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1. Introductory Engineering Courses With Computational Thinking: The Impact of Educational Privilege and Engineering Major Entry Policy on Student Pathways

   https://doi.org/10.1109/FIE56618.2022.9962501  

   Mendoza Diaz, Noemi V.; Trytten, Deborah A.; Meier, Russ (October 2022, 2022 IEEE Frontiers in Education Conference (FIE))

   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. 

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2. Introductory Engineering Courses With Computational Thinking: The Impact of Educational Privilege and Engineering Major Entry Policy on Student Pathways

   Mendoza Diaz, Noemi V.; Trytten, Deborah A.; Meier, Russ (October 2022, 2022 IEEE Frontiers in Education Conference (FIE))

   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. 

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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. An Engineering Computational Thinking Diagnostic: A Psychometric Analysis

   https://doi.org/10.1109/FIE49875.2021.9637142  

   Diaz, Noemi V.; Trytten, Deborah A.; Meier, Russ; Yoon, So Yoon (October 2021, 2021 IEEE Frontiers in Education Conference (FIE))

   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 

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

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

   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. 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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