Publications

Enculturation into engineering refers to the process by which novices learn and internalize the content, practices, and values of the engineering profession. This study employs an enculturation model incorporating both intrinsic and extrinsic influences. Extrinsic influences stem from institutional frameworks, particularly first-year curricular standards set by ABET. The study aimed to operationalize and test this model through a survey conducted during the Spring 2022 with 626 students at two U.S. minority-serving institutions. The research addressed four questions: 1) How do enculturation dimensions vary across student classifications at each institution? 2) What is the relationship between enculturation and the impact of COVID-19? 3) How do these dimensions vary based on First-Generation Status and Race/Ethnicity at a Hispanic-Serving Institution (HSI)? 4) How does Exploratory Factor Analysis characterize enculturation, and how does it relate to self-efficacy? Findings indicate that students generally increase in enculturation dimensions as they advance in their programs, though differences exist across demographics and institutions. COVID-19 significantly impacted two dimensions, with effects varying by institution. First-Generation and minority students followed similar trends, often demonstrating disadvantage compared to majority peers. Finally, factor analyzes confirmed that enculturation and self-efficacy are distinct constructs. These promising results provide proof that the model of enculturation can potentially help attract and retain students in multiple engineering fields. The results also have wide implications for research and practice, specifically when pertaining to disadvantaged minority students.

Computational thinking is one barrier to enculturating as a professional engineer. We created the Engineering Computational Thinking Diagnostic (ECTD) as an instructional tool that can identify at-risk first-year engineering students. The purpose of this study is to provide construct validity, internal consistency reliability, item characteristics, and criterion validity evidence for this diagnostic. From fall 2020 to fall 2021, 469 students from three institutions in the United States took the diagnostic. The data from 152 students at one institution was used to provide evidence of predictive validity. Exploratory and confirmatory factor analyses resulted in 20 items loading onto one factor in a good model fit range, with the internal consistency reliability coefficient, Cronbach α of 0.86. From item analyses based on classical test theory, the diagnostic items on average tended to be slightly easy but had sufficient discrimination power. The correlation matrix for criterion validity evidence indicated that the diagnostic functions well to differentiate students’ computational thinking ability by prior computer science course experience as well as by first-generation status. Predictive validity evidence from regression analyses revealed the statistically significant effect of students’ diagnostic scores assessed at the beginning of the first semester on predicting their end of semester course grades. The ECTD can have a broad impact because it provides a tool to gauge the entry-level skills of students, enabling early curriculum interventions to help retention and persistence to graduation. We make the case that the ECTD could contribute to the development of a more diverse workforce in engineering.

The curriculum challenges presented to first-year engineering instructors at a Southwestern institution in the United States as well as the need to highlight the process of theory-building in engineering education motivated a team to conduct an umbrella research project related to the enculturation of engineering students. This umbrella project has generated six conference papers and this manuscript constitutes the last of the series focusing on the changes that students experience and perceive over time, once exposed to the first-year engineering curriculum, and how these changes inform a theory of enculturation. The research questions investigated how students perceive their enculturation, once they have progressed in their engineering programs, and how the results constitute a theory of enculturation.

Addressing the interdisciplinary challenges of teaching artificial intelligence in higher education while embracing and comprehending diverse backgrounds, promoting inclusivity and diversity, and reducing socioeconomic barriers are crucial for creating a learning environment that can equip students with the necessary skills and knowledge to navigate the complexities of the field and drive innovation in the rapidly evolving world of artificial intelligence. In order to undertake this challenge, the present work proposes a general introductory course to artificial intelligence in the early semesters of the bachelor’s degree, without any tendencies on the specificity of the problems to solve. Prior to this dissemination, authors have pilot-tested the introductory lesson. This paper presents the findings of this pilot and proposes a new approach to diversifying the pool of students who can benefit from an intro course in AI. We expect the FIE audience to provide us feedback on the best strategy to follow for implementation which is expected to occur during Spring or Fall of 2024.

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

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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.

Computational Thinking in the Formation of Engineers: Year 2

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This poster presentation reports the results during the second year of the “Collaborative Research Project: Research in Improving Computational Thinking in the Formation of Engineers, a Multi-Institutional Initiative.” During this year, the validation of the ECTD, as a mechanism to assess entry level computational thinking skills as one factor, has been completed. The progress also includes 27 interviews done with 3 cohorts of students that shared similar social identities in one institution (e.g. race/ethnicity, gender, first generation/gifted classification, decrease in level of confidence) to explore aspects in the first year experience that might affect the decision to be an engineer. The interviews were done with students that reported different levels of stress given position-of-stress questions added to the ECTD and later asked in the semester (at a different point of stress). These interviews revealed privilege of those with prior computing training or experience. For the next year of this project, the research team expects to expand to mixed-method data collection for all three institutions in the collaborative and to continue gathering longitudinal data. The results are expected to inform current first-year engineering programs and impact curriculum, enculturation of engineers, and increase participation. The research team expects these results will improve the preparation of an inclusive community of diverse engineers.

The Impact of Prior Programming Experience on Computational Thinking in First-Year Engineering Experience.

Abstract

Computational thinking is a critical skillset that must be developed by engineers and other computing professionals. Computational thinking is a skillset that allows people to apply computers and technology to systematically solve problems. The ABET student outcomes and the educational outcomes section of the Taxonomy of Engineering Education acknowledge the importance of this skillset for engineers [1] [2] [3]. Moreover, many introductory engineering courses have a component of programming or computational thinking. Earlier work has shown that computational thinking is important to successfully achieving an engineering identity [4]. First year engineering students matriculate with varying levels of computational thinking. Some arrive adequately prepared for engineering computation because of early academic access to computing courses [5]. Most arrive with only pre-university mathematics and science training. In addition, inequities in course quality, self-confidence, and technology access disadvantage women, African American, and Latinx students [5]. Our multi-institutional team of researchers has been investigating how academic preparation and disparities affect computational thinking skills development, professional enculturation, and educational trajectories of first-year engineering students that are required to take introductory courses in engineering computation. With funding from the National Science Foundation, we are working to answer a broad set of research questions. In this paper, we specifically address this subset: 1. How does the integration of computing into the foundational engineering courses affect the formation of engineers? 2. In what ways do social identities (e.g. gender, ethnicity, first generation, socioeconomic status), choices (e.g. major, transfer status), and other factors impact the engineering student experience with computational thinking? a. In what ways are social identities related to perceived task time, perceived task difficulty, and perceived career confidence? In phase one of our research, we developed an Engineering Computational Thinking Diagnostic (ECTD) that can be used to measure development of computational thinking factors such as the social impact of computing, computational abstraction, data representation, task decomposition, and algorithmic thinking. Pre-validation use of the instrument showed its ability to measure skills growth [6]. We are now in the second phase of our research while the ECTD is formally validated through multi-term large population sampling and factor analysis. In this second phase, we have added a three-question instrument administered at the end of ECTD testing, as well as on submission of critical assignments during the academic term. We call these moments “positions of stress” because they represent times during the term where student career confidence may be changing. The ECTD and position-of-stress instruments were administered at the beginning of the Spring 2021 semester and after the midterm exam. From the population of position-of-stress participants, a stratified sampling technique was used to choose semi-structured interview candidates in three categories of decreasing, same, and increasing career confidence. Stratified subpopulations were identified by confidence level and race, confidence level and gender, confidence level and first-generation status, as well as subpopulations that saw the most drastic changes in confidence. During Summer 2021, semi-structured interviews will be conducted to gain qualitative insight into the computing background of participants, the entry path to engineering, how the participant feels about engineering while progressing through the course, the likelihood of enculturation as an engineer, and social identify factors that may be impacting success. Interviews will be transcribed and coded using an iterative and inductive methodology where a codebook will be developed and iteratively improved to represent the contents of the student interview data. Coding will be done by two researchers working collaboratively using NVivo. To assure reliability, interviews will be coded by individual researchers and compared until the differences between independent codings are minimal. After the differences between coders are minimal, coders will independently code the remaining interviews. Inter-rater reliability will be measured by having the coders evaluate two transcripts and comparing the percentage of the transcript with matching coding. The coded interviews will be used to analyze and explain how computational thinking requirements in engineering differentially impact students with various social identities. We expect to find differences in perceptions related to the privileges experienced by students. For example, we expect that students who have the privilege of prior experience with computing will find the integration of computational thinking into engineering less stress inducing than those without this prior exposure. The data will be represented both in counts of students whose quotes fall into the various codes and by selecting especially informative participant quotes for presentation. We expect that this paper will contribute to the literature by showing how computational thinking may exacerbate existing equity problems in engineering by allowing students with privilege to accrue additional advantages that ease the transition into engineering early in their academic programs. These accrued advantages may diminish the opportunities for success of students with less privilege, who may incorrectly view themselves as having less merit. We expect that this will play out differently in a variety of intersectional social identities, as each individual and subgroup experiences differential privileges. The variety of participants with multiple social identities in our participant pool will provide us with unique opportunities to contribute to understanding the impact of computational thinking on engineering enculturation.

An Engineering Computational Thinking Diagnostic: A Psychometric Analysis

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Computational Thinking in the Formation of Engineers (Year 1)

Abstract

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.