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1. 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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2. 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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3. 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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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. Understanding the ‘us all’ in Engineering 4 Us All through the Experiences of High School Teachers

   Berhane, B. ; Dalal, M. ; Klein-Gardner, S. ; Carberry, A. ; Reid, K. ; Beauchamp, C. ; Kouo, J. ; Pines, D. ( January 2021 , 3rd annual CoNECD – The Collaborative Network for Engineering and Computing Diversity Conference)

   null (Ed.)

   As our nation’s need for engineering professionals grows, a sharp rise in P-12 engineering education programs and related research has taken place (Brophy, Klein, Portsmore, & Rogers, 2008; Purzer, Strobel, & Cardella, 2014). The associated research has focused primarily on students’ perceptions and motivations, teachers’ beliefs and knowledge, and curricula and program success. The existing research has expanded our understanding of new K-12 engineering curriculum development and teacher professional development efforts, but empirical data remain scarce on how racial and ethnic diversity of student population influences teaching methods, course content, and overall teachers’ experiences. In particular, Hynes et al. (2017) note in their systematic review of P-12 research that little attention has been paid to teachers’ experiences with respect to racially and ethnically diverse engineering classrooms. The growing attention and resources being committed to diversity and inclusion issues (Lichtenstein, Chen, Smith, & Maldonado, 2014; McKenna, Dalal, Anderson, & Ta, 2018; NRC, 2009) underscore the importance of understanding teachers’ experiences with complementary research-based recommendations for how to implement engineering curricula in racially diverse schools to engage all students. Our work examines the experiences of three high school teachers as they teach an introductory engineering course in geographically and distinctly different racially diverse schools across the nation. The study is situated in the context of a new high school level engineering education initiative called Engineering for Us All (E4USA). The National Science Foundation (NSF) funded initiative was launched in 2018 as a partnership among five universities across the nation to ‘demystify’ engineering for high school students and teachers. The program aims to create an all-inclusive high school level engineering course(s), a professional development platform, and a learning community to support student pathways to higher education institutions. An introductory engineering course was developed and professional development was provided to nine high school teachers to instruct and assess engineering learning during the first year of the project. This study investigates participating teachers’ implementation of the course in high schools across the nation to understand the extent to which their experiences vary as a function of student demographic (race, ethnicity, socioeconomic status) and resource level of the school itself. Analysis of these experiences was undertaken using a collective case-study approach (Creswell, 2013) involving in-depth analysis of a limited number of cases “to focus on fewer "subjects," but more "variables" within each subject” (Campbell & Ahrens, 1998, p. 541). This study will document distinct experiences of high school teachers as they teach the E4USA curriculum. Participants were purposively sampled for the cases in order to gather an information-rich data set (Creswell, 2013). The study focuses on three of the nine teachers participating in the first cohort to implement the E4USA curriculum. Teachers were purposefully selected because of the demographic makeup of their students. The participating teachers teach in Arizona, Maryland and Tennessee with predominantly Hispanic, African-American, and Caucasian student bodies, respectively. To better understand similarities and differences among teaching experiences of these teachers, a rich data set is collected consisting of: 1) semi-structured interviews with teachers at multiple stages during the academic year, 2) reflective journal entries shared by the teachers, and 3) multiple observations of classrooms. The interview data will be analyzed with an inductive approach outlined by Miles, Huberman, and Saldaña (2014). All teachers’ interview transcripts will be coded together to identify common themes across participants. Participants’ reflections will be analyzed similarly, seeking to characterize their experiences. Observation notes will be used to triangulate the findings. Descriptions for each case will be written emphasizing the aspects that relate to the identified themes. Finally, we will look for commonalities and differences across cases. The results section will describe the cases at the individual participant level followed by a cross-case analysis. This study takes into consideration how high school teachers’ experiences could be an important tool to gain insight into engineering education problems at the P-12 level. Each case will provide insights into how student body diversity impacts teachers’ pedagogy and experiences. The cases illustrate “multiple truths” (Arghode, 2012) with regard to high school level engineering teaching and embody diversity from the perspective of high school teachers. We will highlight themes across cases in the context of frameworks that represent teacher experience conceptualizing race, ethnicity, and diversity of students. We will also present salient features from each case that connect to potential recommendations for advancing P-12 engineering education efforts. These findings will impact how diversity support is practiced at the high school level and will demonstrate specific novel curricular and pedagogical approaches in engineering education to advance P-12 mentoring efforts. 

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