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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. 

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 (Mendoza Diaz et al., 2018, 2019; Richard et al., 2016; Wickliff et al., 2018). 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. 

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.