This research WIP paper on innovative practices aims to identify the digital competencies required by graduates to strengthen their graduate profile, with the components of these competencies including the labor requirements of employers. The research is mixed, it started with a diagnosis of 132 digital questionnaires directed to students. The structure of the questionnaire took as a reference the digital competence frameworks proposed by researchers on the subject. The second step consists of the analysis of interviews with 5 employers from various productive sectors and 10 specialized teachers to finally determine the proposal for Digital Competencies.

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.

The need for more underrepresented minorities (URMs) in U.S. engineering faculty has been highlighted by numerous studies and reports. The Pew Research Center emphasizes the importance of faculty representation as a drive for minority students. Current faculty demographics mirror undergraduate demographics in the sense that White and Asian males are the majority for both groups in engineering. This creates a challenge for participation. Efforts to improve this situation are occurring in Historically Black Colleges and Universities (HBCU) and Hispanic Serving Institutions (HSI) and in programs such as NSF Louis Stokes Alliances for Minority Participation (LSAMP) or the Alliances for Graduate Education and the Professoriate (AGEP). Also, the Garcia et al. model provides a framework for servingness that accentuates the exposure of Latinx students to Latinx faculty. With the intent to increase representation of Latinx in the professoriate, a team of researchers at a Southwestern Institution investigated the motivations of minority undergraduate students to become faculty and utilized Garcia et al.'s framework. Researchers posed the research question: What motivates URM engineering students to attend graduate school? A survey framed using the servingness model was created, and student members of the Society for Hispanic Professional Engineers (SHPE) were invited to participate. The survey also collected other demographic information such as participants' first-generation status in college. The survey's final question was open-ended to allow for free-response comments from participants. Preliminary findings of this investigation are corroborating the importance of minority faculty in increasing undergraduate students' motivation to pursue faculty careers. Other important aspects such as the culture of the institution (i.e., how it promotes the pursuit of graduate school transfer) are emerging as critical aspects that increase minority students' participation in graduate education and academia. Specific demographic groups are being analyzed to detect correlations between social identities (e.g., gender, race/ethnicity) and obstacles to their motivation to pursue graduate school and/or academia. The authors of this work in progress expect ASEE-IEEE audiences to provide valuable input in the development of this investigation.

Addressing the interdisciplinary challenges of teaching artificial intelligence in higher education while embracing and comprehending diverse backgrounds, promoting inclusivity and diversity, and reducing socioeconomic barriers are crucial for creating a learning environment that can equip students with the necessary skills and knowledge to navigate the complexities of the field and drive innovation in the rapidly evolving world of artificial intelligence. In order to undertake this challenge, the present work proposes a general introductory course to artificial intelligence in the early semesters of the bachelor's degree, without any tendencies on the specificity of the problems to solve. Prior to this dissemination, authors have pilot-tested the introductory lesson. This paper presents the findings of this pilot and proposes a new approach to diversifying the pool of students who can benefit from an intro course in AI. We expect the FIE audience to provide us feedback on the best strategy to follow for implementation which is expected to occur during Spring or Fall of 2024.

This research is focused on determining components of the Digital Competences of higher education students to strengthen the graduation profile with application at work and educational training. Being its main focus the harnessing the potential of the new information and communication technologies to face the realities of future environments with a focus on social Constructivism; to have concretes scopes making use of the qualitative and quantitative (mixed) research methodology, with 4 phases of development. It will be implemented in educational engineering programs of a Higher Education Institution in central Mexico; They consist of Phase 1.- Surveys and interviews, Phase 2.- Data analysis, Phase 3.- Definition of Digital Competences and their development, and Phase 4.- Reporting and dissemination of results.

This research category paper examines the impact of computational thinking within first-year engineering courses on student pathways into engineering. Computational thinking and programming appear in many introductory engineering courses. Prior work found that early computational thinking development is critical to the formation of engineers. This qualitative research paper extends the research by documenting how pre-university privileges impact first-year student trajectories into engineering through a qualitative examination of student interviews from three institutions with different processes for matriculation into engineering majors. We identify the underlying assumptions of meritocracy that are concealing the role of educational privilege in selecting which engineering students will be allowed to join the field. We provide a suggestion for how institutions can include computational thinking in introductory engineering courses with less risk of furthering the marginalization of students with few academic privileges.

This research-track work-in-progress paper contributes to engineering education by documenting progress in developing a new standard Engineering Computational Thinking Diagnostic to measure engineering student success in five factors of computational thinking. Over the past year, results from an initial validation attempt were used to refine diagnostic questions. A second statistical validation attempt was then completed in Spring 2021 with 191 student participants at three universities. Statistics show that all diagnostic questions had statistically significant factor loadings onto one general computational thinking factor that incorporates the five original factors of (a) Abstraction, (b) Algorithmic Thinking, (c) Decomposition, (d) Data Representation and Organization, and (e) Impact of Computing. This result was unexpected as our goal was a diagnostic that could discriminate among the five factors. A small population size caused by the virtual delivery of courses during the COVID-19 pandemic may be the explanation and a third round of validation in Fall 2021 is expected to result in a larger population given the return to face-to-face instruction. When statistical validation is completed, the diagnostic will help institutions identify students with strong entry level skills in computational thinking as well as students that require academic support. The diagnostic will inform curriculum design by demonstrating which factors are more accessible to engineering students and which factors need more time and focus in the classroom. The long-term impact of a successfully validated computational thinking diagnostic will be introductory engineering courses that better serve engineering students coming from many backgrounds. This can increase student self-efficacy, improve student retention, and improve student enculturation into the engineering profession. Currently, the diagnostic identifies general computational thinking skill

This full research-track paper demonstrates growth in computational thinking in a cohort of engineering students completing their first course in engineering at a large Southwestern university in the United States. Computational thinking has been acknowledged as a key aspect of engineering education and an intrinsic part of multiple ABET outcomes. However, computing is an area where some students have more privileges (e.g. access and exposure to meaningful use of computers) than others. Integrating computing into engineering, especially early in the curriculum, may exacerbate existing experiential disadvantages students from excluded social identities experience. Most introductory engineering programs have a component of programming and/or computational thinking. A comprehensive literature review showed that no existing computational thinking framework fully met the needs of students and professors in engineering and computer science. As a result, this team created the Engineering Computational Thinking Diagnostic (ECTD). This diagnostic was assessed and improved during the 2019-2020 academic year. Data was collected from a cohort in a first-year engineering course that included topics in mathematics, engineering problem solving, and computation. Pre- and post-test data analysis with 62 participants documents statistically significant student growth in computational thinking in this course. Significant differences were not found by gender or a limited racially-based analysis. This diagnostic is of interest and relevance to all institutions providing engineering and computing programs. The short-term impact of this research includes an innovative approach to gauge student abilities in computational thinking early in a course in order to add appropriate intervention activities into lesson plans. The long-term impact is the creation of a measurement of student learning of computational thinking in engineering for courses and programs that wish to develop this important skill in their students.

Cloud computing has emerged to be one of the leading professional competences desired by modern day employers, especially in the computing profession. We use computing as an umbrella term that includes Computer Science, Information Systems, Software Engineering, and other related courses. The benefits and value proposition of cloud computing have made it a desirable course to be taught across computing curricular. However, teaching and learning cloud computing comes with daunting challenges that often discourage educators. As a result, cloud computing is yet to be part of the computing curriculum in a vast majority of higher institutions in the USA; thereby denying students the myriad career and employment opportunities peculiar to their peers with cloud computing skills. The aim of this panel therefore is to analyze the challenges of teaching cloud computing, proffer solutions to those challenges, and develop an approach that can be adopted by computing instructors who desire to incorporate cloud computing competences into their curriculum. In addition, participants will have the opportunity to learn and access resources provided by 'AWS Educate' for teaching and learning cloud computing.