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Literature has consistently pointed to the significant role of personality in students’ decisions to participate in study abroad programs. Studies have highlighted how such experiences are impacted by key personality traits such as extraversion, agreeableness, and neuroticism, and social traits such as social information processing, social skills, and social awareness. Yet there remains a notable gap in the limited examination of students’ personality attributes and their impact on study abroad outcomes. To address this gap, this study investigates the effects of students’ personality attributes and demographic attributes on their transformative learning experiences during their study abroad programs using Mezirow’s transformative learning theory. The research integrates quantitative data collected through instruments. Qualitative insights gathered from open-ended questions in the survey to comprehensively investigate important associations between student attributes and their transformative learning experiences during study abroad programs. Results showed that personality traits, particularly openness and agreeableness, and social skills (a social intelligence scale construct) had a strong correlation with different phases of the journey of transformation. Additionally, the results indicated a potential association between students’ academic majors and the likelihood of experiencing shifts in their epistemic dimension of habits of mind during their respective short-term study abroad programs. 

Changes in course delivery mechanisms necessitated by the COVID-19 pandemic created an opportunity to develop a Virtual International Collaborative Experiential Program (VICEP) as an alternative to traditional, travel-based study abroad programs. This paper presents the results of an investigation of engineering students’ motivation, perceived challenges, and preferred geographic areas for the VICEP. A sample of 116 first-year engineering students at the University of Cincinnati responded to survey items regarding their perceptions of motivation to participate in the VICEP, including in terms of expectancy, value, and cost, along with open-ended questions. Both male and female students scored the highest on value and the lowest on cost but with different weights. However, gender differences in the expectancy, value, and cost were not statistically significant. Intercultural collaboration and learning opportunities were significantly more important for female students than for males, and the engaged learning environment of the program and career skills development were more important for male students than for females. Time commitment and the structure of the program as well as the stress endured during the study abroad were strongly negative factors, more so for male students. Interestingly, the virtual nature of the project and the existence (or not) of incentives were not encouraging to most students. Structuring the world into seven geographic regions, the most preferred regions for virtual collaboration have the common feature of being technologically developed, except China which was among the lowest-ranking countries/regions. Preferences for geographical regions between male and female students was significant only for some regions. The present research provides valuable information for faculty leading virtual intercultural collaborative experiences. 

This literature review was conducted as a preliminary assessment of the available research literature produced by the engineering education community on climate affecting the retention of engineering doctoral students from diverse backgrounds. We seek to understand this specific student group’s retention in context of organizational science--specifically as an organizational climate issue--- and use an intersectional approach to consider the meaning and relevance of students’ belonging, simultaneously, to multiple social categories such as gender, sexual orientation, socioeconomic background, race/ethnicity, and disability status. We review the literature on engineering doctoral students produced by the engineering education community as a first step to building a climate survey instrument. The objective of this literature review is to explore how the concept of ‘climate’ is being used in context of doctoral engineering student retention to degree completion, and we gather a body of evidence of climate factors. To do this, we conducted a targeted literature review and used organizational climate and intersectionality as our approach to interpreting the literature, as we aim to understand how climate affects the retention of engineering doctoral students from diverse backgrounds. In this paper, we first briefly present our understanding of climate as grounded in organizational science and intersectional theory. We then explain our methodology and finally discuss our analysis of the doctoral engineering student literature in engineering. 

This project has been dedicated to advance the way computational thinking is taught to engineering undergraduate students with a multitude of social identities. It is an expectation that with the understanding of the multiple factors that affect computational thinking skills development, students succeed in enculturating to the engineering professional practice. During the third year of this project, the first major result is the conclusion of the validation process of the Engineering Computational Thinking Diagnostic (ECTD) making use of exploratory and confirmatory factor analyses (EFA-CFA). Our validation showed that the ECTD questions cluster in one factor, what we call the computational thinking factor for engineers. Other validation statistical processes (i.e. correlations, regressions, ANOVA and t-tests) proved the predictability potential use of this tool in determining how well prepared students arrive to the engineering classroom and how their prior coding experience can determine their success in introductory coding engineering courses. The second major result is the revelation that the inequities caused by the many forms of privilege that some engineering students benefit from are being exacerbated by the integration of computational thinking into introductory engineering classes. Due to pandemic-related challenges in recruiting a representative sample of participants, the majority of the self-selected participants in our research identify with groups with disproportionately large participation in engineering (specifically White and Asian) and are academically successful in engineering. To respond to this challenge we are seeking to broaden our perspective by seeking participants with failing grades for a final round of data collection, although we are well aware that students in this group are often reluctant to participate in research. The fourth and last major result is related to the position of stress versus Artificial Intelligence (AI) perceptions, both part of the ECTD instrument. The position of stress questions involved perceived difficulty and confidence level after taking the ECTD. The artificial intelligence question asked the perceived impact of AI in students’ future career prospects. Preliminary analysis is suggesting that confidence level is correlated with AI positive perceptions. Although not part of the original NSF grant, we considered AI the natural evolution of computational thinking in the formation of engineers and plan to continue our work in this direction. 

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 

Spatial visualization is defined as the “process of apprehending, encoding, and mentally manipulating three-dimensional spatial forms.” Spatial cognition has been widely studied throughout psychology and education from more than 100 years. Engineering students and engineering professionals exhibit some of the highest levels of spatial skills compared to their counterparts in other majors/careers. Numerous studies have shown the link between spatial skills and success in engineering and interventions aimed at enhancing spatial skills have demonstrated a concomitant improvement in student success, as measured by grades earned and retention/graduation. The question remains: How do well-developed spatial skills contribute to engineering student success? One hypothesis is that spatial skills contribute to a student’s ability to solve unfamiliar problems. Recent studies have demonstrated that spatial skills contribute to success in solving problems from mathematics, chemical engineering, and electrical engineering. The study outlined in this paper, extends this work to examine the impact of spatial skills on the ability to solve problems from engineering mechanics. In this pilot study, a total of 47 students from upper division mechanical engineering courses completed a test of spatial skills and also were asked to solve 5-6 problems from introductory statics/physics. Results showed that a statistically significant positive correlation was found between spatial scores and the percent correct on the mechanics test. Individual problems were also examined to determine if spatial skills appeared to play a role in their solution. Some problems appeared to rely on spatial thinking; others did not. Results from this pilot study will be used to conduct an in-depth study examining the relationship between spatial skills and solving problems in engineering mechanics. This paper outlines key findings from this pilot study and makes recommendations for future work in this area. 

Computational thinking is understood as the development of skills and knowledge in how to apply computers and technology to systematically solve problems. Computational thinking has been acknowledged as one key aspect in the taxonomy of engineering education and implied in multiple ABET student outcomes. Moreover, many introductory engineering courses worldwide have a component of programming or computational thinking. A preliminary study of enculturation to the engineering profession found that computational thinking was deemed a critical area of development at the early stages of instruction. No existing computational thinking framework was found to fully meet the needs of engineers, based on the expertise of researchers at three different institutions and the aid of a comprehensive literature review. As a result, a revised version of a computational thinking diagnostic was developed and renamed the engineering computational thinking diagnostic (ECTD). The five computational thinking factors of the ECTD are (1) Abstraction, (2) Algorithmic Thinking and Programming, (3) Data Representation, Organization, and Analysis, (4) Decomposition, and (5) Impact of Computing. This paper describes the development and revisions made to the ECTD using data collected from first-year engineering students at a Southwestern public university. The goal of the development of the ECTD is to capture the entry and exit skill levels of engineering students in an engineering program.