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The lack of diversity within engineering degree programs and occupations has been an ongoing concern for decades. National engineering programs have placed a high priority on broadening participation in engineering and making the engineering culture more inclusive. Specifically, the cultivation of engineering students' inclusive professional engineering identities (IPEIs)—or the value these individuals place on diversity and their willingness to act inclusively within engineering contexts—might be one way to address this long‐standing lack of representation. Rooted in theoretical contexts regarding professional identity development, the purpose of this study is to uncover developmental patterns of first‐year engineering students' IPEIs and factors that influence IPEI cultivation .Methods This study built upon the previous variable‐centered research findings regarding IPEI development. Specifically, the person‐centered approach of random intercept latent transition analysis (RI‐LTA) was utilized. RI‐LTA allows for the detection of different meaningful groups of individuals demonstrating similarities on the construct and investigating these groups for probabilistic changes over time. Four IPEI groups of students emerged with IPEI developmental patterns that were not always stable. Student IPEI classifications differed significantly across gender and students' levels of engineering identity. Furthermore, a series of intervention experiences instigated an even more malleable nature to student IPEIs. Engineering students' IPEIs demonstrate some likelihood to change over time, with intervention experiences enhancing the likelihoods for changes to occur. Continuing to investigate factors influencing the positive cultivation of students' IPEIs is fundamental to broadening participation in engineering and making the engineering culture more inclusive 

Abstract Background In Spring 2020, the COVID-19 pandemic sent universities into emergency remote education. The pandemic has been disruptive but offers the opportunity to learn about ways to support students in other situations where abrupt changes to teaching and learning are necessary. Purpose/Hypothesis We described the responses of engineering and computer science students to a series of prompts about their experiences with remote learning. Design/Method Data about students' remote learning experiences were collected from undergraduate engineering and computer science students at four different universities through an end-of-semester survey. Descriptive statistics were calculated, and qualitative responses were analyzed using qualitative content analysis through the lenses of master narrative theory and sociocultural theory. Results Student responses revealed how their individual circumstances combined to reduce motivation, create home environments detrimental to completing schoolwork, and increase stress. Many students described the negative impacts of remote learning, but some students found positive aspects of the situation. The majority of students did not indicate a change in their desire or plans to pursue engineering or computer science majors. Conclusions There was wide variation in how students experienced the disruption to university learning during Spring 2020. Implications of this paper can help not only in cases where emergency remote learning is needed in the future but also as universities seek to return to “normal” operations in 2022 and beyond. 

Evaluators often find themselves in situations where resources to conduct thorough evaluations are limited. In this paper, we present a familiar instance where there is an overwhelming amount of open text to be analyzed under the constraints of time and personnel. In instances when timely feedback is important, the data are plentiful, and answers to the study questions carry lower consequences, we build a case for using a machine learning, in particular a sentiment analysis. We begin by explaining the rationale for the use of sentiment analysis and provide an introduction to this method. Next, we provide an example of a sentiment analysis leveraging data collected from a program evaluation of an engineering education intervention, specifically to text extracted from student reflections of course activities. Finally, limitations of sentiment analysis and related techniques are discussed as well as areas for future research.