to promote the development of professional dispositions and technical skills among students while working on real-world projects that serve non-profit and community organizations.

The SPSG framework was piloted in four courses at two institutions, involving 21 student teams during the Fall 2023 and Spring 2024 semesters. These teams, consisting of juniors and seniors, engaged in projects supporting community-based and commercial clients, with the majority working on non-profit initiatives, all using the SPSG framework. To assess the impact of these experiences on students, pre- and post-surveys with a longitudinal component were used to gather data on students’ skill development and professional growth.

Key deliverables from the project include the SPSG Structure Guide, Instructor Guide, Code Management Guide, Project Feasibility Evaluation Rubric, Mentor Guide, and several questionnaires for clients. These resources are designed to streamline the project implementation process for instructors, guiding them through each phase of project planning and execution while encouraging collaboration between students, mentors, and clients. All materials are available on the project’s website, providing open access to any educator interested in adopting the framework.

In the upcoming year, the framework will be further refined and adapted to new educational contexts to broaden its applicability, including the involvement of community partners in socially valuable student projects that don't necessarily lead to a fully deployable software artifact. As survey data is analyzed, adjustments will be made to improve the SPSG framework and its associated tools, enhancing its value to instructors and students alike in engineering education programs.

Authored by
Dr. Chad A. Williams (Central Connecticut State University), Dr. Stan Kurkovsky (Central Connecticut State University), Nathan Sommer (Xavier University), and Prof. Mikey Goldweber (Denison University)

Background
Despite faculty development initiatives focused on pedagogy, the literature reveals descriptions of slow changes in faculty pedagogical transformation [1]. We contend that there is a missing focus on the science of learning and the impact of teacher-student interactions as the reason behind the low efficacy of faculty development activities in changing beliefs and behaviors [2]. To address this gap, this project seeks to broaden engineering teaching with theory-based educational resources (BETTER) through a Caring Science lens [3].

Objectives
Objective 1: Examine the impact over time of a faculty development curriculum grounded in a humanistic-educative framework for promoting a humanizing model to engineering education.
Objective 2: Examine the impact of a CoP as a faculty development opportunity to compel faculty to make active efforts to transform their beliefs and attitudes regarding the use of learning theory as part of their teaching practice.

Research Design/Program Description
This project uses a longitudinal, quasi-experimental, explanatory sequential mixed-methods design. The programming intervention is either via a 6-week in-person CoP (treatment group) or via a 6-modules of self-paced online learning (control group). Participants will be followed and evaluated (pre/post surveys, interviews, artifact collection, and observations) throughout the three years of project funding (NSF IUSE #######). This paper presents preliminary findings of cohort 1 that took place in summer 2023.

Results/Evaluation
Guided by the Faculty Learning Outcomes Assessment (FLOA) Framework, we applied validated quantitative instruments and qualitative approaches to collect and analyze data aligned with programming outcomes regarding appreciating pedagogy, applying pedagogical reasoning to course design, and utilizing teaching practices that enhance student learning [4, 5].
In our first iteration of BETTER, we found statistically significant changes pre/post via the General Teaching Scale [4] in the areas of awareness (p<0.019000), integration (p<0.000301), and emotion (p<0.038000).

Qualitatively, faculty reported making adaptations to their teaching and student interactions, including increasing welcoming behavior, trying to get to know students personally, and explaining reasoning behind their teaching, assessment, and grading practices. They also reported increasing flexibility toward students' lives outside of class and being willing to listen and demonstrate empathy toward the challenges students face in their personal lives. Faculty reported wanting to change even more, but they cited several barriers to making desired changes which will be presented.

Of interest when integrating study findings, although there was noted change in awareness and integration, qualitative data analysis revealed difficulty in extrapolating learning theory examples from different fields of study.

Conclusions
Most educational systems processes impede the preparation of students for the challenges they will face in their professional lives and call for a humanizing way of teaching. Our human-centered model will influence engineering faculty pedagogical beliefs to support student learning and retention, especially those traditionally underrepresented in engineering.

Future Plans
We are continuing to collect longitudinal data from cohort 1, including conducting course observations and artifact analysis (a figure of data collection will be presented). Cohort 2 recently concluded their programming and data collection is underway.

Authored by
Dr. Amber F Young-Brice (Marquette University), Dr. Allison Murray (Marquette University), and Somesh Roy (Marquette University)

This paper describes the design and development of a Web-based Data Science Learning Platform (DSLP) aimed at making hands-on data science learning accessible to non-computing majors with little or no programming background. The platform works as middleware between users such as students or instructors, and data science libraries (in Python or R), creating an accessible lab environment. It allows students to focus on the high-level workflow of processing and analyzing data, offering varying levels of coding support to accommodate diverse programming skills. Additionally, this paper briefly presents some sample hands-on exercises of using the DSLP to analyze data and interpret the analysis results.

Authored by
Dr. Xumin Liu (Rochester Institute of Technology)

Research in nontraditional engineering students (NTES) generally focused on their demographics, the challenges they faced, their deficiencies, and methods to improve the outcomes of NTES in engineering programs. None of the existing studies in NTES are asset-based and focus on their strengths such as their lived experiences, or leveraging their strengths to increase engaged student learning for all students. The objective of this study is to identify the characteristics of NTES lived experience that can be incorporated into engineering classrooms to increase engagement for all students. Through interviews with NTES and thematic analysis, the attributes of NTES’ past experience that were of interest to traditional engineering students in their engineering courses and classrooms were identified. Results show that NTES professional skills (networking and problem-identification), and the application of their work experience into technical lecture content were more frequently discussed when they collaborated with traditional engineering students. Future work for this project will include validating the lived experience of the NTES with the traditional students, and creating in-class cooperative learning activities that utilize NTES lived experience.

Authored by
Dr. Ean H Ng (Oregon State University), Dr. Ganapathy S Natarajan (University of Wisconsin - Platteville), and Ingrid Scheel (Oregon State University)

In a world where data are ubiquitous, it is becoming increasingly important to practice ethical data management, to ensure data is findable, accessible, interoperable, and reusable (FAIR). However, there remains a lack of interdisciplinary endeavors that embed data life cycle ethical management education into undergraduate research academic courses in Engineering.

In this project, we are designing the framework and curricula to develop competencies of undergraduate researchers regarding ethical management of data throughout the research data life cycle. Our “Design Reasoning in Data Life Cycle Ethical Management Framework” is informed by three existing frameworks: 1) The Design Reasoning Quadrants Framework, 2) The Data Life-Cycle Model, and 3) The Reflexive Principles Framework. This framework guides the integration of data management and ethical considerations in engineering design reasoning. We presented the initial framework at ASEE 2024 and have refined it.

To augment the data ethical management practices of undergraduate researchers, we developed and taught stand-alone courses for undergraduate researchers as well as workshops for research mentors. One new course “Understanding Your Research Data” in Spring 2024. Eleven undergraduate research scholars enrolled in the course and applied research data life cycle ethical management to their current undergraduate research projects. Their mid-term, final reflections and final projects indicated that the course improved their skills in working with data efficiently and ethically, built up their confidence and comfort in managing data-intensive projects, and recognized the crucial role of organized data in the success of a research project.

In addition, the team received feedback from the students expressing the need for additional scaffolding like worksheets and concrete examples. A practical guidebook is currently under development featuring worksheets and samples to support efficient and responsible data cycle management. Some examples are “Data Management Plan Checklist", “Data Preservation Protocol”, "File Naming and Organization Guide”, “Data Quality Checking”, “Metadata - Quantitative and Interview Data Dictionary Creation”, “Data Storage and Backup”, and “Data Repository Selection”.

Authored by
Dr. Wei Zakharov (Purdue University at West Lafayette (COE)), Dr. Senay Purzer (Purdue University at West Lafayette (PWL) (COE)), Dr. Carla B. Zoltowski (Purdue University at West Lafayette (PWL) (COE)), Joreen Arigye (Purdue University at West Lafayette (COE)), and Sarah Sewell (Purdue University at West Lafayette (COE))

Several reports suggest there is an urgent need to greatly increase both the number and diversity of students graduating in STEM fields over the next decade. They recommend switching to teaching methods backed by research, like concept-based active learning. This approach focuses on using activities to help students understand key concepts deeply, rather than just memorizing facts or algorithmically solving problems. Studies show that pedagogies like concept-based active learning boosts student engagement and achievement, helps retain students in their program of study, and narrows the performance gap for underrepresented groups. It is also crucial for solving real-world problems, especially in fields like engineering.

However, the main challenge isn’t proving that these methods work better than traditional teaching—it’s getting instructors to actually adopt them. This project aims to spread the use of the Concept Warehouse, a web-based tool for concept-based active learning, in Mechanical Engineering (ME) programs. The tool was originally developed for Chemical Engineering (ChE) and includes over 3,500 "ConcepTests," which are short questions designed to engage students and assess their understanding of concepts. The Concept Warehouse also contains concept inventories and more extensive instructional tools like inquiry-based activities and virtual laboratories.

The Concept Warehouse has grown significantly, now supporting over 1,700 faculty and 40,000 students. In the current phase of the project, new questions have been added for Statics, Dynamics, and Mechanics of Materials, with totals of 354, 445, and 42 questions, respectively. A review process is underway to improve the clarity of these questions and identify areas where more questions are needed. Since June 30, 2018, 720 new faculty accounts have been created, and 3,466 students have answered these mechanics questions through the tool’s student interface while some instructors deliver the content through other mechanisms. The team is working on developing a new Rigid Body Dynamics Concept Inventory to expand the current capabilities of the Dynamics Concept Inventory and has also created several adaptive learning modules for mechanics and material science.

Our analysis has focused on both instructor and student learning. On the instructor side, we have investigated the impact of introducing the Concept Warehouse on instructors’ trajectories of practice, an innovative framework based on our theoretical model to understand the role of contexts (including their institutions, courses, students, personal history and pandemic-related adaptations) in their use of the tool’s multiple affordances. We also have studied students’ conceptual and metacognitive learning processes through analysis of written explanations and think-aloud interviews.

We delivered a three-day workshop to 22 two- and four- year university faculty members dedicated to concept-based active learning and the use of the Concept Warehouse. There was an overwhelming response from our call, with 179 applications completed. Twenty-one (21) out of 22 rated the summative question “Would you recommend this workshop to a colleague?” as “Strongly Recommend” and one (1) as “Recommend.” In the coming academic year, we will hold a virtual Community of Practice with faculty from the workshop to further use of concept-based instruction.

Authored by
Prof. Milo Koretsky (Tufts University), Dr. Brian P. Self (California Polytechnic State University, San Luis Obispo), Dr. Christopher Papadopoulos (University of Puerto Rico, Mayaguez Campus), Dr. Michael J. Prince (Bucknell University), and Prof. Dominic J Dal Bello (Allan Hancock College)

The addition of generative artificial intelligence (AI) algorithms to the traditional engineering design process, e.g., generative design (GD) and large language model (LLM) assisted design, significantly shifts the role, cognition, and behavior of the human designer. The way the human explores the design space is the design task most affected by the generative AI-based paradigm shift. Traditional design (TD) characteristically requires the human designer to explore the design space by generating a range of ideas/potential solutions which might solve the goal(s), and then analyze their performance in the objective space. However, the GD process prompts designers to employ inverse thinking by computationally defining and scoping the objective space (e.g., materials and manufacturing constraints) for the AI-agent to explore and analyze all available options in the design space while optimizing towards (a) feasible solution(s) which are presented to the human designer for further selection and iteration. The inverse relationship of TD and GD requires the designer to think and behave in a unique manner for each paradigm. However, there is little research exploring Generative Design Thinking, i.e., the cognitive processes that underlie GD. There also lacks an available infrastructure for conducting and applying the results of educational research to determine best GD teaching practices and develop new teaching materials.

The goals of this joint-effort project (UT-Austin, UIUC, Oregon State University, University of Arkansas, and the Institute for Future Intelligence) are to address three research gaps: design cognition research to define GDT, education research to create/test materials for teaching GD/GDT, and technology development to support GD research. In design education, the development of curriculum to prepare next-generation students for effective industry use of generative AI requires a clear definition of GDT which describes the relevant cognitive processes and how to best teach them. In design research, this definition will aid the development of AI-agents for augmenting previously human-driven tasks, e.g., by providing inspiration during conceptual design to overcome fixation.

Project outcomes fall within these three gaps. First, a systematic review of literature on design thinking/design cognition topics (e.g., systems thinking, computational thinking, design creativity) is underway to define GDT by suggesting how these cognitive processes manifest during GD, how to best teach them, and to clarify the differences between GDT and other “design thinking” concepts. We have developed an Evolving Design Thinking (EDT) model to guide our review and represent how evolving technologies like generative AI drive the evolution of design thinking/cognition. Second, we have developed a text-based curriculum with CAD activities to teach basic GD principles. Student data (transcripts via think-aloud, design process and artifact data via a CAD platform) were gathered and used to refine the curriculum and explore students’ thought processes during GD. Third, software development includes the creation and continued support of Aladdin, a web-based open-source CAD/CAE platform which enables research and the creation of educational modules in renewable energy design contexts. Finally, project outcomes have been disseminated to collaborative academic (Lehigh University, Utah Tech, Hawaii Kapiolani Community College) and industrial partners (PTC Inc.) for broader impacts.

Authored by
John Zachary Clay (University of Texas at Austin), Dr. Molly H Goldstein (University of Illinois Urbana-Champaign), Dr. Charles Xie (Affiliation unknown), Dr. H. Onan Demirel (Oregon State University), and Dr. Zhenghui Sha (University of Texas at Austin)

This paper highlights a key approach, specifically developing and adopting a comprehensive strategic plan, that was taken to support curriculum innovations and enable cultural change. Over the past four years, the School of Civil and Environmental Engineering at the Georgia Institute of Technology has worked systematically to change the undergraduate civil engineering and environmental engineering programs. The goals were to enhance early engagement in the major, strengthen professional identity development, improve retention in the program, and enhance engineering efficacy and professional skills development. A key feature of the process included adopting and institutionalizing these goals in the School’s strategic plan. The strategic plan was developed over an 18-month period and included engagement of numerous stakeholders, including faculty, students, staff, advisory boards, alumni, and industry partners. Throughout the planning process, the School aimed for a balance of alignment with the College and Institute’s strategic directions, and charting a unique path forward germane to the disciplinary and professional contexts within Civil and Environmental Engineering. The final plan encompasses three umbrella areas: Community, Student Experience, and Discovery & Service with themes, objectives, and initiatives in each area. The initiatives under the area of Student Experience are strongly aligned with the objectives of the NSF-funded Revolutionizing Engineering Departments (RED) grant, as well as a Kern Entrepreneurial Engineering Network (KEEN) grant from the Kern Family Foundation. These initiatives include developing new courses, adopting a new curriculum, and deploying innovative pedagogy to support the program goals listed above. In addition, initiatives in the area of Community target the associated cultural changes, including incentivizing professional development, enhancing inclusiveness, and improving the sense of belonging. The effect of integrating these initiatives in the comprehensive strategic plan was to gain wide “buy-in” from faculty and staff and to effectively formalize the program goals into the shared vision for the future of the School.

Authored by
Dr. Donald R. Webster (Georgia Institute of Technology), Dr. Adjo A Amekudzi-Kennedy (Georgia Institute of Technology), and Dr. Robert Benjamin Simon (Georgia Institute of Technology)

Tutoring support is vital to eliminate knowledge gaps and achieve learning outcomes, while attaining instructional scalability to large class sizes common in STEM courses. However, determining which courses require additional tutoring support is challenging due to the lack of formal quantitative measurement tools, thus hindering the ideal provision of Teaching Assistant (TA) allocation. Herein, we develop a NetLogo framework for an Agent-Based Model (ABM) designed to simulate student progress and performance in a required Electrical and Computer Engineering (ECE) undergraduate course. It predicts and quantifies course outcomes under varying amounts of tutoring support via TA office hours. The ABM incorporates key parameters such as learners' attainment in previous semesters, grading schemata, and tutoring impact to predict the corresponding number of at-risk students. Meanwhile, the technical approach utilizes a NetLogo-based implementation of the ABM, which realizes a flexible, modular, and observable design.

The ABM is composed of three primary components: student agents which represent individual students along with their content proficiency, a course environment which encapsulates the grading scheme along with tutoring support parameters of the TAs available, and a feedback mechanism which enables the ABM to adjust its predictions based on the instructor's input. The technical results demonstrate that the ABM is capable of accurately predicting the number of students having significant risk of DFW, i.e. earning a course letter grade of D or F, or withdrawing (W). The results show that the ABM is robust and reliable in its predictions of DFW rate, whereas three out of four semester configurations analyzed indicated that the ABM’s predicted values bordered the 95% confidence interval. When measuring accuracy, test runs included course enrollment ranging from 70 to 121 students, commensurate with actual course delivery enrollments. The NetLogo model was parameterized towards attaining the worthy objective of lowering the DFW rate. Furthermore, to assist administrative decision making, it computes the monetary cost of tutoring per supported student. This new metric, known as Remediation Cost Per Supported Student (RCSS), delivers a quantitative measurement of cost-effectiveness for a course staffing configuration when considering the number of tutors paid and the number of students who received remediation. The model's performance is evaluated through a series of experimental scenarios, which involve varying student enrollment, grading schemes, and teaching assistant support levels using a dataset of previous course offerings.

Authored by
Mr. Paul Amoruso (University of Central Florida), Prof. Ivan Garibay (University of Central Florida), Dr. Joel Alejandro Mejia (University of Cincinnati), Dr. Laurie O Campbell (University of Central Florida), Dr. Florencio Eloy Hernandez (TAMUCC), and Dr. Ronald F. DeMara P.E. (University of Central Florida)

With higher and faster growing wages, STEM-related employment is key to rebuilding thriving communities. In the deindustrialized Midwest, the urban demographics often show higher percentages of those underrepresented in STEM, such as low socio-economic status (LSES) and underrepresented minorities (URM). These cities often have poverty rates double the national average, lower educational attainment, and the ‘brain drain’ problem. This creates barriers to developing and retaining a STEM workforce.

Funded through an NSF IUSE replication grant, the Community-Engaged Educational Ecosystem model (C-EEEM) targets deficits with which many deindustrialized cities struggle – engagement, knowledge, skill, capacity, and economic - while using an asset-based lens. Following the third replication year for the Community-Engaged Educational Ecosystem model (C-EEEM) in two other Midwestern regions, researchers have found that the C-EEEM demonstrated similar student and community outcomes in new contexts (self-efficacy, STEM-identity, place attachment). 1-5 Broadly, C-EEEM engages students in problem-based learning (PBL),6 with the community-issue becoming part of the curriculum and the community as the classroom; it delivers high-impact educational practices, 7-12 particularly for LSES and URM, while showing broader impacts in neighborhoods, industry, and attraction to the region.13 Despite fidelity to the implementation of the core elements of the C-EEEM, contextual differences at the institutional and community level meant differences in the learning environment. This paper provides a description of the distinctions of the C-EEEM delivery at the institutional level and the community level based on the implementation context, identifying asset-based adaptations. Using survey and reflection data, we explored contextual differences against student experiences and outcomes. Implications for adapting and sustaining the C-EEEM to different institutional and community contexts are then discussed.

Authored by
Dr. Danielle Wood (University of Notre Dame), Dr. Faisal Aqlan (University of Louisville), Dr. Jay B. Brockman (University of Notre Dame), and Dr. Hazel Marie (Youngstown State University - Rayen School of Engineering)

Quantum science and engineering will play a huge role in the 21st-century STEM workforce, as evidenced by national investments in quantum industries and the many interdisciplinary quantum information science and engineering (QISE) programs that have emerged in recent years. Science and engineering educators can play an important role in researching the best ways to prepare a thriving and diverse quantum workforce. Nuclear magnetic resonance (NMR) is one quantum technology that historically has had a multidisciplinary impact - having garnered five Nobel prizes across physics, chemistry, and medicine - and still serves as a crucial analytic and diagnostic tool in applied science and engineering industries. NMR uses the quantum mechanical properties of atomic nuclei in an external magnetic field to provide information about a sample’s chemical composition and structure. Magnetic resonance is used more broadly for manipulating quantum spins to encode information useful for biomedical imaging or quantum computation. Integrating NMR into the undergraduate science and engineering curriculum would help build the STEM workforce of the future, where a basic understanding of quantum physics will become a necessity in emerging 21st-century technologies.

We have received an NSF-IUSE grant and established an interdisciplinary and cross-institutional team to develop, assess, and disseminate modular lab-based materials that help integrate NMR across the undergraduate science and engineering curriculum. Along with our team’s expertise in research-based science educational pedagogies such as investigative science learning environment, process-oriented guided inquiry learning, and peer-led team learning - our materials also draw from the recent education research findings of the course-based undergraduate research experience model. Over the past three years, these materials have been developed and tested at both Sarah Lawrence College and the City College of New York and reviewed by an advisory board of NMR and science education specialists. Our materials have been designed to: (1) make use of current pedagogical best practices for an engaged and inclusive science learning environment, (2) provide students with class-based undergraduate laboratory experiences that introduce research skills and emulate experimental research in a lab, and (3) be easily adapted and adopted for use in a wide array of educational environments and courses in the science curriculum. We have evaluated these objectives through surveys, focus group interviews with students and instructors, and analysis of video recordings of different classroom implementations of the modules. Our research shows that students using our modules not only successfully master the content, they also: (1) spend over four times as much time sense-making using our modules as in a traditional lecture course, (2) demonstrate positive scientific identity shifts, and (3) make statistically significant gains in learning attitudes about science and self-assessed research skills. We see the integration of these modules into the undergraduate curriculum as a positive step towards the larger goal of expanding the pool of quantum-literate workers needed for the 21st-century STEM workforce.

Authored by
Dr. Merideth Frey (Sarah Lawrence College), Dr. Dedra Demaree (Blue Ridge School), David Gosser (Affiliation unknown), and Colin David Abernethy (Affiliation unknown)

Peer mentoring programs have become common on college campuses. Frequently, peer mentors are hired to work in writing centers or math learning centers to tutor students on learning. Peer mentors have also been integrated into courses such as with the Learning Assistant (Barrasso & Spilios, 2021) and supplemental instruction (Dawson et al., 2014) programs. It is common for university-based makerspaces to hire students to be peer mentors who can help those coming into the space accomplish their goals.

Unique to our program is hiring peer mentors (who are engineering majors) to support engineering students enrolled in a first-year design course taking place in a makerspace classroom. The course is structured to meet once a week for two hours. The typical course has 40 students and is led by a single instructor who is a member of the university engineering faculty. Thus, the student-to-faculty ratio in the space is 49 to 1. In the course, students work in small groups (usually four students) as they engage in assignments designed to help increase their knowledge of engineering. Some of the activities are scripted (e.g., circuit building), while others require the students to design and prototype a product following general guidelines. Thus, the students learn about engineering through design, prototyping, testing, refining, rebuilding, and retesting.

Given the high level of student engagement in activities in the course that they may not be familiar with (e.g., building prototype circuits using breadboards, using power tools), there is a need for additional learning support beyond what a single instructor can provide. Thus, the college hires a cadre of peer mentors (also undergraduate engineering students) to work with the students in the courses and support students in the makerspace classroom during open lab hours. The integration of peer mentors into the course has been taking place for several years. We are working with 15 undergraduate engineering majors this semester, preparing them to be peer mentors and researching how their mentoring experiences are influencing their professional development.

We recognize the potential for mentoring to benefit the mentors (Smith & Nadelson, 2016). However, there is a gap in the research detailing how peer mentors working with students in makerspace classrooms in undergraduate engineering may enhance their perceptions and knowledge of themselves as both professionals and engineers. To address this gap, we are gathering data from the peer mentors using both surveys and semi-structured interviews. Our survey contains ten free response items designed to elicit the thoughts of the peer mentors about how mentoring other students in the first-year design course taking place in the makerspace has influenced their knowledge and perceptions of themselves as developing into engineers.

We also developed our interview protocol to gather the narrative of the peer mentors to empirically document their lived experiences as mentors and the impact their mentoring experiences have on their professional and engineering identity development (Nadelson et al., 2017; Villanueva & Nadelson, 2017).

In our final report, we will detail the specific activities of peer mentors and how these activities enhanced their understanding of engineering, their thoughts about being a professional engineer, and their engineering and professional identities.

References

Barrasso, A. P., & Spilios, K. E. (2021). A scoping review of literature assessing the impact of the learning assistant model. International Journal of STEM Education, 8, 1-18.

Dawson, P., van der Meer, J., Skalicky, J., & Cowley, K. (2014). On the effectiveness of supplemental instruction: A systematic review of supplemental instruction and peer-assisted study sessions literature between 2001 and 2010. Review of educational research, 84(4), 609-639.

Nadelson, L. S., McGuire, S. P., Davis, K. A., Farid, A., Hardy, K. K., Hsu, Y. C., ... & Wang, S. (2017). Am I a STEM professional? Documenting STEM student professional identity development. Studies in Higher Education, 42(4), 701-720.

Smith, J., & Nadelson, L. (2016). Learning for you and learning for me: Mentoring as professional development for mentor teachers. Mentoring & Tutoring: Partnership in Learning, 24(1), 59-72.

Villanueva, I., & Nadelson, L. (2017). Are we preparing our students to become engineers of the future or the past. International Journal of Engineering Education, 33(2), 639-652.

Authored by
Dr. Louis S. Nadelson (University of Central Arkansas) and Dr. Pamela L Dickrell (University of Florida)

Earth Trek is a mixed reality educational game that aims to enrich geotechnical engineering education through gamified learning. The game is designed for undergraduate geotechnical engineering students as part of the Multiphysics-Enriched Mixed reality for Geotechnical Engineering (MERGE) platform. It combines virtual reality technology with experimentation, field exploration, and engineering design through a series of mini-games based on standard lab assignments. These enable students to learn key concepts in geotechnical engineering in a virtual environment. Students can access various testing tools (e.g. thermal conductivity measurements and direct shear tests) through the game missions. They can also conduct parametric studies in the virtual laboratory to understand soil properties under different geological conditions. Students can also perform parametric studies in a virtual lab to understand the efficiency of heat transfer in geothermal piles. The game allows students to intuitively grasp complex theoretical concepts and practical operations through visualization and simulation tools. By completing the game's tasks, students can earn points and update the appearance of their characters, increasing their motivation to learn. Implemented across multiple institutions, Earth Trek not only improves students' laboratory skills and geotechnical knowledge, but also integrates smart city design concepts and develops their metacognitive and problem-solving skills. Beyond classroom teaching, the game supports self-paced learning, promoting lifelong education and preparing students for careers in geotechnical engineering.

Authored by
LuoBin Cui (Rowan University), Dr. Ying Tang (Rowan University), and Mr. Chenchen Huang (Rowan University)

Engineering students taking dynamics, vibrations, and control theory courses struggle to acquire a deep understanding of complex engineering concepts due to their highly mathematical nature, lack of prior knowledge, limitations of large lectures, limited resources preventing the use of commercially available lab equipment, and lack of innovative teaching tools that could be utilized to enhance learning.
Hands-on experiences are especially crucial for engineering students to help them bridge the gap between theory and application. However, commercially available laboratory equipment utilized in the mechanical vibrations and control labs are bulky and expensive. Further, one may question issues of accessibility and equity for students. The inability to work at their own pace, repeat experiments later, and develop adequate knowledge and experience from troubleshooting the equipment and resolving problems is lacking with these turnkey solutions.
Since the pandemic, digital learning tools have become necessary complements, not just accessories, to support student engagement and learning. They offer advantages to increase student learning that traditional laboratory environments cannot, including increased accessibility for students with mobility impairments. Although some open-source virtual labs are available for science courses, there are limited available virtual labs for undergraduate engineering courses, including vibrations and control theory courses.
In our previous NSF IUSE Level I project (Award #2002350), we developed seven vibrations and one control lab equipment that are low-cost and 3D-Printed along with their learning activities. Learning activities for each tool use a modified POGIL (Process Oriented Guided Inquiry Learning) approach grounded in constructivist theory. They include an orientation to why content was important to learn, clear learning objectives, performance criteria for the learning process, exploratory prerequisite questions to activate prior knowledge, instructions for working with the devices, final critical thinking questions. Additionally, in a separate project, we developed open-source virtual labs for mechanical vibrations, control theory, and associated laboratories to support student learning of vibrations and controls concepts.
In our current NSF IUSE Level II project, we are developing additional learning experiences for students that leverage multiple representations of knowledge by combining (1) hands-on and (2) virtual simulation lab or demonstration experiences with (3) AI support embedded within a robust (4) process-oriented learning activity. These elements of the learning experience create complete learning packages that will advance student learning, enhance accessibility by increasing hands-on and virtual simulation learning experiences, and provide an affordable alternative to lab equipment. This work aims to broaden the impact on learning vibrations and controls across a diverse set of learners and learning contexts at multiple institutions.
The project has produced three new learning experiences based on new lab equipment designs and one prior design is adapted as a new classroom exercise, along with their virtual labs. The adapted design is being used in a Fall 2024 course, and the other devices are being tested for usability in multiple settings at two institutions. Data for these implementations is currently being collected. Results will address the impact on students' perceived capabilities as learners and engineers and on the achievement of intended outcomes.

Authored by
Dr. Ayse Tekes (Kennesaw State University), Dr. Tris Utschig (Kennesaw State University), and Coskun Tekes (Affiliation unknown)

Learning is a lifelong process exercised within and beyond the classroom, and a vital skill in almost all technical professions. Engineers, in particular, are impacted by rapidly evolving technologies and practices that require continuous learning and adaptation long after their training and the initial transition into their professional careers. However, despite the critical role of learning in their academic success and profession, engineering students experience academically rigorous and challenging courses with minimal emphasis or conscious focus on learning strategies that power effective learning.
Often-used learning strategies such as rereading, highlighting, repetition, and memorization are intuitive for many students, yet do not facilitate the higher-order thinking required to solve difficult engineering problems and understand concepts. As a result, weak learning strategies are an important factor in why students often initially struggle in their courses to reach the level of concept mastery and the ability to synthesize, apply, and evaluate problems using engineering principles; and why they may continue to struggle as lifelong learners. The consequences of ineffective learning are already transcending in students’ academic careers: slowing their curricular progress and affecting their ability to adjust to university life, build self-regulatory skills, and gain a sense of control over their learning experiences.
These challenges stem from the fact that the engineering curriculum has traditionally emphasized teaching content and material while assuming that students can manage their own learning. Integrating self-regulated learning skills into the engineering curriculum holds great potential to promote students’ learning skills and growth mindset. The aim of this NSF IUSE-funded project is to develop, implement, and evaluate course-integrated self-regulated learning skills training interventions.
One challenge of such interventions is to have a thorough understanding of how engineering students are learning, and what learning strategies and learning behaviors they most engage with, so we can develop a targeted intervention to promote engagement of effective learning strategies/ behaviors and move away from ineffective learning strategies and behaviors. Although there is a range of literature that developed surveys and empirical studies to understand various components of students’ self-regulated learning skills, such as motivation, learning strategies inventory, metacognition, and growth mindset, there is no single survey and study that covers all these aspects. In addition, the majority of the empirical studies about learning strategies' utility were conducted in psychology, math, physics, and biology classes, with limited data on engineering students. Thus, as the first step of our project, we developed a complete set of survey questions to understand all three dimensions of students’ self-regulated learning (growth mindset, cognitive strategies, metacognition), with some questions adapted from existing validated surveys, and some newly developed questions. Using the convenience sampling method, in a pilot study, the surveys were sent to 831 engineering students in five mechanical and electrical engineering courses, which were taught by the four instructors who are NSF project team members. In this paper, we demonstrate the survey results with a general descriptive analysis for the general students' sample, as well as for various demographics groups, such as gender, first-generation college students, transfer students, and other underrepresented groups.

Authored by
Dr. Huihui Qi (University of California, San Diego), Celeste Pilegard (University of California, San Diego), Dr. Minju Kim (University of California, San Diego), Dr. Saharnaz Baghdadchi (University of California, San Diego), Prof. Curt Schurgers (University of California, San Diego), Dr. Alex M. Phan (University of California, San Diego), and Dr. Marko Lubarda (University of California, San Diego)

This NSF-funded Division of Undergraduate Education (DUE) Improving Undergraduate STEM Education (IUSE) project aims to integrate sociotechnical issues into Introduction to Circuits, typically the first course in electrical engineering (EE) for undergraduate students. To prepare graduates for the real-world problems, which are interdisciplinary and involve complex social impacts, instructors must help students address the sociotechnical nature of engineering. However, many engineering instructors are unsure how to do this. In this project, we aim to make it easier for them by developing sociotechnical modules with detailed teaching guides for the Introduction to Circuits course, each connecting fundamental circuits topics to larger social issues. In year 2, we implemented our first two modules at different institutions, developed and administered a survey assessing students’ perceptions about sociotechnical issues in engineering, recruited and facilitated a cohort of graduate students from across the USA to develop more modules, and presented our work at several international venues.

Authored by
Dr. Susan M Lord (University of San Diego) and Dr. Cynthia J. Finelli (University of Michigan)

The University of X's Freshman Year Innovator Experience (FYIE) program, hosted at a Minority Serving Institution (MSI), seeks to improve the first-year experience for new students by nurturing essential academic success skills. Specifically tailored to freshman mechanical engineering students, the program aims to equip them with self-transformation skills to navigate through the amplified academic and professional obstacles brought about by the COVID-19 pandemic. Participants of FYIE engage in two concurrent courses: Introduction to Engineering (Course A) and Learning Frameworks (Course B). In Course A, students undertake a 6-week engineering design project, while in Course B, they work on a 6-week academic career path project. Throughout these simultaneous projects, time-bound interventions highlight the similarities between the engineering design process and the academic career pathways project. The main goal is for students to recognize that the design thinking skills acquired in the engineering design process can be applied to resolve their academic career challenges. The FYIE program was initiated as a pilot in the spring semester of 2023, with instructors from Course A and B introducing the parallel projects. The implementation has continued through the fall 2023, spring 2024, and ongoing fall 2024 semesters, introducing improvements at every iteration, with adjustments made to the parallel projects and the identification of intervention points for self-transformation through analogy. The creators of the program will present the outcomes from the pilot implementations and address the obstacles and future work. This proposed endeavor is aligned with the continuous mission of the College of Engineering and Computer Science (CECS) at the University of X, which includes: 1) increasing the number of STEM degrees granted to Hispanics, 2) promoting the participation of women in STEM-related fields, and 3) enhancing persistence and self-confidence in STEM fields amidst the challenges posed by COVID-19. The project is supported by the NSF award 2225247.

Authored by
Dr. Noe Vargas Hernandez (The University of Texas Rio Grande Valley), Dr. Javier A. Ortega (Affiliation unknown), Dr. Arturo A Fuentes (The University of Texas Rio Grande Valley), Dr. Eleazar Marquez (The University of Texas Rio Grande Valley), and Dr. Pierre Lu (The University of Texas Rio Grande Valley)

This paper reports on a year-long faculty professional development program for STEM faculty teaching at a northeast community college and for area high school science and mathematics teachers. The program focused on culturally responsive and inclusive teaching practices and consisted of a 3-week virtual summer institute and mentoring on use of inclusive practices during the academic year that followed. Our research questions were: Does participation impact faculty beliefs and self-efficacy in using such practices? Do students who take classes with faculty trained in culturally responsive and inclusive practices show higher levels of academic achievement in STEM?
Data was collected from faculty participants using a survey at three timepoints that measured self-efficacy and awareness of using cultural culturally responsive pedagogy. Institutional research provided a comparison of student academic achievement in college STEM courses taught by participants vs. nonparticipants.
A total of 18 college and 10 high school faculty participants took part in the first two Institutes. Results in this report include data collected 1-3 terms post-Institute from the first two cohorts of college faculty participants. Findings show faculty confidence was sustained between the end of the summer institute and the end of the follow-on semesters. Preliminary student evidence showed a higher rate of achievement of Black and Latinx students in STEM course sections taught by participants from Cohort 1 compared to sections taught by non-participants. That is, the percentage of Black and Latinx students achieving a grade of C- or higher in STEM courses taught by cohort 1 faculty participants was higher post-institute. In all other (non-participant) college STEM courses during this same timeframe, the percentage of students achieving a grade of C- or higher remained largely unchanged.

Authored by
Dr. Bernadette Sibuma (Massachusetts Bay Community College), Jayne Ryczkowski (Massachusetts Bay Community College), and Meredith Watts (Affiliation unknown)

This NSF project (DUE2215989) aimed to deepen our understanding of effective instructional practices in middle and upper-level engineering classes that enhance student learning. We explored how faculty members' philosophical beliefs are applied in practice within specific disciplinary contexts. Research on teaching methods in middle and upper-level engineering classes has been limited (Pendergast et al., 2020), despite these courses being recognized as particularly challenging and essential for students' mastery of specialized knowledge and skills in their chosen fields (McGough Spence et al., 2022). Our key research questions were what student-centered teaching methods are used by exemplary engineering faculty to promote knowledge building, and how do these align with their teaching beliefs, and how can a sustainable community of practice spread these methods across departments to improve student learning. We identified and successfully recruited a diverse set of participants who were recommended by their department heads as exemplary teachers. We employed a participatory action research (PAR) approach using the Postsecondary Instructional Practices Survey (PIPS), semi-structured interviews, classroom observations, course documents, course consultation, and focus groups across multiple phases. The survey and classroom observations revealed common effective strategies used by instructors, such as guiding students through key topics, connecting content to their lives and careers, providing immediate feedback, and encouraging peer interaction. Besides, instructors often structured classes with clear introductions, visual aids, and real-world examples. Unique practices observed included debates, involving students in decisions, relating content to other courses, “quiet” problem-solving, and making intentional mistakes as teaching tools. In the consultation projects, common recommendations for course improvements included further diversifying teaching strategies, revising assessments, refining syllabi, aligning course materials with accreditation standards, supporting team dynamics, and reducing plagiarism through a variety of evidence-based practices aimed at improving student engagement and learning outcomes. During the summer workshop, participants shared positive experiences from the course consultation projects and expressed interest in implementing several recommended improvements in their upcoming classes. Participants reported that the summer workshop provided a valuable opportunity to explore effective student engagement concepts and technologies, connect with like-minded educators, discover adaptable ideas for diverse teaching contexts, navigate the transition between small and large classes, and develop strategies for managing larger class sizes based on data-supported practices. In future work to extend the impact of this grant beyond the funded timeline, we plan to conduct a series of workshops to share exemplary teaching practices used by our participants across Virginia Tech’s College of engineering.

References:

Pendergast, D., Main, K., & Bahr, N. (2020). Teaching Middle Years: Rethinking curriculum, pedagogy and assessment. Routledge.

McGough Spence, C., Kirn, A., & Benson, L. (2022). Perceptions of future careers for middle year engineering students. Journal of Engineering Education, 111(3), 595–615. https://doi.org/10.1002/jee.20455

Authored by
Shabnam Wahed (Virginia Polytechnic Institute and State University), Dr. Nicole P. Pitterson (Virginia Polytechnic Institute & State University), Dr. Jennifer "Jenni" M Case (Virginia Polytechnic Institute and State University), and Dr. David B Knight (Virginia Polytechnic Institute and State University)

South Dakota Mines (SDM), a STEM-focused campus, is uniquely situated at the foothills of the Black Hills. The region is geologically rich with its abundant clays and shales. Within a ~50 mile radius one finds urban, forest, ranching and tribal lands. Consequently, the local high school student population is equally diverse, with a relatively high percentage of first-generation college, rural and Native American (Lakota) students. Douglas High School has a student population that represents this diversity. In this regard, SDM hosted a high school STEM teacher from Douglas High School through a NASA Research Experience for Teachers as part of a research internship. The teacher conducted research on ceramic glaze formulations using 100% local materials from the Black Hills. The teacher also successfully modified the formulations of local minerals to develop a ceramic coating (glaze) and will translate this understanding to her high school Chemistry classes. The translation will cover kinesthetic topics that include utilizing the potter’s wheel, kilns, and the Scanning Electron Microscope at SDM. Finally, the research findings, and translation to the curriculum will be mapped to meet the SD Department of Education Standards.

Authored by
Dr. Katrina Donovan (South Dakota School of Mines and Technology) and Dr. Jon J Kellar (South Dakota School of Mines and Technology)

This NSF IUSE research collaboration between the University of Illinois Chicago and the University of Texas at San Antonio focuses on curriculum design for early and mid-level engineering courses (e.g., first-year, sophomore, junior level) that emphasize engineering design. The partnership led to the creation of a course that integrates critical consciousness and Freire’s dialogic principles into the teaching of the engineering design process. This approach addresses two significant gaps in engineering education: (1) the shortage of mid-level design courses and (2) the need for a contextualized engineering curriculum.

Since spring 2023, both institutions have offered this course twice, attracting students from their respective engineering colleges. The course is open to any student that meets the course pre-requisites, a standard practice in engineering. While the institutions serve slightly different student populations and implement the course with some variations, the outcomes have been consistent. These include increased student understanding of the social, cultural, economic, and political aspects of design, a stronger sense of engineering identity through project development, a focus on community-centered design, and improved peer-community building through the dialogic practices used in the course. In this poster, we will present the main framework developed for the course, the dialogic principles used during implementation, and the impact of these on the development of critical consciousness among students.

This project offers valuable insights for educators interested in incorporating dialogic methods into their engineering curriculum and for those who aim to enhance design knowledge by integrating contextualized perspectives.

Authored by
Dr. Renata A Revelo (The University of Illinois at Chicago), Dr. Joel Alejandro Mejia (University of Cincinnati), Julio C Mendez (University of Illinois Chicago), and Luis E Montero-Moguel (The University of Texas at San Antonio)

The exponential rise of Artificial Intelligence (AI) hardware technologies, fueled by rapid data science advancements, has reshaped the computing landscape, transforming machine learning from a theoretical pursuit into a driving force behind real-world innovation. From the early days of basic processors to today’s GPUs, TPUs, and specialized AI accelerators, hardware breakthroughs have continuously redefined the boundaries of scalability and application. Our project, funded by the NSF Improving Undergraduate STEM Education (IUSE) program, began in 2022 with an ambitious vision: to create a gamified curriculum for teaching hardware fundamentals using Field-Programmable Gate Array (FPGA) platforms. As the project evolved, we expanded to include AI Internet of Things (AIoT) applications, and most recently, we’ve sharpened our focus on intelligent embedded systems. Central to this initiative is our commitment to exposing engineering students to these cutting-edge technologies early in their higher education, helping them make empowered career choices while ensuring the workforce is prepared to keep pace with accelerating technological advancements. By adapting swiftly, our curriculum is equipping students to stay ahead and lead the next wave of innovation.

Over the past two years, the curriculum has been iteratively refined and implemented at a large public R1 university in the Southeastern US, following Design-Based Implementation Research (DBIR) principles. Grounded in equity-centered practices informed by Culturally Relevant Pedagogy (CRP) and Universal Design for Learning (UDL), the curriculum combines inquiry-based and experiential learning in a Project-Based Learning (PBL) format. This approach effectively builds students' understanding of key hardware concepts like binary numbers, Boolean logic, and sequential circuits while also integrating AI to gather data from IoT devices and solve real-world embedded systems challenges. The use of accessible FPGAs and IoT boards provides multiple entry points, offering hands-on learning that fosters self-efficacy, particularly for neurodiverse learners. This strategy ensures students gain both foundational knowledge and the confidence to navigate the rapidly evolving field of intelligent embedded systems.

This paper and poster presentation will explore the evolution of this curriculum, enriched by data collected from Fall 2023 to Fall 2024 on students' career choices, identity, interest, outcome expectations, and self-efficacy in hardware engineering, AIoT, and intelligent embedded systems. To gauge participants' perceptions, we administered both pre and post-surveys, conducted focus groups, assessed conceptual learning, and conducted interviews with 17 students in Fall 2023 and 17 students in Fall 2024. These mixed methods provided a nuanced understanding of their experiences and perspectives regarding the curriculum. Offered as an elective in the Electrical and Computer Engineering (ECE) department and open to all engineering majors, the program has attracted a diverse student body, both in terms of academic backgrounds and demographics, with each iteration showing an increase in race and gender identity diversity. These results demonstrate that the curriculum’s inclusive, hands-on approach resonates with a broad range of students, positioning them to thrive in the growing field of intelligent embedded systems. The findings carry significant implications for educational practice, highlighting the value of inclusive, experiential learning environments in attracting and retaining diverse talent within rapidly advancing technological fields.

Authored by
Dr. Andrea Ramirez-Salgado (University of Florida), Dr. Swarup Bhunia (Affiliation unknown), Dr. Pavlo Antonenko (Affiliation unknown), Woorin Hwang (University of Florida), Christine Wusylko (University of Florida), Ms. Yessy Eka Ambarwati (University of Florida), Tanvir Hossain (The University of Kansas), Tamzidul Hoque (The University of Kansas), and Rohan Reddy Kalavakonda (University of Florida)

Engineering student persistence remains low in undergraduate universities across the United States (50-60% graduate within 6 years [1]), especially for underrepresented minority (URM) groups (~40% [2]), with the largest dropout rate occurring in the first year (18.5%, [2]). Research has revealed that students decide to leave engineering for many interrelated reasons (e.g., [3], [4], [5], [6]), making it challenging to intervene and help many students at the same time. However, recent developments in machine learning (ML) technologies and motivation theory present a unique opportunity for making substantial, equitable change: identifying individualized factors for intervention to promote persistence. Predictive ML models such as neural networks, random forest, and Bayesian networks can be used to identify students at risk of leaving engineering, and ML explanation methods such as SHAP and LIME can be used to indicate reasons behind these predictions. These tools can be applied to engineering persistence data to predict which students might leave and why, such that interventions could be targeted to meet individual student needs.
Our funded NSF-IUSE project was awarded to test and compare the effectiveness of the current cutting-edge ML models in predicting engineering attrition and identifying individualized targets for intervention. Over the course of three years, we will evaluate the accuracy and consistency of three common ML models using 5 years of retrospective data from a large southeastern university, and then test the generalization of results on data from a similar institution.

Thusfar, we have completed Phase 1 of the project, which included data preprocessing and preliminary predictive model testing. Our work has revealed several important considerations for using ML to predict engineering persistence. First, different models require different preprocessing styles for categorical data such as race and gender [BLIND]. Specifically, the assumptions behind naïve Bayesian models do not agree with the standard one-hot-encoding procedure often used in neural network and random forest models.

This poster will present our more recent findings. We found that Undersampling and Synthetic Minority Over-sampling TechniquE (SMOTE) are two techniques that, when used together, effectively handle the imbalanced data intrinsic to first-year persistence. Random undersampling consists of randomly removing samples of the majority class (i.e., retained students) in the dataset [7]. Meanwhile, SMOTE is an over-sampling technique in which synthetic samples are created for the minority class (i.e., withdrawn students [8]). They are combined to balance the dataset and enhance the performance of the ML classifiers. Our results show that these methods successfully improve model F1 scores (a standard ML metric that will be defined in the paper) by 16% and are therefore worth using when predicting engineering persistence.

References
[1] PCAST President’s Council on Advisors on Science and Technology, “Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics,” Washington, DC: Office of the President, 2012. doi: 10.1080/10668921003609210.
[2] B. L. Yoder, “Engineering by the numbers: ASEE retention and time-to graduation benchmarks for undergraduate engineering schools, departments and programs,” American Society for Engineering Education, 2017, Accessed: Jan. 05, 2023. [Online]. Available: https://ira.asee.org/wp-content/uploads/2017/07/2017-Engineering-by-the-Numbers-3.pdf.
[3] V. Tinto, Leaving college: Rethinking the causes and cures of student attrition. ERIC, 1987.
[4] V. Tinto and J. Cullen, “Dropout in Higher Education: A Review and Theoretical Synthesis of Recent Research.,” Washington, D.C. Office of Planning, Budgeting, and Evaluation, Department of Health, Education, and Welfare Contract OEC-0–73–1409, 1973.
[5] J. Bean and S. B. Eaton, “The psychology underlying successful retention practices,” J Coll Stud Ret, vol. 3, no. 1, pp. 73–89, 2001, doi: 10.2190/6r55-4b30-28xg-l8u0.
[6] C. P. Veenstra, E. L. Dey, and G. D. Herrin, “A model for freshman engineering retention,” Adv Eng Educ, vol. 1, no. 3, 2009.
[7] H. He and Y. Ma, Imbalanced learning: Foundations, algorithms, and applications. 2013. doi: 10.1002/9781118646106.
[8] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, 2002, doi: 10.1613/jair.953.

Authored by
Arinan De Piemonte Dourado (University of Louisville), Christian Zuniga-Navarrete (University of Louisville), Alvin Tran (University of Louisville), Luis Segura (University of Louisville), Dr. Xiaomei Wang (University of Louisville), and Dr. Campbell R Bego (University of Louisville)

The choice of academic major is a critical juncture in a student’s academic and professional journey, however, this selection is frequently made uninformed and under uncertainty, leading to some declared major students having an increased risk of attrition when compared to undecided students[1]. A major decision is often a multifaceted and intricate process that is heavily influenced by different behavioral, sociological, and economic factors such as personal interests, familial background, and financial considerations [1,2]. Despite these findings, a gap remains to explore why undergraduate engineering students choose a particular engineering major.

To address this topic, this NSF IUSE funded project aims to better understand engineering students' decision-making process when selecting their academic major across two large public land-grant universities. The study utilizes an intervention of an online major exploration tool followed by a questionnaire to collect quantitative and qualitative data.

We have previously reported on the development of our survey and interview results [3,4]. The results and understandings obtained from previously published results were used as the basis for designing a new major exploration tool. The online major exploration tool was made available to students enrolled in ENG 100: Introduction to Engineering on both campuses in their first semester. After using the tool, students were provided a questionnaire to provide feedback and insights on the tool.

Results from the post-tool use survey indicated that 80% of students were satisfied with the majors that the tool presented as a match and 50% said that they felt more confident in their major choice as a result of using the tool. Open-ended feedback included students’ appreciation of being able to compare majors side-by-side, the ability to consider specific curricular differences between majors like the number of math courses, access to linked-in profiles from graduates of the program to explore their career paths, and students’ desire for more comparisons of majors, even outside of engineering. The findings will allow us to better understand students’ considerations as they go through the critical process of choosing an academic major.

Authored by
Dr. Jennifer R Amos (University of Illinois at Urbana - Champaign), Prof. Houshang Darabi (University of Illinois Chicago), and Nikith Rachakonda (The University of Illinois at Chicago)

This project aims to develop engineers who are not only proficient in technical skills but also deeply attuned to ethical considerations and global challenges. Modern engineers have an essential role in shaping society, driving innovation, and addressing complex, multidisciplinary problems that impact communities. However, there remains a critical gap in how engineering students engage with the broader ethical and societal dimensions of their work. Prior research, has highlighted that undergraduate engineering students often feel detached from the social and cultural responsibilities of their profession. This project directly responds to calls for more effective strategies to foster a sense of moral agency and societal engagement in future engineers.

The project is funded by the National Science Foundation’s Improving Undergraduate STEM Education (IUSE) program and is piloting a new pedagogical framework that combines established reflective practice techniques from cognitive design theory with novel methods of coupled ethical-epistemic analysis from the field of philosophy. These strategies will be integrated into both classroom settings and research environments across multiple engineering disciplines to assess their effectiveness. The primary objective is to explore how these combined approaches influence students’ ability to engage with the ethical dimensions of engineering and cultivate moral agency.
The exploratory nature of this study involves three key tasks. First, it examines whether participation in undergraduate research employing coupled ethical-epistemic analysis contributes to the development of moral agency among engineering students. Second, it investigates how implementing coupled ethical-epistemic pedagogy in classroom settings impacts students’ ethical engagement. Finally, the study assesses how variations in teaching methods and subject matter influence the effectiveness of this approach. To date, we have completed two semesters of undergraduate research employing coupled ethical-epistemic analysis and one semester of classroom data collection. Our results show positive changes in student scores on ethical principle identification, recognizing the ethical dilemma, assessing viewpoints across stakeholders, determining coherence across ethical principles to identify potential resolution, and reflective analysis of the proposed solution after completing a REU experience.

Through this initiative, we seek to contribute new insights into engineering ethics education by filling gaps in current pedagogy and identifying mechanisms that can enhance students' ability to address society-relevant issues. By presenting this work as a project-in-progress, we aim to generate discussion and feedback from the ASEE community, which will inform the next stages of implementation and further refine our iterative research design to better support student learning outcomes.

This work aligns with the NSF's focus on improving the quality of undergraduate education and preparing students to meet the complex demands of the engineering profession with both technical expertise and ethical insight.

Authored by
Dr. Caitlin A Grady (The George Washington University)

This NSF-funded Division of Undergraduate Education (DUE) Improving Undergraduate STEM Education (IUSE) project conducts three studies using mixed research methods to understand the academic success of STEM college students with ADHD. Studies 1 and 2 are complete, while Study 3 is ongoing. Study 1 is a quantitative analysis examining the relationships between pre-college factors, college experiences, and the academic success of college students with ADHD. The study found that the relationship between an ADHD diagnosis and first-year grades was partially mediated by academic adjustment. Study 2 is a scoping literature review exploring the individual student experiences of STEM college students with ADHD. After synthesizing 39 articles, the study identified key characteristics and findings about the individual student experience, highlighted potential gaps in the literature, and proposed opportunities for future research.

Study 3 is an ongoing qualitative investigation studying the effects of instructional practices (including lecture and active learning) on the individual student experience. We conducted 9 focus groups and 6 individual interviews, with a total of 26 unique engineering college students from a research-intensive institution located in the Midwest. Our study is guided by the Individual Student Experience component of Terenzini & Reason’s (2005) College Impact Model.

We used inductive and deductive coding approaches to analyze students’ classroom experiences, academic adjustment, and sense of belonging. In the first round of coding, we applied a deductive approach to label codes and refine the codebook. We coded 1,001 segments as classroom experiences (with sub-categories of instructional practices and student response), 268 segments as academic adjustment (with sub-categories of academic transitions and study skills), and 214 segments as sense of belonging (with sub-categories of classroom belongingness and engineering belongingness). In the second round of coding, we are using an inductive approach to allow emerging themes and sub-themes within each sub-category and identify the underlying patterns.

Preliminary results provide some insights about the classroom experiences of engineering college students with ADHD who participated in our study. For instance, the students generally prefer active learning classes over lecture-based classes, and they favor instructional practices that provide more structure, such as mandatory attendance and homeworks with fixed deadlines. Participants also reported that asking questions in class and having professors who knew them by name motivated them to learn. They also shared that they were more likely to learn if they could feel the instructors’ efforts and passion.

The overarching goal of this project is to understand the relationship between individual student experiences and instructional practices to create more inclusive learning environments for engineering college students with ADHD. The next steps include synthesizing findings of all three studies, and providing comprehensive suggestions to better support engineering instructors and educators.

Authored by
Musabbiha Zaheer (Affiliation unknown), Nolgie O. Oquendo-Colón (University of Michigan), Xiaping Li (University of Michigan), and Dr. Cynthia J. Finelli (University of Michigan)

The National Science Foundation (NSF) award (2206952) establishes a new Research Experiences for Teachers (RET) site, enhancing their knowledge and skills in advanced manufacturing/robotics at Bowling Green State University (BGSU). The primary objective of this project is to play a transformational role in preparing future leaders in advanced manufacturing by instilling advanced manufacturing/robotics research experience within STEM educators through six-week summer workshops dedicated to hands-on research projects/experiences. In these workshops, participants engaged with highly qualified researchers using cutting edge robotics technology and augmented by industry access. The research projects focused on contemporary advanced manufacturing topics including modern sensors and actuators, advanced robot programming, CNC programming, CAD/CAM, 3D printing, and e-factory. At the end, participating educators translated their research experiences and knowledge into classroom practice. As part of this research experience, participating educators developed working robots/models, instructional modules, and course materials that they used in their classrooms and share with other educators at their institutions.
The project cements the partnership among BGSU, local high schools, and community colleges to address the common need of producing STEM graduates in advanced manufacturing area. BGSU RET site provided research experience to 28 regional high school and community college educators during 2023-2024. During the 6-week summer workshops, these 28 educators conducted advanced manufacturing research at BGSU eFactory lab and developed curriculum modules for their students. After completion of the summer workshop, many of these educators implemented their curriculum modules at their respective institutions. They will continue to implement these modules in the future and create a sustained wave of awareness among future students in the U.S. manufacturing heartland.

Authored by
Dr. Md B. Sarder (Bowling Green State University)

The NSF-funded Research Experiences for Undergraduates (REU) program on Inclusive Innovation in Medical Devices at the University of Massachusetts Lowell aims to engage undergraduate students in cutting-edge, multidisciplinary research at the intersection of engineering, biology, and medicine. This program provides hands-on research opportunities in biomedical engineering while simultaneously addressing the broader impact of societal needs in healthcare.
The REU program's core objective is to empower students to tackle real-world healthcare challenges by designing and innovating medical devices. Throughout the ten-week summer program, participants are exposed to both laboratory research and professional development workshops, equipping them with technical expertise and communication skills. Key research projects include mobile phone-based imaging for diagnostics, biomaterial development for tissue engineering, and computational modeling of respiratory devices. Key professional development events include research a jump start seminars, technical writing workshops, communication workshops, microagression training, and a final poster presentation event.
Preliminary findings indicate significant growth in students’ research self-efficacy, with a notable increase in their ability to discuss research at professional meetings and conferences. Additionally, participants expressed heightened confidence in pursuing graduate studies in STEM fields, further solidifying the program’s role in shaping future biomedical innovators. The program’s emphasis on inclusion has proven essential in cultivating a diverse talent pipeline ready to address the healthcare needs of tomorrow.

Authored by
Dr. Yanfen Li (University of Massachusetts Lowell) and Walfre Franco (Affiliation unknown)

Research experiences for undergraduates (REUs) are crucial in shaping academic and professional development. Engaging in research allows students to apply theoretical knowledge to real-world problems, fostering critical thinking, problem-solving, and analytical skills. Moreover, students benefit in many ways by living in the university dorms for 10 weeks, exploring a new university town, making new friends, and taking on new life experiences.

The purpose of this NSF Grantees Poster is to provide a holistic overview of undergraduate student researcher perceptions related to participating in an NSF REU Site program, titled “NSF REU Site: Growing Entrepreneurially-Minded Undergraduate Researchers with New Product Development in Applied Energy.” This REU integrated the strengths of academic applied research, including a solid theoretical foundation and rigorous scholarship, with key business practices such as real-world customer discovery and the creation of viable business models. The intention of the REU was to equip students with an entrepreneurial mindset, expanding their research toolbox and skillset.

At the end of the 10-week REU program, participants completed a final reflection responding to the following questions:
1. Entrepreneurial Mindset: The entrepreneurial mindset is defined as “the inclination to discover, evaluate, and exploit opportunities.” What are the 3 most important things you learned about an entrepreneurial mindset (or entrepreneurship) by participating in the REU program?
2. Research Skills: Identify the top three research skills gained while participating in the REU program.
3. Advisor Research Lab: What did you like about working with your advisor? What opportunities for improvement can you recommend?
4. Connect to Real World: What skills did you learn that are important for engineers conducting research in the real world? Please consider both professional skills (e.g., communication, collaboration, etc…) and context specific skills (e.g., topic area).

Thematic analysis was conducted on the reflections using NVivo. Findings and lessons learned are provided.

Authored by
Dr. Lisa Bosman (Purdue University) and Rhea Dutta (Affiliation unknown)

Early involvement in engineering research has proven to be a highly effective way to inspire undergraduate students to pursue advanced studies or research-intensive careers. By engaging students in real-world, hands-on research projects, they not only sharpen their problem-solving skills but also develop the intellectual independence needed to tackle complex engineering challenges. These benefits are amplified when the research experience is multidisciplinary, allowing students to engage with topics beyond the confines of their chosen major. Moreover, participation in a collaborative cohort—where continual interactions and shared learning experiences occur—helps foster a sense of community and shared purpose, further enhancing the learning process. This paper presents the outcomes and impacts of a unique undergraduate research program conducted collaboratively between Oklahoma State University, Stillwater, and the University of Alabama in Huntsville. What sets this program apart is its fusion of engineering and engineering technology disciplines, its blend of applied and fundamental research, and its focus on multidisciplinary topics such as human safety, fire protection technology, mechanical engineering technology, electrical engineering, and artificial intelligence. The program engages students from sophomore to senior levels, offering them a chance to explore various research methodologies and work on projects that span multiple fields of engineering. This exposure helps them cultivate a comprehensive understanding of engineering systems and their real-world applications. In this paper, we will delve into the structure and activities of the Research Experiences for Undergraduates (REU) program, discussing its various components as well as the educational and research outcomes it has produced. A central theme of the program is its focus on multidisciplinary research, which ranges from technical fields such as fire protection and mechanical engineering technology to more advanced areas like electrical engineering and artificial intelligence. This breadth of topics ensures that students are equipped with a wide range of skills, from analytical problem-solving to creative thinking, as they learn to approach engineering challenges from multiple perspectives. Additionally, the program’s emphasis on cohort-building activities plays a crucial role in shaping the students’ experiences. By promoting collaboration among students from different disciplines, the program encourages the cross-pollination of ideas, mutual learning, and the development of soft skills such as communication, teamwork, and leadership. The interactions fostered within the cohort help students build a network of peers who share similar academic and career aspirations, strengthening their commitment to research and professional development. The paper will also present the results of both formative and summative assessments of the program, highlighting its impacts on student learning, skill development, and long-term career trajectories. By examining these outcomes, we demonstrate how this collaborative and multidisciplinary research program has successfully nurtured the next generation of independent researchers and engineering leaders, equipping them to meet the challenges of an increasingly complex and interconnected world.

Authored by
Dr. Avimanyu Sahoo (The University of Alabama in Huntsville) and Dr. Haejun Park (Oklahoma State University)

This paper examines the impact of a National Science Foundation Scholarships in Science, Technology, Engineering, and Mathematics (NSF S-STEM) Program at a large, Minority-Serving institution in the western U.S. Despite growing efforts to diversify STEM fields, underrepresented minority (URM) students continue to face significant challenges in persistence and success. This scholarship program addresses these challenges by providing financial support, faculty and peer mentorship, and skills development opportunities to academically talented and low-income URM STEM students. This study evaluates how participation in the program enhances key noncognitive skills, such as students' sense of belonging, leadership and collaboration skills, and science identity, which are critical to STEM persistence. Using both survey and university-based data among the 47 participating scholars, results reveal that program participants report strong levels of sense of belonging, high efficacy in leadership and collaboration skills, and strong science/math identities. Additionally, compared to university rates, scholarship students showed above-average retention and graduation rates, with the majority pursuing graduate studies or careers in STEM. These findings highlight the importance of comprehensive support programs that integrate financial aid, mentorship, and professional development to promote persistence and success among URM students in STEM fields.

Authored by
Dr. Jen-Mei Chang (California State University, Long Beach), Dr. Jelena Trajkovic (California State University Long Beach), and Dr. Gino Galvez (California State University, Long Beach)

This research explores the use of pre-trained large language models (LLMs) to predict weekly lecture-based engagement of college STEM students based on longitudinal experiential data. We leverage non-cognitive attributes, such as emotional responses, and socio-economic background information to forecast engagement patterns. To address data limitations, we employ a contextual data enrichment method. Experiments with BERT (encoder-only) and Llama (decoder-only) models demonstrate that BERT achieves higher accuracy, particularly with non-cognitive data, while both models improve with background data integration. These findings highlight LLMs' potential to enable data-driven interventions in STEM education by predicting student engagement.

Authored by
Ahatsham Hayat (University of Nebraska - Lincoln), Bilal Khan (Lehigh University), and Mohammad Rashedul Hasan (University of Nebraska - Lincoln)

Despite efforts to cast STEM fields in a more inclusive light, engineering and computer science are still perceived as ill-fitting and exclusionary by women and people of color. Another common misconception is that STEM content is too abstract to be relevant to everyday life. These perceptions affect efforts to ensure the involvement of underrepresented groups in STEM and efforts to shift the sometimes-exclusionary cultures of STEM workplaces and universities. In this work, we recast the paradigm of a robotics course into a more inclusive space by showcasing the value of interdisciplinary collaboration in engineering as well as highlighting both the value of engineering in crafting and of crafting in engineering by teaching robotics topics through a craft that has alternately been coded as masculine or feminine depending on time period and culture: weaving.

We developed an undergraduate introduction to robotics course that explores the connection of weaving and engineering through the rich engineering history of looms, the physical properties of cloth, incorporating electronics into cloth through e-textiles, and building robotic looms. Weaving patterns can be represented mathematically through the binary, matrix-like nature of the pattern, allowing the physical properties (feel and drapability) to be analyzed geometrically through an understanding of properties connected to engineering (yarn tension, weight, and interlacement structure). Furthermore, to create high-quality, complex cloth, weavers follow a process that mirrors the engineering design process. Weavers' desire to create more complex patterns and the industry's desire to mass produce these products has led not only to advancements in processes but also to multiple engineering innovations. For example, the development of modern automation was driven by the introduction of punch cards to program the first Jacquard looms which led to modern-day computers.

The course was supported by the use of RoboLoom, an open-source, educational, Jacquard loom kit, to bridge the gap between weaving and engineering in the classroom. This course was taught in the Spring of 2024 to 17 students from mayors ranging from art to computer science. Students were randomly distributed in interdisciplinary groups that were balanced by area of expertise. In these groups, students completed in-class interdisciplinary assignments and final projects. We conducted surveys and observations throughout the course to understand students’ experiences and how their attitudes toward math, weaving, engineering, and collaborative learning changed. Additionally, we conducted interviews to give students an opportunity to reflect and expand upon their attitudes and how they changed through the course experience. Finally, students reflected on each assignment in the course on how they used engineering or creative skills, how well the assignment worked for them, and how it could be improved. We received generally positive feedback from students who, in conclusion, valued learning interdisciplinary skills. In this work, we present our curriculum, findings, as well as reflections and recommendations for the design of this and other interdisciplinary engineering curricula. This material is based upon work supported by the National Science Foundation Division of Undergraduate Education.

Authored by
Samantha Speer (Carnegie Mellon University), Dr. Melisa Orta Martinez (Carnegie Mellon University), Dr. Kylie Peppler (University of California, Irvine), Olivia Robinson (Carnegie Mellon University), Dr. Joey Huang (North Carolina State University), Nickolina Yankova (Affiliation unknown), and Santiago Ojeda-Ramirez (University of California, Irvine)

This paper presents progress and insights from the NSF-funded Transforming STEM Education using an Asset-Based Ecosystem Model project at California State University, Los Angeles, a minority-serving institution where over 70% of students are Hispanic/Latiné, Pell-eligible, and first-generation. Historically, the College of Engineering, Computer Science, and Technology has implemented various intervention programs - preparatory courses, cohorting, tutoring, workshops, and peer-mentoring - to support students from their transition to college through graduation. While these efforts have led to incremental improvements, they have not delivered the transformative outcomes we envisioned. A key realization from these interventions is the need for a new approach that meets our students “where they are”. This prompted a shift from operating through the lenses of a rigid, "factory model" of education—which assumes uniformity in student input and output—to an adaptable ecosystem framework that leverages its agents' assets and community cultural wealth.

The Eco-STEM project focuses on developing structures and tools to allow the current system, constrained by factory-like processes, to evolve into an asset-based ecosystem that better serves the diverse needs of its agents—students, faculty, and staff. Key initiatives include: (1) the Eco-STEM Faculty Fellow Community of Practice, a year-long cohort engaging in discussions on topics such as identity, teacher identity, and cultural wealth, culminating in Action Research Teaching (ART) projects; (2) the Eco-STEM Lecturer Faculty Workshops, providing condensed versions of the Faculty Fellow Community of Practice experience; (3) the Educational Ecosystem Health Survey (EEHS), which uses validated constructs to assess the well-being of the system's members; (4) the Eco-STEM Peer Observation Tool and Process focused on formative, growth-oriented feedback for faculty; (5) the Eco-STEM Student Opinion Survey designed around the ecosystem model, examining classroom climate, structure, and vibrancy; and (6) the Eco-STEM Mental Model Survey, which assesses faculty perspectives on academia through the lens of ecosystem and factory educational paradigms. This paper briefly discusses the tools and strategies developed, lessons learned through implementation, and team member reflections on how creating educational spaces that value and adapt to the unique strengths of students, faculty, and staff can lead to thriving outcomes for all.

Authored by
Dr. Gustavo B Menezes (California State University, Los Angeles), Dr. Corin L. Bowen (California State University, Los Angeles), Nicholas Rabb (Affiliation unknown), Kenya Z. Mejia (San Francisco State University), Dr. Lizabeth L Thompson P.E. (California Polytechnic State University, San Luis Obispo), and Dr. Nancy Warter-Perez (California State University, Los Angeles)

Over the course of a seven-year study, our team created and disseminated several Low-Cost Desktop Learning Modules (LCDLMs) used to teach college students difficult engineering principles. The goal of this project is not only to elevate students' understanding of engineering concepts by learning in a hands-on manner, lowering the associated cognitive load, but to allow them an opportunity to work in interactive groups. This approach is inspired by Social Cognitive Theory (Author, 2011), which posits learning is a social process, and thus complex ideas are learned best collaboratively. LCDLMs are thus meant to help students visualize the concepts to be learned and create an environment where students can make observations and test hypotheses together, sharing and evolving their understanding through their differing perspectives. Students are asked to participate in pre- and posttests to assess learning of the associated concepts, and a survey to gauge their motivation inspired by using the LCDLMs.

Now that the project has been running for several years, and data have been collected in several classrooms at universities across the country, it is worth examining whether instructors have embraced this approach to enhance their own learning strategies as well as for us to assess student learning within the classroom. The LCDLMs were disseminated to instructors who agreed to participate via a “Hub and Spoke” model, where workshops were held in different regions at various “hubs” across the US to instruct professors on appropriate uses of LCDLMs. Feedback was gained through post-implementation forms with written responses submitted semesterly. The intent is to remove any barriers instructors may have in implementing LCDLMs effectively, such as lack of funds, poor technical support, insufficient how-to information, as well as to include their suggestions about more effective strategies for using the LCDLMs and collecting test scores and survey information from their students.

In the past year, greater attempts have been made to communicate with participating instructors throughout the school year. Instructors have asked that the pre- and posttest results from the LCDLM activities be shared with them outside of workshops, not only to support the validity of use of the LCDLMs, but so the activities can be incorporated into their grade books. Additionally, we have compiled a list of “best practices” from both the researchers working on the project and the participants in the study to implement the LCDLMs more efficiently. To assess the effectiveness of this new initiative, we have called participants in for interviews to receive their feedback on the best practices and learn about their implementation process in greater detail, to formulate new hypotheses and identify misconceptions and misunderstandings. We have also gone over all the post-implementation forms reporting participant implementation strategies since the release of our best practices document in August 2023 and comparing said strategies to class performance.

Finally, new LCDLMs are under development to incorporate additional engineering topics not yet covered by the current set. A glucose analyzer LCDLM is being produced, tested, and prepared for implementation, while a recently developed fluidized bed will be used for a third time in the classroom and recent work includes use of a gel-immobilized enzyme. Results from implementations will be analyzed based on pre- and post-tests and motivational surveys.

Authored by
Riley Jackson Fosbre (Washington State University), David B. Thiessen (Washington State University), Prof. Bernard J. Van Wie (Washington State University), Dr. Olusola Adesope (Washington State University), Dr. Prashanta Dutta (Washington State University), Faraz Rahimi (Washington State University), and Md Shariful Islam (Washington State University)

Design thinking in scientific modeling emphasizes creativity, collaboration, and iteration, making it highly beneficial in education. It encourages deep engagement with scientific concepts, enhancing critical thinking and problem-solving skills. As Inkinen et al. (2020) noted, model development is a fundamental scientific practice that aids students in understanding complex phenomena by providing explanations and facilitating predictions about the natural world. This approach fosters an active learning environment where abstract concepts become more accessible (Citrohn & Svensson, 2020).

The iterative nature of design thinking allows for continuous refinement of models through feedback and evaluation. Mentzer et al. (2015) highlight that models can be visual, graphical, or mathematical, essential in engineering design processes. This flexibility helps students explore multiple solutions, deepening their understanding of scientific principles. Emphasizing feasibility and evaluation encourages students to assess their models critically, ensuring they are both theoretically sound and practically applicable.

Incorporating design thinking into science education enhances students' epistemic knowledge, which is their understanding of the nature and justification of scientific knowledge. Lee (2023) explains that experimentation and model development help students navigate scientific inquiry complexities, such as hypothesis formulation and data analysis. This process is vital for cultivating scientific literacy, empowering students to construct evidence-based arguments, and engaging in meaningful discussions. Argumentation within this framework enriches learning as students articulate reasoning and critique peers' models.

Research indicates that model-building demands critical thinking and creativity. Dauer et al. (2013) emphasize the need for genuine motivation beyond compliance with school tasks, suggesting modeling should connect molecular phenomena to broader concepts, reflecting students' understanding of scientific principles.

Selecting appropriate representations is crucial for effectively communicating scientific ideas. Peterson et al. (2021) discuss cognitive image functions that enhance comprehension through visual displays. Understanding these functions helps students articulate invisible aspects of phenomena, enriching explanations. Park et al.'s (2021) findings show that collaborative drawing activities facilitate the negotiation and refinement of visual representations.

The cognitive demands of modeling are significant as students navigate complex content and representation design. Minkley et al. (2018) note that developing representations simplify natural phenomena's complexity, requiring identifying relevant aspects and relationships akin to a design process considering functionality.

Teachers play a crucial role in supporting modeling practices. Baumfalk et al. (2018) stress the importance of creating environments for effective model construction and utilization, guiding students through iterative processes where they refine models based on feedback and evidence. Such support is essential for developing skills necessary for modeling as a practice rather than a task.

The project with preservice science teachers explored how they design models to explain phenomena. Pedagogical tools were developed to scaffold this process, with groups presenting models using meta-representations to explain occurrences while peer feedback enhanced feedback skills. Key findings included shifts in designs based on evidence or feedback across iterations, variations in initial designs, and an initial focus on objects rather than invisible components.

Authored by
Dr. Jaclyn K. Murray (Mercer University) and Alex St Louis (Mercer University)

The biotechnology and pharmaceutical industries are increasingly reliant on a workforce pipeline of graduates possessing the skills needed to quantitatively describe complex systems to predict functional outcomes relevant to healthy physiological function and to disease states. These skills will be essential for not only identifying novel drug targets and ascertaining the etiology of complex diseases such as cancer and heart disease, but also for achieving truly personalized medical diagnostics, therapies, and surgical approaches toward treating these diseases. Moreover, inherent biological complexity and high-throughput measurement approaches lead to massive “big data” sets, often with thousands of heterogeneous values. This complexity requires data science tools such as data-driven modeling and machine learning to appropriately integrate heterogeneous data. Thus, it is imperative to train a diverse new generation of scientists in the concepts and practice of multi-scale systems bioengineering and biomedical data sciences (BDS) research.

At the University of Virginia, we developed an NSF-funded REU Site in Multi-Scale Systems Bioengineering and BDS (NSF #1560282 & #1950374) that has supported 81 students engaging in research projects for the past eight years (2017-2024). These students were recruited out of a total of 1,375 applicants, with participants drawn from 54 colleges and universities. Two summers (2020 & 2021) the program was run as a virtual REU due to institutional constraints on visiting researchers due to the pandemic. The REU students were matched to a mentor based on a combination of student interest in specific sub-areas of systems bioengineering research and mentor availability each summer. Most research projects relied primarily on previously developed methods and tools and typically involved application to biological data and generation of testable hypotheses. The specific research projects included a wide variety of topics in the field, ranging anywhere from molecular scale biophysics models to cell-scale signaling models, biomedical data science analysis of genetic data, tissue-level biomechanics models, and image analysis algorithms for quantifying cell distribution in tissue-engineered constructs. The participants took part in an introductory bootcamp on the fundamentals of systems modeling and had multiple opportunities to present their research progress throughout the summer to experts in the field. They also received professional development training on research ethics, technical communication, and launching careers in systems bioengineering. We analyzed participant demographics, outcomes in presenting or publishing their work, career outcomes, and survey data from each summer’s cohort.

The 81 REU participants came from 54 colleges and universities and represented 24 different majors, with 47% of them biomedical engineering (BME) majors; 68% were from groups traditionally underrepresented in STEM, 32% were first-generation students; 58% were women; and 41% attended non-R1 institutions; 67% presented their work at national meetings, and nine have become co-authors on ten papers. Of the 60 who have since graduated, 85% are either in graduate school or in STEM industry positions. Post-REU surveys of participants revealed that 98% of respondents rated their overall experience with the REU as either “very satisfied” or “satisfied” (average 4.72 on a Likert scale). Evaluations of specific program objectives and mentoring were similarly high. Regarding impact on long-term goals, nearly 75% said that the REU increased their interest in STEM and encouraged them to pursue further education towards a research or academic career, while 45% said the program helped solidify interest specifically in systems bioengineering.

From a programmatic standpoint, we have several recommendations: Our large number of applications suggest that the specific research area is important, and if well-presented to possible applicants can be a highly motivating selling point. Our experience during the pandemic was that a virtual REU can lead to positive research outcomes, although cohort bonding and the experience of working in a lab are diminished. One challenge of a one-size-fits-all bootcamp is appropriately accommodating the needs of the varied research topics. Some projects require coding, but others do not since they use established software tools; some require model development, others data science. We are continually iterating to find the optimal balance of instruction in topics that support every student in the program.

Authored by
Dr. Timothy E. Allen (University of Virginia)

Experiential learning fosters engagement, deepens understanding, and enhances practical problem-solving skills in STEM education (Beier, 2019; Kolb, 1984; NAE, 2005). The [PROJECT] at [INSTITUTION] creates data science learning opportunities for undergraduates with diverse academic backgrounds and exposes them to the practical applications of data science within interdisciplinary contexts. Unlike traditional data science academic programs for students already interested in the field, [PROJECT] seeks to train a future workforce where data science skills are needed as a core attribute of most career paths. Two interventions created for students include 1) data science modules developed for and deployed in introductory STEM and social science courses, and 2) experiential learning opportunities in data science through internal and external internships that allow students to apply data science concepts to real-world projects.

This paper focuses on the latter component of the project. We use a qualitative case study approach to explore the personal and professional growth of four students—each serving in distinct roles during their internship. Through our analysis, we identify key themes related to personal and academic growth. Key findings include the significant enhancement of the interns’ abilities in teamwork, organizational skills, and leadership, as well as a marked increase in confidence in data analytics and data visualization. For example, interns who initially had moderate confidence in data-related work reported substantial improvement by the program's end, with their career goals in data science becoming clearer. Interns also highlight the program's effectiveness in providing networking opportunities and a sense of community, which were instrumental in shaping their professional development.

The program’s approach to integrating data science into broader fields demonstrates its potential as a model for interdisciplinary education. Experiential learning opportunities bridge the gap between theoretical knowledge and practical application. The research highlights the importance of such initiatives in fostering comprehensive skill development and confidence in STEM students and equips students—regardless of their primary discipline— with the skills needed for the modern workforce. This research is supported by the NSF IUSE program under [GRANT NUMBER].

References

Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall.

National Academy of Engineering. (2005). Educating the Engineer of 2020: Adapting Engineering Education to the New Century. Washington, DC: The National Academies Press. https://doi.org/10.17226/11338.

Beier M.E., Kim M.H., Saterbak A., Leautaud V., Bishnoi S., & Gilberto J.M. (2019). The effect of authentic project-based learning on attitudes and career aspirations in STEM. Journal of Research in Science Teaching. 56 (1), 3–23. https://doi.org/10.1002/tea.21465

Authored by
Prof. Petra Bonfert-Taylor (Dartmouth College), Dr. Laura E. Ray (Dartmouth College), and Scott Pauls (Dartmouth College)

In order to build a diverse engineering workforce, it is important to create a pipeline of students from diverse backgrounds who are interested in engineering. The Inquiry Driven Engineering Activities using Bioengineering Examples (IDEA-BioE) project was designed to engage secondary science and mathematics teachers from school districts with highly diverse student populations in research focused on biomedical engineering and translate those experiences into modules that teachers can implement in their classrooms.

Interest in engineering among pre-college students, especially girls and students from racial and ethnic minority groups, is often low. This suggests the need for interventions at an early age. However, simply exposing students to engineering design principles is not sufficient to create interest in engineering careers among a broad range of students; interventions must also address social and psychological barriers.

We identify two sets of challenges to promoting interest in engineering and engineering careers among K-12 students. First, teachers may not be aware of the scope and specifics of the engineering field. In addition, teachers who are primarily trained in science pedagogy may struggle to incorporate and explain engineering design concepts within science courses. Second, students may be disinclined to engage with engineering for a variety of reasons. Students may be unaware of what engineers do. Students may also struggle to see themselves in the engineering field due to racial and gender stereotypes and a lack of role models. In addition, perceptions of engineering as a field that primarily involves working alone, requires brilliance to be successful, and is done by those who are nerdy or socially awkward may negatively impact student interest in engineering.

Our program involves an immersive six-week summer experience for preservice and practicing teachers. Participants are matched with bioengineering-focused research lab experiences from a variety of disciplines including chemistry, chemical engineering, and mechanical engineering. Although teachers work on research projects within the labs, the key deliverable is development of modules for implementation in their classrooms. To aid in module development, the teachers attend workshops that address engineering design, next generation science standards, best practices for lesson design, and relevant educational psychology concepts (e.g., occupational values, STEM anxiety). Following the completion of the program, teachers implement the modules in classrooms.

Survey data from the participating teachers’ students indicates increased self-efficacy for engineering (“I am good at engineering”) and identification with engineering (“People who look like me use engineering”) after implementation of their modules. Initial analysis of responses to open-ended questions about perceptions of engineers and engineering also indicates changed views, demonstrating effectiveness of these modules on students’ perceptions of engineering as a future career option among diverse student populations.

Funding for this project was provided by the Research Experience for Teachers in Engineering and Computer Science program at the National Science Foundation.

Authored by
Dr. Prajnaparamita Dhar (The University of Kansas)

Abstract
In alignment with the mission of the National Science Foundation to advance scientific progress, enhance national health, prosperity, and welfare, and strengthen national defense, this Research Experiences for Undergraduates (REU) Site in Computational Sciences and Engineering provides a diverse group of students with exposure to various challenges in the field. Participants engage in solving these challenges through the application of methodologies derived from cybersecurity, data analytics, machine learning, process automation, structural engineering, and data-driven safety management. The program offers students direct, hands-on research experience that addresses significant societal issues. Such practical involvement not only enhances students' scientific problem-solving capabilities but also fosters an understanding of the scientific method as a tool for investigating technical challenges. Furthermore, this application of theory to real-world problems is instrumental in solidifying students' interest in STEM fields, which is crucial for improving retention and graduation rates at minority-serving institutions and providing upward mobility for students from underrepresented communities. Through comprehensive research activities, year-long faculty mentoring, and continued engagement via research presentations and publications, the program aims to significantly increase minority students' interest in STEM education. The opportunities provided through this initiative serve as foundational steps in cultivating undergraduate students into proficient scientists and researchers.

This paper presents a summary of the experiences and lessons learned from establishing the REU site, including the development of a dedicated website for promotion, student recruitment strategies, and the implementation of research and extracurricular activities. Additionally, it includes reflections on student projects and feedback. The insights gained from this initiative can serve as valuable guidance for similar future programs designed to engage students in undergraduate research.

Authored by
Dr. Vassilios Tzouanas (University of Houston-Downtown), Dr. Henry Clyde Foust (University of Houston - Downtown), Dvijesh J Shastri (University of Houston - Downtown), Emre Yilmaz (Affiliation unknown), Prof. Arash Rahmatian (University of Houston - Downtown), and Mahmud Hasan (University of Houston - Downtown)

This brief summarizes the first two years of participants’ data from a National Science Foundation (NSF) Research Experiences for Teachers (RET) project on Chip Design (Chip-RET). Semiconductor workforce development has become a national priority due to microchips’ importance to our supply chain security, national defense, and technological leadership. K-14 teachers play a pivotal role in exciting, motivating, and preparing students to join various microelectronics-related career pathways. To meet such requirements, K-14 STEM teachers need to receive the necessary training on the subject matter. Our institution proposed the Chip-RET, the first RET program in the US that focused exclusively on integrated circuit design and K-14 semiconductor education. To evaluate the effectiveness of such training, we further developed a custom semiconductor knowledge and literacy test (SKLT), whose content and interpretation have been validated by semiconductor industry experts.
Our data reveals that the Year One cohort of ten teachers demonstrated an increase in their mean percentage of correct responses to the SKLT test, from 39% to 65% pre- and post-RET. A follow-up Wilcoxon Rank-Sum Test underscored the significance of this difference, with a W-value of 3 and a p-value less than 0.001. Moreover, a repeat measure of the SKLT test nine months after the Chip-RET training (post-9 months) showed a mean percentage of correct response of 55%, suggesting that participants were able to retain much of their knowledge gained nearly one year after the training. A Wilcoxon Rank-Sum Test from pre- to post-9 months showed a W-value of 12 and a p-value of 0.007, further confirming the gain despite some loss of knowledge over the nine-month period.
Finally, the Year Two cohort of another ten teachers also showed an increase from 48% to 69% pre- and post-RET. The post-9 months data is not yet available and will be collected.

Authored by
Haniye Mehraban (Oklahoma State University), Dr. Jennifer Dawn Cribbs (Oklahoma State University), Erin Dyke (Oklahoma State University), Dr. James Stine (Oklahoma State University), and Dr. John Hu (Oklahoma State University)

The National Science Foundation (NSF) Research Experiences for Teachers (RET) program supports summer research experiences for K-14 educators to enhance their scientific disciplinary knowledge in engineering or computer science and translate their research experiences into classroom activities and curricula to broaden their students’ awareness of engineering education and career pathways.

In 2024 an RET site (funded by the NSF) launched at the University of Alabama with a common theme of technologies and applications of sensing systems for physiological and environmental monitoring. This theme was selected because knowledge of how to measure physical quantities of materials, devices, tissues, and the environment is critical to answering research questions across all engineering domains. This then leads to a strong alignment of projects with national and state goals of integrating engineering design and engineering design practices within the context of science content creation.

In the first iteration of our RET, 11 science and math teachers from middle schools in west Alabama participated in research and professional development workshops over 7-weeks. During each week teachers engaged in structured engineering, sensors/programming, and education/curriculum workshops in addition to their faculty directed research. The engineering workshops highlighted the different disciplines and career-paths across engineering and computer science. The sensors/programming workshops guided teachers through using the Arduino Uno platform to measure physical quantities (light, temperature, humidity, sound, and acceleration). The education/curriculum workshop guided teachers through linking their research to education standards and exploring resources available to help teach engineering content in their classrooms. At the end of the 7-weeks the program culminated in a poster symposium with teachers presenting the results of their research to faculty, engineering students, and other professionals on campus.

Based on focus group feedback collected at the end of the program, teachers enjoyed the challenge of learning to do a research project and learned skills they could bring into their classrooms to increase student engagement. However, they noted that more support during the summer to help them develop their curriculum would be useful and help them transition their research to their classrooms. This work will outline the lessons learned from running our site and workshops during Year 1 and outline the planned revisions for Year 2 to address teachers focus group feedback to improve their research experiences and transitioning their work to their classrooms. These lessons and details are expected to support other engineering educators who are planning to launch a RET site or are interesting in possible revisions for an existing site.

Authored by
Dr. Todd Freeborn (The University of Alabama), Chris Smith Crawford (The University of Alabama), and Erika Machan Steele (The University of Alabama)

This poster highlights the establishment of a Research Experiences for Undergraduates (REU) site at the University of South Alabama, dedicated to tackling the growing issue of microplastics in the Gulf Coast region through an interdisciplinary and hands-on approach. The primary objective of the program is to cultivate a positive and proactive mindset among undergraduate students regarding the detection, measurement, and remediation of microplastics, while simultaneously instilling sustainable engineering practices. The program aims to recruit 30 undergraduate students for a 10-week immersive experience at the University of South Alabama, where they will engage in collaborative research and experiential learning focused on microplastics. The intellectual framework of the program encompasses five key thrusts: (1) Understanding the degradation of plastics into microplastics and their impact on ecosystems, (2) Developing novel detection and measurement techniques for microplastics in various environmental contexts, (3) Designing and testing filters to mitigate microplastics in waterways, (4) Establishing systems to monitor microplastic pollution over time, and (5) Repurposing collected microplastics for alternative applications. Through this comprehensive approach, students will gain both fundamental research skills and practical experience, while working in teams to address real-world environmental challenges. In addition to research activities, the program emphasizes the development of essential skills such as ethics and responsible conduct in research, data collection and analysis, critical thinking, technical writing, and teamwork. Mentorship from faculty and peers will be central to the students’ growth, fostering frequent interactions that encourage creativity and innovation. The ultimate goal is to transform undergraduate students from passive learners into independent, collaborative researchers equipped to tackle complex environmental problems, with a focus on sustainable solutions for the future.

Authored by
Dr. Shenghua Wu (University of South Alabama), Prof. Jinhui Wang (University of South Alabama), and Melike Dizbay-Onat (University of South Alabama)

The Resilient and Sustainable Infrastructure Systems in Smart Cities (RSISSC) REU Site grant was developed to bring a cohort of students to a large R-1 university to expose students to the critical role of civil engineers in serving society. Students participated in hands-on interdisciplinary research with faculty teams exploring innovations for Smart Cities, including new image analysis tools, sustainable materials analysis, technologies for quantifying indoor air quality and remediating water pollution, decision analysis tools, and strategies for autonomous navigation. Associated cohort programming helped students to build basic research skills (e.g. literature searches, data analysis, ethics considerations), to develop their career paths with emphasis on the preparation process for graduate studies, and to expose students to real-world applications of ‘smart’ technologies for resilient and sustainable infrastructure. The design of these enhancing activities was guided by the REU Site project evaluation framework that posits that guided immersive research experiences have measurable impacts on shifting students’ vision of career choices and that expanded personal networks, in this case through relationships with other researchers, are influential in converting student goals to actions. Evaluation assessments used a combination of surveys and interviews, personalized to the survey results.

The Resilient and Sustainable Infrastructure Systems in Smart Cities REU Site grant has met project goals in each of two years that the project has been active (2023, 2024). Evaluations findings from the first year informed adjustments to the program structure and organization for the second year. Across the two cohorts, the project engaged 20 undergraduate students (17 were not currently enrolled at the university) from a variety of majors to work on research questions in the fields of civil, environmental and geodetic engineering. Students were able to see relevance to Smart City innovations of their backgrounds in microbiology, biochemistry, physics, mechanical engineering, computer science, architecture and urban planning, and civil engineering. The student cohort diversity increased across the program years with the 2024 cohort having gender parity, one student from a Historically Black College and University, and two students from Puerto Rico. Students reported the most frequent skills they practiced throughout the program to be communication skills, creative problem-solving, professional and ethical responsibilities, shared leadership, and business and management principles (listed from most to least frequent). Students also developed social capital related to career exploration and the research process through the relationships developed with their graduate student mentors, faculty advisors and the program leadership. Overall, student participants reported positive program experiences, pride in the work results they showcased in a closing college-wide poster session, and greater clarity in their future academic and career plans.

Authored by
Dr. Allison Mackay (The Ohio State University), Prof. Jieun Hur P.E. (The Ohio State University), Dr. Julie P Martin (University of Georgia), and Mrs. Jennifer Brown (Clemson University)

First offered in the summer of 2006, the NSF-funded AERIM Research Experience for Undergraduates (REU) program has now gone through four full 3-year funding cycles and has been offered a total of 12 different summers, with 1-2 year breaks in between. The focus of this REU program has long been on automotive and energy research, with strong ties to the automotive industry in Southeastern Michigan. A total of 122 students from 83 different colleges and universities have taken part in the program and have now progressed to different points of their academic and professional paths. One of the greatest successes of this REU has been its ability to attract a diverse group of undergraduate researchers, with groups historically underrepresented in engineering (particularly women) representing over two-thirds of the participants. The program is assessed each year using pre- and post-surveys, as well as focus group discussions in the past 3 years. The assessment and follow-up has also included periodic emails and surveys to gauge the outcomes of the program several years after students have completed the REU. Maintaining contact and tracking the career progressions of students after several years is no easy task, but one that has been made easier with the advent of professional social networking sites, such as LinkedIn. The goal of this paper is to report on some of the demographics and outcomes of this REU, as well as share some of the lessons learned, particularly since the advent of COVID-19.

Authored by
Dr. Laila Guessous (Oakland University) and Dan DelVescovo (Oakland University)

This study aimed to improve problem-solving skills in students entering engineering at the College Algebra level. These students start with deficiencies in math knowledge, and remedial math courses are traditionally used to support their success in engineering. By the end of their first year in college, many of these students either transfer out of engineering or discontinue their education. This study focuses on students' ability to solve story or word problems, which contain a mathematical problem embedded in a narrative. Such problems are common in engineering, especially in courses like Statics and Dynamics. Previous studies have shown that students often struggle to understand and represent matematically word problems. Solving these problems can be particularly challenging for at-risk students, including those with learning disabilities.

This study summarizes the findings from the second year of this NSF-supported project, conducted at an R-1 land-grant institution in the mid-Atlantic region. Ninety-eight (98) students participated in the second year of the study. All participants were enrolled in an engineering problem-solving course. Multiple word application problems were designed to blend engineering and math concepts. The study received approval from the Institutional Review Board.

The study explores how concurrent judgments can enhance students’ performance and self-assessment accuracy (i.e., calibration, as measured by the absolute accuracy index) during problem-solving. Absolute accuracy evaluates the precision of a confidence judgment relative to performance on a criterion task. Concurrent judgments require students to assess their confidence in their performance while engaged in the task (solving word problems). One-third of the student were allocated to the control group (they solved word problems with no metacognitive judgement intervention) and the remining two-thirds received an intervention where they were asked to solve word problems and provide concurrent judgement.

This metacognitive monitoring study focuses on the relationship between confidence judgments and performance. This presentation summarizes the results of the study, the successes in improving students' judgment and problem-solving skills, and the lessons learned on how to support students with deficiencies in math knowledge. This presentation will benefit institutions that accept students into engineering programs who are not ready to start Calculus during their first semester in college.

Authored by
Dr. Lizzie Santiago (West Virginia University), Jake Follmer (West Virginia University), and Mr. Michael Keith Brewster (West Virginia University)

The Effect on Students’ Attitudes toward Graduate Education and Transportation-related Fields in the Research for Inclusivity and Driving Equity (RIDE) REU Site

Improving upon inequities in transportation that disproportionately harm underserved groups is of paramount importance. In addition, incorporating underrepresented minority (URM) students into STEM research that improves the experiences of underserved communities is significant to their retention as well as education and career advancement [1]-[3]. In line with the National Science Foundation’s goal to support science and engineering research to promote the advancement of students [4], the Division of Engineering Education and Centers (EEC) funds the RIDE REU Site from 2022 - 2025.

The purpose of the RIDE REU Site is to provide undergraduate students with an immersive and interdisciplinary experience in community-engaged research focused on improving the transportation experience of underserved and underrepresented communities. In 2023 and 2024, the site recruited a diverse pool of 26 undergraduate students, including 15 women, 15 URM students, 4 first-year students, and 10 from no-or-low research institutions. The site also engaged students in interdisciplinary research projects in engineering, science, psychology, planning, and policy over the nine-week summer program. Students were assigned to research projects based on their backgrounds, interests, and the evolving research among faculty, graduate student mentors, and community patterns. Twelve faculty and 17 graduate student mentors helped to train and support students during the summer, whom they continue to maintain connections with and mentor beyond the summer. In addition, the RIDE site provided professional development and communication seminars on topics such as community-engaged research and ethical research practices and technical seminars to provide exposure to real-world transportation-related work as well as potential career paths in transportation.

To assess the RIDE REU Site’s effectiveness, we employed a survey and focus groups. Results from the descriptive analysis and focus groups provide evidence of the program’s influence on students’ attitudes toward enrolling in graduate school and pursuing transportation-related fields. Specifically, before the program, 40% of students indicated it was likely they would attend graduate school. This increased to 80% after the program. Regarding pursuing studies in transportation-related fields, the proportion of students who indicated it was “somewhat unlikely” decreased from 33% to 17%, while those who indicated “somewhat likely” increased from 17% to 25%. In addition, 90% of participants indicated they were “not at all knowledgeable” to “somewhat knowledgeable” about the research process before the program. However, after the program, 90% indicated they were knowledgeable about the research process. In the focus groups, one participant explained, “I had zero research experience coming here. I learned coding and how to set up interviews. It was very useful…” Others also said that the “hands-on approach” of the program facilitated their learning, and they enjoyed working in a setting where they could “make mistakes without worrying about grades.” This study contributes to the understanding of the effectiveness of multi-faceted research training programs on undergraduate education and career advancement and points to the importance of a mentoring framework for an inclusive research community.

Authored by
Shannon Roberts (University of Massachusetts Amherst) and Betty Annan (University of Massachusetts Amherst)

Significant numbers of first-year community college students place below Calculus-level mathematics and are underprepared for direct entrance to core prerequisite courses in an engineering baccalaureate degree curriculum. As a result, a potentially daunting and abstract sequence of math courses can dissuade otherwise promising candidates from the engineering profession. This NSF-IUSE project, launched in fall 2022, is a collaboration between engineering, mathematics, history, English, and physics faculty to create a two-quarter learning community experience for precalculus-level students entering our engineering transfer program at Whatcom Community College. The Engineering in Context Learning Community is a six-course curriculum that integrates contextualized precalculus, English composition, Pacific Northwest history, engineering orientation, and introductory problem solving and computing skills. The program employs high-impact practices including place-based learning, community-engaged projects, contextualized instruction, and undergraduate research to motivate foundational skill development, emphasize social relevance, and develop students' engineering identity, sense of belonging, and academic readiness.
The 2023-24 academic year marked the first pilot offering of the new learning community with an initial cohort of 19 students out of a capacity limit of 24. This paper and poster reports on early findings comparing persistence rates into the second-year curriculum between the first learning community cohort and our more general engineering student population. For example, 14 out of 19 (74%) cohort students enrolled in Calculus 1 within two terms of starting Precalculus 1. All 14 passed. This retention rate compares to 13 out of 30 (43%) for non-cohort students who were concurrently enrolled in Precalculus 1 and Introduction to Engineering. Only 11 of whom passed Calculus 1 on their first attempt.

Authored by
Prof. Eric Davishahl (Whatcom Community College), Anna Fay Booker (Whatcom Community College), Mr. Pat Burnett (Whatcom Community College), Seth Greendale (Whatcom Community College), Petra McDonnell-Ingoglia (Whatcom Community College), Anna Wolff (Whatcom Community College), Tran M Phung (Whatcom Community College), and Tyler L Honeycutt (Whatcom Community College)

In Fall 2022, the Vertically Integrated Projects (VIP) learning model was launched at University of X (UofX), a public urban research university in the southeastern United States. Supported by an IUSE grant, this program is now in its third year, with 6 teams comprising 29 undergraduate participants. The VIP model involves active learning on faculty-led, team-based projects. Team members include first-year students through graduate students with one to three or more semesters of participating in VIP. The VIP model enables tiered mentoring, from faculty to graduate students, graduate students to undergraduates, and more senior to newer students [1]. The goals of UofX’s VIP program are to: (1) help build a more inclusive research culture; and (2) help students build STEM identity, as well as self-efficacy, mindset, and intentions to stay in engineering. The first goal was based on research about the positive impact of active learning and mentoring for recruiting and retaining women and other historically excluded groups in STEM [2]. The second goal builds on previous work on mentoring and community building on STEM Identity, while extending that work to related variables [3, 4]. The purpose of this paper is to report on the evaluation of the VIP program at UofX after two years.

This paper comprises three parts. In the first, we will discuss the challenges and opportunities in implementing the VIP model at the UofX from the perspective of the PIs. This section will focus on issues involving recruiting faculty mentors and recruiting and retaining diverse undergraduates into the VIP program. In the second, we will report results from focus groups conducted in the second year of the program with undergraduates, graduate student mentors, and faculty team leaders. This section will focus on motivations for joining VIP, perceived strengths and weaknesses of the program, and suggestions for improving the program. In the third, we will summarize our research findings to date and discuss issues related to conducting research in this context and how we have adapted some of our research strategies in response to certain challenges. We conclude with lessons learned as well as directions for moving forward.

References

[1] “The VIP model.” VIP Consortium. https://www.vip-consortium.org/content/vip-model (accessed Jan. 26, 2024).
[2] National Academies of Sciences, Engineering, and Medicine, Promising Practices for Addressing the Underrepresentation of Women in Science, Engineering, and Medicine: Opening Doors, Washington D.C., USA: The National Academies Press, 2020.
[3] S. Ivey and S. Parish, "Cultivating STEM Identity through the Peer Mentoring Relationship," in Navigating the Peer Mentoring Relationship: A Handbook for Women and Other Underrepresented Populations in STEM, K. Wade-James, A. Rockinson-Szapkiw, J. Wendt, Dubuque, IA, USA: Kendall Hunt, 2020, p. 316.
[4] C. O. Stewart, J. T. Campbell, T. Chase, M. Darbeheshti, K. Goodman, S. Hashemikamangar, M. Howland Cummings, S. S. Ivey, D. J. Russomanno & G. E. Simon, “Communicating Identity in the Urban STEM Collaboratory: Toward a Communication Theory of STEM identities,” International Journal of Science Education, Part B, vol. 13, no. 4, pp. 345-361, Oct. 2023, doi: 10.1080/21548455.2023.2179380.

Authored by
Craig O. Stewart (University of Memphis), Dr. Chrysanthe Preza (The University of Memphis), and Dr. Stephanie S Ivey (The University of Memphis)

The Department of Biomedical Engineering at the University of Arkansas is the only research-intensive, PhD. granting institution in the state and nearby region. The NSF-funded Biomedical Optics and Imaging REU program (award number EEC #2243953) was initiated in Summer 2023 to primarily recruit students from regional schools with strong undergraduate programs in biology or related areas that lack a graduate program in biomedical engineering. Through this REU site, students were paired with faculty mentors to work on a range of individual, hypothesis-driven projects which apply or develop state-of-the-art biomedical imaging methods and techniques. Students also received a range of lectures on professional development topics, social activities, and the experience culminated in each student submitted an abstract to present at the Biomedical Engineering Society Annual Meeting. To evaluate students’ experience in our REU program, an external evaluator was used to conduct a survey and assessment of what was learned.

In Summer 2023, the first year of the program, seven students (3 female, 4 male) submitted and presented their abstracts at the BMES conference in Seattle, WA. Most students were recruited from a range of regional institutions, with one student attending from Stanford University. Four of these students had 2 semesters or fewer of prior research experience, with two students having never performed independent research at all prior to attending our program. Large majorities of the students (5-6 out of the 7) reported Large or Very Large gains in acquiring new laboratory skills, preparing a scientific poster, and understanding what day-to-day research is like. Additionally, large majorities of >5 students reported they were somewhat or very likely to pursue an advanced graduate degree (MS or PhD) and continue to pursue working in a research lab during the rest of their undergraduate tenure.

Overall, students attending the Biomedical Optics and Imaging REU at the University of Arkansas reported favorable experiences, gained significant specific laboratory and presentation skills in areas crucial to biomedical engineering, and were more likely to attend graduate school following the completion of this program.

Authored by
Prof. Jeff Wolchok (University of Arkansas) and Timothy J. Muldoon (University of Arkansas)

Our project uses an ecological belonging intervention that requires one class session to implement and has been shown to eliminate equity gaps in student outcomes in introductory STEM courses. We describe disparate impacts on outcomes as equity gaps because they exist not from any deficit of the students themselves but rather due to systemic issues of marginalization. Our NSF IUSE: EDU Program, Institutional, and Community Transformation track grant brings this intervention into engineering where during the grant period we have implemented the intervention at three strategically chosen universities in both first- and second-year engineering courses. The intervention focuses on common challenges faced by students in engineering and is introduced by instructors early in the semester. Our research has demonstrated that the intervention is effective during the first year in supporting belonging for Black, Latiné, and Indigenous (BLI) students and in reducing equity gaps in academic performance during a first-year programming course. Our research has also demonstrated that BLI students who receive the intervention have improved help-seeking behaviors and are more likely to be retained in engineering into the second college year and that women students who receive the intervention may have more positive self-efficacy.

Our project is comprehensive in the development and delivery of the ecological belonging intervention and in the study of its effects. We use focus groups to understand challenges engineering students face in their courses and in different institutional contexts to contextualize the intervention specifically to each course in which it is delivered. We also provide instructor onboarding and training to deliver the intervention which includes an overview of the theoretical frameworks that undergird the intervention and how those theoretical properties are actualized and delivered during the intervention. These efforts along with the rest of our training are aimed at increasing implementation fidelity as we hypothesize that the intervention is most effective when fidelity is high. Our work is guided by a theory-of-action that is the backbone of the project’s research activities and iterative processes of improvement. We use a synergistic mixture of qualitative and quantitative methods to study both student and faculty outcomes.

To expand on understanding the impacts of the intervention on students, we have recently begun to examine how students experience the intervention, if they remember it, what they remember about it, and what they feel they gained from it. In this paper, we provide an overview of our findings in this area using data collected from surveys of two first-year engineering programming courses at two study institutions and focus groups and interviews with students at the third institution where the intervention is being implemented within second-year courses in specific engineering majors. We will also report on our continued research on the efficacy of the intervention on student outcomes. In their totality, the results of this work can provide actionable strategies for reducing equity gaps in students' degree attainment and achievement in engineering.

Authored by
Dr. Linda DeAngelo (University of Pittsburgh), Dr. Allison Godwin (Cornell University), Mr. Matthew Bahnson (Purdue University – West Lafayette (College of Engineering)), Dr. Danielle V. Lewis (University at Buffalo ), Prof. Natascha Trellinger Buswell (University of California, Irvine), Erica McGreevy (University of Pittsburgh), Dr. Christian D Schunn (University of Pittsburgh), Eric Trevor McChesney (University of Pittsburgh), Blayne D. Stone (University of Pittsburgh), Liwei Chen (University of Pittsburgh), Carlie Laton Cooper M.Ed. (University of Georgia), Spencer Currie (University of California, Irvine), Charlie Díaz (University of Pittsburgh), Gerard Dorvè-Lewis (University of Pittsburgh), Rachel Kelly Forster (University of Pittsburgh), Kevin Jay Kaufman-Ortiz (Purdue University/Cornell University), Melissa Lepe (University of California, Irvine), and Kelly Tatone (University of Pittsburgh)

Evidence-Based Instructional Practices (EBIPs) are pedagogical approaches grounded in research that enhance student learning, engagement, and retention. These methods, such as active learning, problem-based learning, and peer instruction, have been shown to improve both short- and long-term learning outcomes, particularly in fields like engineering, where students often face complex, abstract concepts and large class sizes. EBIPs help bridge the gap between theoretical knowledge and real-world application, fostering critical thinking and problem-solving skills that are essential in engineering education. Despite their known benefits, many engineering faculty struggle to implement EBIPs due to limited training, time constraints, and a lack of discipline-specific resources.

This NSF-funded project seeks to increase the adoption of EBIPs in undergraduate engineering courses by identifying and addressing the specific contextual barriers and affordances that faculty face during implementation. Faculty often report challenges such as insufficient time for curriculum redesign, a lack of professional development opportunities, and institutional cultures that prioritize research over teaching innovation. To address these challenges, the project focuses on three key activities: (1) investigating the decision-making processes and contextual challenges faculty encounter when implementing EBIPs; (2) co-developing course materials and curriculum to align with EBIP strategies; and (3) creating research-informed resources to support EBIP-based course development.

This project engages faculty from over 40 institutions, including R1 universities, undergraduate-focused colleges, minority-serving institutions, and two-year colleges. By capturing faculty experiences across this diverse range of educational environments, the project aims to develop scalable, adaptable strategies for EBIP implementation. Faculty participants are paired with experienced mentors who provide ongoing, tailored support to address discipline-specific challenges. These mentors assist in curriculum redesign, pedagogical guidance, and troubleshooting barriers, facilitating a deeper understanding of how EBIPs can be integrated effectively.

In this poster, we present key findings from the project, focusing on the contextual barriers faculty encounter during the decision-making process of implementing EBIPs, and the strategies used to overcome these challenges. Case study examples from the participating institutions illustrate both the struggles and successes faculty experience under the scaffolded guidance of a faculty mentor. These case studies offer a window into the process of curriculum transformation, highlighting practical approaches to integrating EBIPs and providing rich, detailed descriptions of the change process.

Authored by
Dr. Maya Menon (New Jersey Institute of Technology), Stephanie Adams (Oregon State University), Dr. Prateek Shekhar (New Jersey Institute of Technology), Dr. Shane A. Brown P.E. (Oregon State University), and Jeff Knowles (Oregon State University)

WebTA is an autocritiquer providing real-time feedback for programming in flipped-class active learning classes for first-year engineering students. WebTA was developed to critique student code in introductory computer science courses that programmed using Java. WebTA provides just-in-time feedback on syntax errors, subtle logic errors, and style issues.

The I-USE project Rich, Immediate Critique of Antipatterns (RICA) in Novice Programmer Code: Broadening Adoption, Supporting Student LEarning, and Enhancing Programming Competencies project is simultaneously extending WebTA for MATLAB and examining its impact on the computer programming self-efficacy of novice programmers. Within first-year engineering classes, students were asked to submit MATLAB code to WebTA for feedback, so that they might revise it prior to submission for grading. In this manner, WebTA provided real-time, instantaneous feedback for classes of up to 120 students at a time - a feat which even the most attentive teaching team of instructors and TAs could not achieve.

As self-efficacy is tied to success in engineering programs, and computer programming is an essential component of the education of first-year engineering students, our team is examining the impacts of WebTA on the computer programming self-efficacy of first-year engineering students as they learn to code. This paper summarizes our current progress investigating how prior programming experience and initial confidence levels influence the effectiveness of the code critiquer tool in enhancing programming self-efficacy. By analyzing these variables, the study identifies key factors that mediate the impact of automated feedback and provides strategies for tailoring educational interventions to diverse student needs.

Authored by
Mary Benjamin (Michigan Technological University), Dr. Michelle E Jarvie-Eggart P.E. (Michigan Technological University), Dr. Leo C. Ureel II (Michigan Technological University), and Dr. Jon Sticklen (Michigan Technological University)

This study presents findings from a National Science Foundation (NSF)-funded project aimed at exploring how students apply monitoring and evaluation (ME) processes in conjunction with their metacognitive knowledge of tasks (MKT). The research focused on problem-solving activities in engineering and mathematics courses, specifically Ordinary Differential Equations and Engineering Statics, which were chosen to represent different yet interconnected fields in the second-year engineering curriculum. Twenty undergraduate students (7 female, 13 male) from these courses participated. Data were collected through semi-structured, one-on-one interviews conducted before and after problem-solving sessions, with a think-aloud protocol employed during the sessions. Each student solved two problems of varying difficulty, resulting in a total of 80 qualitative data points.

The qualitative analysis of the semi-structured interview data provided insights into the students' understanding of tasks prior to engaging in problem-solving. Comparative Content Analysis (CCA) was used to systematically examine and compare qualitative data segments from the two courses, as well as the varying difficulty levels of the tasks. This approach enabled a detailed analysis of similarities, differences, and trends in students' metacognitive knowledge about the tasks. The Think-Aloud Protocol (TAP) data offered further insight into students' self-regulation in action during problem-solving. From this data, seven distinct problem-solving learning episodes were identified and categorized into four quadrants, each representing different interactions between students' metacognitive knowledge about the task and their self-regulation (monitoring/evaluation) during problem-solving activities.

In this paper, we focus on two learning episodes within the fourth quadrant (Routine Learning and Non-Adaptive Learning), where students possess adequate metacognitive knowledge about the task but do not employ sufficient monitoring and evaluation (M/E) strategies. This discrepancy leads to either successful or unsuccessful outcomes. In Routine Learning, participants demonstrate low levels of M/E strategies but high levels of metacognitive knowledge about tasks (MKT). They are familiar with the problem's context and have a strong understanding of it, allowing them to solve it successfully despite using fewer M/E strategies. In contrast, participants in Non-Adaptive Learning share similar characteristics but fail to solve the problem, even though they initially have a reasonably good understanding of it.

The study discussed how different episodes of problem-solving activities can shape students' perceptions of their task performance, either positively or negatively, and how these experiences influence their deeper understanding of the subject matter. It also examined the critical roles of metacognitive knowledge about tasks (MKT) and monitoring and evaluation in enhancing the teaching and learning processes within mathematics and engineering education, highlighting their impact on students' ability to navigate complex tasks and refine their problem-solving skills.

Authored by
Dr. Oenardi Lawanto (Utah State University) and Zain ul Abideen (Utah State University)

This paper presents four complementary course projects designed to enhance undergraduate students’ understanding of software performance engineering (SPE). These projects, integrated into various courses in a software engineering curriculum, collectively address key aspects of performance-related issues and solutions, fulfilling the broader objectives of fostering performance awareness and competency.

The first project developed for SSW 345: Software Modeling and Simulation, uses a set of Unity-based games to illustrate eight fundamental performance concepts. Students learn through gameplay and LoadTester-based simulations, gaining insights into inefficiencies and system performance under load. In SSW 567: Software Testing, the second project introduces students to performance testing through a Machine Readable Travel Document (MRTD) system, highlighting both system and test case performance. The third project, conducted in SSW 533: Software Cost Estimation, focuses on real-world return-on-investment (ROI) analysis of performance issues, allowing students to analyze issue reports and assess the cost-benefit of resolving performance-related challenges. Lastly, in SSW 315: Object Oriented Design, students compare the performance of object-oriented and procedural programming paradigms and data structures, exploring the trade-off between performance and maintainability. Our evaluation plan extends over multiple course iterations, utilizing detailed quantitative and qualitative metrics to continually assess the project's impact on course outcomes.

Together, these projects form a cohesive learning experience that builds students’ SPE skills, connecting theoretical concepts with real-world applications and performance trade-offs, thus preparing them for the complexities of modern software systems.

Authored by
Dr. Lu Xiao (Stevens Institute of Technology (School of Engineering and Science)), Yu Tao (Stevens Institute of Technology (School of Humanities, Arts, and Social Sciences)), Dr. Andre Benjamin Bondi (Stevens Institute of Technology (School of Systems & Enterprises)), and Eman Abdullah AlOmar (Stevens Institute of Technology (School of Engineering and Science))