This Work-in-Progress research paper presents the examination of the impact of COVID-19 interruptions on first-year engineering students' intentions to persist and whether their intentions varied by race/ethnicity and financial need status. Of 7,159 first-year students involved in this study (pre-COVID$n{=}$3,660, COVID cohort$n=3,499$), The results indicated students in the COVID-19 cohort had higher persistence than the pre-COVID cohort. Regardless of the cohort, Asian students were more likely to persist, Black students were less likely to persist than White students, and students who received financial aid were less likely to persist than those without financial need. Furthermore, Black students and students with greater financial need were less likely to persist- across both cohorts. Although, COVID-19 appears to have not exacerbated these already existing gaps. Concerning the retention status across cohorts by financial need status, our findings revealed that the relationship between financial need status and retention status was not different between the two cohorts. Discussion and implications are discussed.

Enculturation to engineering is a topic of interest to professional organizations such as ASEE or IEEE. Enculturation can be understood as the process by which students are assimilated into the engineering culture. This culture involves the base knowledge, practices, and values shared by the community of practicing engineers. Both the culture and its assimilation are somewhat obscure and often attributed to role-modeling and hidden curriculum. Nevertheless, undergraduate students are expected to undergo this assimilation process, and by the end of a five-year program, they behave, talk, and do what engineers do. An approach to this process is the well-established model of engineering identity. This model of identity, however, limits its scope to the intrinsic process occurring in the student without paying much attention to the support systems expected to welcome and nurture students into the profession. Enculturation is a relatively new model that proposes both intrinsic and extrinsic factors affecting this assimilation of students. During the Spring of 2022, a team of researchers at a U.S. Southwest institution applied a survey to operationalize the extrinsic factors of a recently outlined engineering model of enculturation to understand the impact of COVID on engineering students. Eight Likert-based questions were asked to 534 engineering undergraduate students at different school year classifications (e.g., sophomore, junior, or senior). The model of enculturation tested eight extrinsic factors involving (1) engineering design, (2) teamwork, (3) engineering profession, (4) ethics, (5) engineering communications, (6) mathematical/physical modeling, (7) problem-solving, and (8) algorithmic/computational thinking. Additional Likert-based questions asked students about the perceived impact of COVID on their educational experience. The research questions guiding their investigation were: (a) How are the dimensions of enculturation to engineering changing across engineering undergraduate classifications?, and (b) How is enculturation associated with the COVID impact on students? Preliminary results show that six of the factors (namely 1–5 and 7) increase by students' school classification. This means that students in the lower years of their undergraduate program perceive their enculturation, as portrayed by factors 1–5 & 7, less than students at more advanced stages of their program. In terms of the perceived impact of COVID, only two factors showed negative correlations with this perceived impact The factors were ethics and algorithmic/computational thinking. In other words, the worse the student's perception of COVID's impact on their educational experience, the less they endorsed their enculturation in the impact of society of their technical solutions and their use of programming languages. While these results show encouraging applicability of the enculturation model, it can only be considered a first approach to a more fully developed model. The researchers expect to engage the FIE community in a discussion leading to a more refined enculturation model.

Prior research shows that ethical misconduct occurs in all sectors of science and engineering, including laboratory-based research, engineering design, and data science and modeling. Problems have arisen at individual and across organizations. Many of the recent unethical incidents in scientific research involve data fabrication and falsification, data tampering, plagiarism, intellectual theft, and misinformation. A few examples of recently reported unethical behaviors in engineering in industry include the delayed response in the GM ignition switch failure case, the diesel emission software manipulation case at Volkswagen, and the lack of attention to user privacy by companies such as Facebook. These problems arise from numerous sources. One is insufficient ethical policies at the leadership level, as prior research has reported a positive correlation between companies' success and strong ethical policies at that level. Yet the idea that these problems can be solved solely at the leadership level seems flawed, as it is difficult to transfer ethical practices from the leadership to their team members if the latter have a weak or flawed understanding of what ethical responsibility entails, if without a proper ethical research efficacy assessment method and improvement plan. This paper presents preliminary findings from a National Science Foundation (NSF) funded project on ethical and responsible research (ER2) in science and engineering. More specifically, the NSF project aims to enhance ethical self-efficacy and competence in undergraduate engineering students through curricular interventions. While the overall goal of the NSF project also includes development of a validated scale to assess the ethical self-efficacy and competency of engineering students, the objective of this paper is limited to conduct a survey of first-year engineering students at Texas A&M University to establish baseline results for future work. Furthermore, based on the survey results, the paper also investigates differences in the ethical self-efficacy level of students based on their demographic attributes and high school education on ethics.

The traditional approach to prioritization of customer satisfaction (CS) attributes includes methods such as analytic hierarchical process (AHP) that do not consider the correlation among CS attributes. This paper presents an analytic network process based framework that allows decision makers to prioritize the CS attributes by considering not only the correlation among the attributes themselves, but also the cross correlation among attributes and factors. Furthermore, the proposed framework employs Bayesian Belief Network methodology to deal with uncertainty in prioritization process due to subjectivity of the information during the early stages of product development. The belief probabilities are expressed in terms of conditional probabilities that reflect the contribution of an attribute toward a given prioritization criterion. In addition, we propose a novel approach to estimate the belief probabilities by considering different states of an attribute such as strong, average, and null. This approach improves the precision of the belief probabilities that are usually estimated through expert interviews. The framework is illustrated with an automotive industry case study with results presented for two disparate vehicle types. The model results are also compared with those of a traditional AHP model to show the benefits of the proposed framework. Lastly, sensitivity analysis is performed to understand the impact of network structural uncertainty on the prioritization decisions thereby demonstrating the robustness of the framework.