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1. Computational Thinking in the Formation of Engineers (Year 1)

   Mendoza Diaz, Noemi V.; Meier, Russ; Trytten, Deborah A.; Yoon Yoon, So; Moore, Janie M.; Ogilvie, Andrea M.; Weichold, Mark. (July 2021, 2021 ASEE Annual Conference)

   null (Ed.)

   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. 

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2. Computational Thinking in the Formation of Engineers (Year 1)

   Mendoza Diaz, N. V. (July 2021, 2021 ASEE Virtual Annual Conference)

   Computational thinking is an important skill in the formation of engineers. Many schools of engineering include a programming course during the first-year. In 2019, NSF funded the “Collaborative Research: Research in Improving Computational Thinking in the Formation of Engineers, a Multi-Institutional Initiative.” The project’s goal is to improve the way computational thinking is taught at the college engineering level via the understanding of the multiple factors that affect computational thinking development. The project’s research questions are: · Research Question 1: How does the integration of computing into the foundational engineering courses affect the formation of engineers? · Research Question 2: In what ways do social identities (e.g. gender, ethnicity, first generation college attending, socioeconomic status), choices (e.g. major, transfer status), and other factors impact the engineering student experience with computational thinking? · Research Question 3: In what ways do computational thinking skills develop over time in engineering students? In order to respond to these questions, the research team developed a Computational Thinking Hybrid Framework, an Engineering Computational Thinking Diagnostic (ECTD) and a Position of Stress Questionnaire. Amid COVID-19, the advances of the project include approximately 2000 participants responding to the diagnostic during the Fall of 2019 and the Fall of 2020. With this participation, two cycles of validation have taken place for the ECTD and results are presented in this poster session. The factors validated in this diagnostic are (1) Abstraction, (2) Algorithmic Thinking and Programming, (3) Data Representation, Organization, and Analysis, (4) Decomposition, and (5) Impact of Computing. The positions of stress have been collected for the Fall of 2020 and preliminary results are also presented in this session. 

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3. Computational Thinking Growth During a First-Year Engineering Course

   https://doi.org/10.1109/FIE44824.2020.9274250  

   Mendoza Diaz, Noemi V.; Meier, Russ; Trytten, Deborah A.; Yoon Yoon, So (October 2020, 2020 IEEE Frontiers in Education Conference (FIE))

   null (Ed.)

   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. 

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4. Board 240: Computational Thinking in the Formation of Engineers: Year 3 Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. https://peer.asee.org/42683

   Mendoza Diaz, N. V. (June 2023, Proceedings ASEE annual conference)

   Over the past three years we have been exploring not only how computational thinking impacts the first-year student experience but also persistence to graduation and enculturation to engineering. Students matriculate to engineering degrees with different academic preparation in mathematics and computing. We began our work by designing a computational thinking diagnostic that can be administered to students as they enter the engineering program in order to determine student's ability to use the principles and practices that are learned by studying computing. We can report that 3584 students were participants during the development of the Engineering Computational Thinking Diagnostic (ECTD) and the last 469 were involved in exploratory and confirmatory analysis. 

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5. Computational Thinking in the Formation of Engineers: Year 3

   https://doi.org/10.18260/1-2--36826  

   Mendoza Diaz, Noemi V; Meier, Russ; Trytten, Deborah Anne; Moore, Janie M; Yoon, So Yoon; Hogan, Harry A (June 2023, American Society of Engineering Education Annual Conference & Exposition)

   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. 

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