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1. Demographic Data Collection on LGBTQ+ Identities: Barriers and Motivations

   Sanchez, Daniel ; Chavela Guerra, Rocio ; Farrell, Stephanie ( January 2022 , ASEE Annual Conference Proceedings)

   Although recent studies highlight the unique difficulties that lesbian, gay, bisexual, transgender, and queer (LGBTQ+) engineering professionals face in their careers, the availability of data concerning this underserved population remains scant. Postsecondary educational institutions play a critical role in the collection of LGBTQ+ demographic data related to sexual orientation and gender identity (SOGI) among students, faculty, and staff. Such data can be utilized to measure retention and success of LGBTQ+ individuals. However, many institutions choose not to collect these data for various reasons, which can potentially further the marginalization of LGBTQ+ individuals in academic settings. This study explores the motivations behind academic leaders—which includes facilitators of the demographic information collection process and advocates for the collection of SOGI data—along with the barriers that hinder efforts to make the collection of SOGI data a standard practice. We first administered a nation-wide survey to the American Society for Engineering Education (ASEE) Engineering Deans Council to determine what types of SOGI data, if any, these institutions collect. Among the 46 respondents, we found that three institutions collect some type of SOGI data for students, faculty, or staff. Survey respondents were then invited to participate in semi-structured interviews to discuss their insights into institutional practices with respect to SOGI data collection. In total, six institutions are represented through the perspectives of the 6 interviewees. We analyzed the transcripts from the interviews and identified themes within two categories: barriers and motivations. Although SOGI data collection is in nascent stages, this study provides insight into the critical conversations academic leaders navigate to better serve the LGBTQ+ community, which may aid further efforts to implement SOGI data collection in academia. 

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2. Understanding the ‘us all’ in Engineering 4 Us All through the Experiences of High School Teachers

   Berhane, B. ; Dalal, M. ; Klein-Gardner, S. ; Carberry, A. ; Reid, K. ; Beauchamp, C. ; Kouo, J. ; Pines, D. ( January 2021 , 3rd annual CoNECD – The Collaborative Network for Engineering and Computing Diversity Conference)

   null (Ed.)

   As our nation’s need for engineering professionals grows, a sharp rise in P-12 engineering education programs and related research has taken place (Brophy, Klein, Portsmore, & Rogers, 2008; Purzer, Strobel, & Cardella, 2014). The associated research has focused primarily on students’ perceptions and motivations, teachers’ beliefs and knowledge, and curricula and program success. The existing research has expanded our understanding of new K-12 engineering curriculum development and teacher professional development efforts, but empirical data remain scarce on how racial and ethnic diversity of student population influences teaching methods, course content, and overall teachers’ experiences. In particular, Hynes et al. (2017) note in their systematic review of P-12 research that little attention has been paid to teachers’ experiences with respect to racially and ethnically diverse engineering classrooms. The growing attention and resources being committed to diversity and inclusion issues (Lichtenstein, Chen, Smith, & Maldonado, 2014; McKenna, Dalal, Anderson, & Ta, 2018; NRC, 2009) underscore the importance of understanding teachers’ experiences with complementary research-based recommendations for how to implement engineering curricula in racially diverse schools to engage all students. Our work examines the experiences of three high school teachers as they teach an introductory engineering course in geographically and distinctly different racially diverse schools across the nation. The study is situated in the context of a new high school level engineering education initiative called Engineering for Us All (E4USA). The National Science Foundation (NSF) funded initiative was launched in 2018 as a partnership among five universities across the nation to ‘demystify’ engineering for high school students and teachers. The program aims to create an all-inclusive high school level engineering course(s), a professional development platform, and a learning community to support student pathways to higher education institutions. An introductory engineering course was developed and professional development was provided to nine high school teachers to instruct and assess engineering learning during the first year of the project. This study investigates participating teachers’ implementation of the course in high schools across the nation to understand the extent to which their experiences vary as a function of student demographic (race, ethnicity, socioeconomic status) and resource level of the school itself. Analysis of these experiences was undertaken using a collective case-study approach (Creswell, 2013) involving in-depth analysis of a limited number of cases “to focus on fewer "subjects," but more "variables" within each subject” (Campbell & Ahrens, 1998, p. 541). This study will document distinct experiences of high school teachers as they teach the E4USA curriculum. Participants were purposively sampled for the cases in order to gather an information-rich data set (Creswell, 2013). The study focuses on three of the nine teachers participating in the first cohort to implement the E4USA curriculum. Teachers were purposefully selected because of the demographic makeup of their students. The participating teachers teach in Arizona, Maryland and Tennessee with predominantly Hispanic, African-American, and Caucasian student bodies, respectively. To better understand similarities and differences among teaching experiences of these teachers, a rich data set is collected consisting of: 1) semi-structured interviews with teachers at multiple stages during the academic year, 2) reflective journal entries shared by the teachers, and 3) multiple observations of classrooms. The interview data will be analyzed with an inductive approach outlined by Miles, Huberman, and Saldaña (2014). All teachers’ interview transcripts will be coded together to identify common themes across participants. Participants’ reflections will be analyzed similarly, seeking to characterize their experiences. Observation notes will be used to triangulate the findings. Descriptions for each case will be written emphasizing the aspects that relate to the identified themes. Finally, we will look for commonalities and differences across cases. The results section will describe the cases at the individual participant level followed by a cross-case analysis. This study takes into consideration how high school teachers’ experiences could be an important tool to gain insight into engineering education problems at the P-12 level. Each case will provide insights into how student body diversity impacts teachers’ pedagogy and experiences. The cases illustrate “multiple truths” (Arghode, 2012) with regard to high school level engineering teaching and embody diversity from the perspective of high school teachers. We will highlight themes across cases in the context of frameworks that represent teacher experience conceptualizing race, ethnicity, and diversity of students. We will also present salient features from each case that connect to potential recommendations for advancing P-12 engineering education efforts. These findings will impact how diversity support is practiced at the high school level and will demonstrate specific novel curricular and pedagogical approaches in engineering education to advance P-12 mentoring efforts. 

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3. A Novel Approach to Detecting and Mitigating Keyloggers

   Elelegwu, D ; Chen, L ; Ji, Y ; and Kim, J. ( March 2024 , Proceedings of the IEEE SoutheastCon)

   As the digital world gets increasingly ingrained in our daily lives, cyberattacks—especially those involving malware—are growing more complex and common, which calls for developing innovative safeguards. Keylogger spyware, which combines keylogging and spyware functionalities, is one of the most insidious types of cyberattacks. This malicious software stealthily monitors and records user keystrokes, amassing sensitive data, such as passwords and confidential personal information, which can then be exploited. This research introduces a novel browser extension designed to effectively thwart keylogger spyware attacks. The extension is underpinned by a cutting-edge algorithm that meticulously analyzes input-related processes, promptly identifying and flagging any malicious activities. Upon detection, the extension empowers users with the immediate choice to terminate the suspicious process or validate its authenticity, thereby placing crucial real-time control in the hands of the end user. The methodology used guarantees the extension's mobility and adaptability across various platforms and devices. This paper extensively details the development of the browser extension, from its first conceptual design to its rigorous performance evaluation. The results show that the extension considerably strengthens end-user protection against cyber risks, resulting in a safer web browsing experience. The research substantiates the extension's efficacy and significant potential in reinforcing online security standards, demonstrating its ability to make web surfing safer through extensive analysis and testing. 

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4. Addressing Recruitment Issues with Potential Transfer Students from State Technical Colleges

   Conner, S. ; Duncan, L. ; Averitt, L. ; Boyer, D..M. ; Kennedy, M ( August 2022 , ASEE Annual Conference proceedings)

   In this work-in-progress paper we present emergent recruitment issues encountered during an ongoing design-based project with participants from two-year colleges for an NSF-funded scholarship program. Our hope is to connect with researchers who have previously explored similar issues or may be experiencing them in their current work. Student Pathways in Engineering and Computing for Transfer Students (SPECTRA) is an NSF S-STEM program that provides financial assistance to students transferring from the South Carolina Technical College System into Engineering or Computing majors at Clemson University [1]. SPECTRA also assists students by connecting them with peers at the technical colleges who move together through the transfer process to Clemson and are supported by the SPECTRA program until graduation. In addition to exploring the experiences of current SPECTRA participants, we investigate how the project can be scaled to include more students and sustained after NSF support ends. The 2021-2022 academic year is the third of the five-year program, although, given emergent recruitment issues, we foresee application for a no-cost extension. The primary concern is the low number of students currently supported in comparison to our goals, highlighting recruitment for further examination. We planned to support up to twenty students in year 1, 52 students in year 2, 70 students in year 3, but our actual numbers in the first three years are 7, 12, and 28 students. Given this trend, our concern over how we recruit students into SPECTRA is now at the forefront of our work. The program is not reaching those students who are eligible, and low recruitment has limited the quality of research needed to inform the construction of a sustainable program. To explore recruitment, we have added interviews with potential students at the technical colleges. In addition to this interview process, we have reviewed our internal practices, analysed existing public information and social media from similar programs, and reviewed existing literature from related research and practice. We identified aspects that may have impacted our current situation. The first was explicit, being the impact of COVID-19 on our ability to hold in-person recruitment events. Similar to studies that have identified other COVID-19 impacts to two-year institutions such as “retention rates declined the most in the community college sector (-2.1 pp to 51.6%)” [2], “disparities in upward transfer mobility increased during the pandemic year” [3], and community colleges being hit hardest “with a 9.4 percent decline” in enrollment [4], we intend to further clarify the influence of COVID-19 on our context. COVID-19 also played a role with regard to the need for scholarship funds, as one of the technical colleges in our program used federal relief funds to provide free tuition for all students during the 2020-2021 academic year. Another potential impact is the effectiveness of the SPECTRA webpages and other online materials to meet the needs of potential students considering the program. In this work-in-progress paper, we will share how we are addressing recruitment issues and how new interventions are impacting recruitment. 

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5. Impacts Resulting from a Large-Scale First-Year Engineering and Computer Science Program on Students’ Successful Persistence Toward Degree Completion

   Ragusa, Gisele ; Allen, Emily L. ; Menezes, Gustavo B. ( June 2020 , ASEE Annual Conference Proceedings 2020)

   There is a critical need for more students with engineering and computer science majors to enter into, persist in, and graduate from four-year postsecondary institutions. Increasing the diversity of the workforce by inclusive practices in engineering and science is also a profound identified need. According to national statistics, the largest groups of underrepresented minority students in engineering and science attend U.S. public higher education institutions. Most often, a large proportion of these students come to colleges and universities with unique challenges and needs, and are more likely to be first in their family to attend college. In response to these needs, engineering education researchers and practitioners have developed, implemented and assessed interventions to provide support and help students succeed in college, particularly in their first year. These interventions typically target relatively small cohorts of students and can be managed by a small number of faculty and staff. In this paper, we report on “work in progress” research in a large-scale, first-year engineering and computer science intervention program at a public, comprehensive university using multivariate comparative statistical approaches. Large-scale intervention programs are especially relevant to minority serving institutions that prepare growing numbers of students who are first in their family to attend college and who are also under-resourced, financially. These students most often encounter academic difficulties and come to higher education with challenging experiences and backgrounds. Our studied first-year intervention program, first piloted in 2015, is now in its 5th year of implementation. Its intervention components include: (a) first-year block schedules, (b) project-based introductory engineering and computer science courses, (c) an introduction to mechanics course, which provides students with the foundation needed to succeed in a traditional physics sequence, and (d) peer-led supplemental instruction workshops for calculus, physics and chemistry courses. This intervention study responds to three research questions: (1) What role does the first-year intervention’s components play in students’ persistence in engineering and computer science majors across undergraduate program years? (2) What role do particular pedagogical and cocurricular support structures play in students’ successes? And (3) What role do various student socio-demographic and experiential factors play in the effectiveness of first-year interventions? To address these research questions and therefore determine the formative impact of the firstyear engineering and computer science program on which we are conducting research, we have collected diverse student data including grade point averages, concept inventory scores, and data from a multi-dimensional questionnaire that measures students’ use of support practices across their four to five years in their degree program, and diverse background information necessary to determine the impact of such factors on students’ persistence to degree. Background data includes students’ experiences prior to enrolling in college, their socio-demographic characteristics, and their college social capital throughout their higher education experience. For this research, we compared students who were enrolled in the first-year intervention program to those who were not enrolled in the first-year intervention. We have engaged in cross-sectional 2 data collection from students’ freshman through senior years and employed multivariate statistical analytical techniques on the collected student data. Results of these analyses were interesting and diverse. Generally, in terms of backgrounds, our research indicates that students’ parental education is positively related to their success in engineering and computer science across program years. Likewise, longitudinally (across program years), students’ college social capital predicted their academic success and persistence to degree. With regard to the study’s comparative research of the first-year intervention, our results indicate that students who were enrolled in the first-year intervention program as freshmen continued to use more support practices to assist them in academic success across their degree matriculation compared to students who were not in the first-year program. This suggests that the students continued to recognize the value of such supports as a consequence of having supports required as first-year students. In terms of students’ understanding of scientific or engineering-focused concepts, we found significant impact resulting from student support practices that were academically focused. We also found that enrolling in the first-year intervention was a significant predictor of the time that students spent preparing for classes and ultimately their grade point average, especially in STEM subjects across students’ years in college. In summary, we found that the studied first-year intervention program has longitudinal, positive impacts on students’ success as they navigate through their undergraduate experiences toward engineering and computer science degrees. 

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