Global socioeconomic trends are changing the nature of the American workplace. To address the challenges brought about by these changes, American engineering education must focus on developing students into future professionals, equipped to thrive in the fast-paced, technologically intense, globally competitive workplace of the future. One of the most effective ways to prepare students to face the future is by teaching them to innovate. This thesis presents the "Creative Entrepreneurs Organization: Developing Innovative Products and Businesses" (CEO) concept as a method by which Virginia Tech could help students learn innovation. The CEO concept is a student-involvement program intended to develop students into successful entrepreneurs as they work together in small teams to develop and market intellectual property. This Program is intended to produce revenue for the University by virtue of the successful commercialization of the intellectual properties it generates. Additionally, the CEO Program will allow faculty and students to share in the financial rewards associated with the intellectual properties they generate. The CEO Program concept is presented in light of current trends in the business and academic worlds. Various issues related to its implementation are addressed. The Program is evaluated for its expected value to students, to the University, to the State, and to the Nation. A survey is presented by which the success of the Program can be measured. For the CEO concept to be successfully realized, several challenges must be overcome. First, the University must embrace this somewhat unorthodox Program in which both educational and financial motives play significant roles. Second, there must be a Program Advocate who will be able to effectively communicate the value and feasibility of the Program. Third, fiscal and physical resources must be available to ensure the successful start-up and operation of the CEO Program. Finally, the Program must find ways to nurture creativity in its participants. I conclude that the effort required to implement the CEO Program is outweighed by its potential benefits to students, to the University, to the State of Virginia, and to the Nation. Therefore, I recommend that the Virginia Tech College of Engineering consider the CEO Program for implementation. / Master of Science

<p dir="ltr">Students choose engineering majors in college for multiple personally motivating reasons. However, 40 % of engineering students do not graduate from their initial engineering major, with most attrition occurring in the first two years of the degree program. This trend has personal and societal consequences. Engineering careers can be personally fulfilling and financially rewarding for many individuals. At the same time, engineering jobs provide critical infrastructure, products, and services that society relies on. In addition, these gatekeeping mechanisms exacerbate inequalities and barriers for systemically minoritized individuals (e.g., women, Black, Indigenous, Latina/o/x, students, etc.) and people at the intersection of these identities in STEM disciplines. Higher educational institutions must ensure that students enrolled in STEM degrees are well supported to pursue and achieve their career goals, especially as more students come from increasingly diverse backgrounds and different levels of prior academic preparation. Motivation can help explain why students choose engineering degrees and how they persist, considering the many challenges they face in obtaining their engineering degree. Motivation is essential for learning and predicts academic achievement and engagement. Motivated students learn more, persist longer, use appropriate learning strategies, and are more likely to achieve their learning outcomes. The following chapters investigate how students navigate one institution’s introductory chemical engineering course across multiple semesters.</p><p dir="ltr">In the second chapter, I used the Self-Determination Theory (SDT) to study the effects of the rapid transition to online learning during the COVID-19 pandemic on students’ motivation and well-being in this course. Surveys were administered at the beginning and end of the semester to measure motivation using the Basic Psychological Needs Scale (BPNS) and psychological distress using the Depression Anxiety Stress Scale (DASS-21). Motivation (<i>autonomy</i> and <i>competence</i>) decreased during the semester, based on the results from the paired t-tests. In addition, I predicted students’ final course grades using multiple linear regression to examine the effect of motivation on students’ final course grades. Women with positive changes in competence during the semester were predicted to perform worse than their peers, indicating a negative relationship between motivation and performance for women in the sample population. Finally, the path analysis model results showed that higher psychological distress reduced students’ motivation, higher autonomy predicted higher final grades and higher competence predicted lower final grades. Furthermore, the open-ended survey questions asked the research participants to reflect on their learning experiences. These qualitative responses contextualized the findings from the statistical results.</p><p dir="ltr">In Chapter 3, I examined students’ self-regulated learning beliefs in an introductory chemical engineering course across three semesters. Self-regulated students steadily monitor and assess their learning and performance to achieve their desired academic outcomes. I conceptualized motivation using the Expectancy-Value-Cost Theory to explain why students engage in academic tasks. Using confirmatory factor analysis (CFA), I examined the relationship between the theoretical constructs. Then, I used hierarchical multiple linear regression to predict students’ final course grades. The surveys were administered at the beginning and end of the semester. Motivation was measured using the Motivated Strategies for Learning (MSLQ), while cost was measured using the Flake et al. (2015) cost survey. All motivation and cost constructs were stable each semester, except for <i>self-efficacy</i> (Fall 2022) and <i>loss of valued alternatives</i> (Fall 2023). Also, gender identity, self-efficacy, and emotional costs were significant predictors of final grades for students in this class.</p><p dir="ltr">Then, in Chapter 4, I interviewed five students who had completed the course and conducted a qualitative study of their experiences in this course, informed by the Social Cognitive Career Theory (SCCT). The results demonstrate how positive social interactions increased students’ self-efficacy judgments, while unfavorable interactions isolated students and reduced their self-efficacy beliefs. Second, the students described strategies to achieve their desired academic performance and persist while facing obstacles. These responses provided evidence to support the proposed conceptual framework. Our findings show how social interactions and self-efficacy judgments influence students’ performance and persistence in an introductory engineering course context.</p><p dir="ltr">Together, these findings contribute to the literature on motivation theories by elucidating how motivation affects students’ performance in an introductory engineering class. It will also contribute to efforts to implement educational reform to retain and support students in engineering programs. To improve students' outcomes and contribute to the research and efforts to support and retain engineering students in the majors.</p>

Engineering Education is a developing field, with new research and ideas constantly emerging and contributing to the ever-evolving nature of this discipline. Textual data (such as publications, open-ended questions on student assignments, and interview transcripts) form an important means of dialogue between the various stakeholders of the engineering community. Analysis of textual data demands consumption of a lot of time and resources. As a result, researchers end up spending a lot of time and effort in analyzing such text repositories. While there is a lot to be gained through in-depth research analysis of text data, some educators or administrators could benefit from an automated system which could reveal trends and present broader overviews for given datasets in more time and resource efficient ways. Analyzing datasets using Natural Language Processing is one solution to this problem. The purpose of my doctoral research was two-pronged: first, to describe the current state of use of Natural Language Processing as it applies to the broader field of Education, and second, to demonstrate the use of Natural Language Processing techniques for two Engineering Education specific contexts of instruction and research respectively. Specifically, my research includes three manuscripts: (1) systematic review of existing publications on the use of Natural Language Processing in education research, (2) automated classification system for open-ended student responses to gauge metacognition levels in engineering classrooms, and (3) using insights from Natural Language Processing techniques to facilitate exploratory analysis of a large interview dataset led by a novice researcher. A common theme across the three tasks was to explore the use of Natural Language Processing techniques to enable the computer to extract meaningful information from textual data for Engineering Education related contexts. Results from my first manuscript suggested that researchers in the broader fields of Education used Natural Language Processing for a wide range of tasks, primarily serving to automate instruction in terms of creating content for examinations, automated grading or intelligent tutoring purposes. In manuscripts two and three I implemented some of the Natural Language Processing techniques such as Part-of-Speech tagging and tf-idf (text frequency-inverse document frequency) that were found (through my systematic review) to be used by researchers, to (a) develop an automated classification system for student responses to gauge their metacognitive levels and (b) conduct an exploratory novice led analysis of excerpts from interviews of students on career preparedness, respectively. Overall results of my research studies indicate that although the use of Natural Language Processing techniques in Engineering Education is not widespread, although such research endeavors could facilitate research and practice in our field. Particularly, this type of approach to textual data could be of use to practitioners in large engineering classrooms who are unable to devote large amounts of time to data analysis but would benefit from algorithmic systems that could quickly present a summary based on information processed from available text data. / Ph. D. / Textual data (such as publications, open-ended questions on student assignments, and interview transcripts) form an important means of dialogue between the various stakeholders of the engineering community. However, analyzing these datasets can be time consuming as well as resource-intensive. Natural Language Processing techniques exploit the machine’s ability to process and handle data in time-efficient ways. In my doctoral research I demonstrate how Natural Language Processing techniques can be used in the classrooms and in education research. Specifically, I began my research by systematically reviewing current studies describing the use of Natural Language Processing for education related contexts. I then used this understanding to inform use of Natural Language Processing techniques to two Engineering Education specific contexts: one in the classroom to automatically classify students’ responses to open-ended questions to understand the metacognitive levels, and the second context of informing analysis of a large dataset comprising excerpts from interview transcripts of engineering students describing career preparedness.

The UTeach Observation Protocol (UTOP) is designed to capture what occurs in a classroom. The UTOP was developed for use in the nationally recognized UTeach program (uteach.utexas.edu) and has been validated nationally in the Gates Foundation Measures of Effective Teaching. (http://www.metproject.org/downloads/Preliminary_Findings-Research_Paper.pdf) Currently the UTOP has been used in both science and math classrooms and is being developed for use in English language arts and social studies classrooms as well. This report serves to begin the modification of the UTOP for use in an engineering classroom to evaluate engineering specific content. The UTOP has been described as a lens for reflection on teaching practices and the goal of this report is to help focus that lens more clearly on the engineering classroom. This tool was created for utilization in both educator and administrator roles. Teachers can use the UTOP to self-assess their own teaching practices as well as in observing other teachers and identify classroom best practices. Administrators and other classroom visitors can use the UTOP to understand and evaluate what occurs in a classroom for a multitude of outcomes. The methodology chosen in this report to create the engineering specific examples used real lessons that have been implemented in engineering classrooms and vetted in actual practice. Using both initial lessons from the teachers and their feedback along with language taken from the Next Generation Science Standard Framework and the UTeachEngineering Engineering Design Protocol, the examples were developed to show how to score each indicator on a scale of 1 to 5, with 1 being the lowest and 5 being the highest score, in a secondary engineering classroom. The next steps recommended for this work are to pilot the examples created in this report and test the usefulness of the examples created. This can be accomplished by field-testing it in UTOP training with teachers and modifying the information based on the feedback that they provide. The work described in this paper was made possible by a grant from the National Science Foundation (Award DUE-0831811). / text

<p>There is a large variation in the quantity and quality of ethics that U.S. engineering students learn. Why is there so much room for improving the state of engineering ethics education in the United States? Recognizing the interplay between individual agency, structural factors, and historical contingency, this dissertation is a three-part approach to answering that question – I present three distinct, mutually informative threads for studying engineering ethics education from different angles. The first thread is an historical approach. The second thread is an empirical study of the mental models that faculty members have regarding engineering ethics education. The third thread applies theoretical constructs from political science and economics to analyze structural factors impinging on engineering ethics education.</p><p><br></p> <p>From the studies, first we see that trailblazers of engineering ethics developed the new knowledge required of this emerging field through interpersonal relationships; they leveraged existing organizations and built new institutional mechanisms for sharing knowledge and creating a community of scholars and an engineering ethics curriculum; they utilized resources from supportive colleagues and administrators to corporate, governmental, and nongovernmental funding that legitimated their work. Their efforts ultimately created pedagogical materials, prevalent ideas, publication outlets, meetings, and foundations that not only contributed to the current state of U.S. engineering ethics education but also the launching point for future generations to build upon and continue developing that state. Second, mapping the mental models of engineering ethics education among engineering faculty members provided a typology for analyzing the state of engineering ethics education and places where one can expect to find variation, deepening our understanding of the state of engineering ethics education. Third, outlining a theory of the political economy of engineering education highlighted factors that could be influencing curricular and pedagogical decisions in engineering departments. Furthermore, I supplemented the outlined theoretical phenomena with data from the mental models interviews in order to provide a proof of concept and relevant grounding for the phenomena.</p><p><br></p> <p>In sum, faculty members make decisions based on their mental models. Structural factors shape the broader environment and institutions in which those faculty members operate. Those structures and institutions change over time, leading to the current state of engineering ethics education. Having all three pieces has provided a more complete understanding of the state of U.S. engineering ethics education.</p><p><br></p> <p>Ultimately, my dissertation accomplishes multiple goals. First, I have provided additional evidence for understanding and explaining the qualitative and quantitative discrepancies of engineering ethics coverage in U.S. undergraduate engineering education at multiple levels of analysis. Second, I have amassed evidence that can inform future research efforts. Third, I have demonstrated the use of certain theories and methods infrequently employed in engineering education research. Finally, I have outlined potential new avenues for interdisciplinary research, especially at the nexus of political economy, education, engineering, and society. </p>

This dissertation explores how students’ beliefs and attitudes interact with their identities as physics people, motivated by calls to increase participation in science, technology, engineering, and mathematics (STEM) careers. This work combines several theoretical frameworks, including Identity theory, Future Time Perspective theory, and other personality traits to investigate associations between these factors. An enriched understanding of how these attitudinal factors are associated with each other extends prior models of identity and link theoretical frameworks used in psychological and educational research. The research uses a series of quantitative and qualitative methodologies, including linear and logistic regression analysis, thematic interview analysis, and an innovative analytic technique adapted for use with student educational data for the first time: topological data analysis via the Mapper algorithm. Engineering students were surveyed in their introductory engineering courses. Several factors are found to be associated with physics identity, including student interest in particular engineering majors. The distributions of student scores on these affective constructs are simultaneously represented in a map of beliefs, from which the existence of a large “normative group” of students (according to their beliefs) is identified, defined by the data as a large concentration of similarly minded students. Significant differences exist in the demographic representation of this normative group compared to other students, which has implications for recruitment efforts that seek to increase diversity in STEM fields. Select students from both the normative group and outside the normative group were selected for subsequent interviews investigating their associations between physics and engineering, and how their physics identities evolve during their engineering careers. Further analyses suggest a more complex model of physics and engineering identity which is not necessarily uniform for all engineering students, including discipline-specific differences that should be further investigated. Further, the use of physics identity as a model to describe engineering student choices may be limited in applicability to early college. Interview analysis shows that physics recognition beliefs become contextualized in engineering as students begin to view physics as an increasingly distinct domain from engineering.

abstract: Engineering education can provide students with the tools to address complex, multidisciplinary grand challenge problems in sustainable and global contexts. However, engineering education faces several challenges, including low diversity percentages, high attrition rates, and the need to better engage and prepare students for the role of a modern engineer. These challenges can be addressed by integrating sustainability grand challenges into engineering curriculum. Two main strategies have emerged for integrating sustainability grand challenges. In the stand-alone course method, engineering programs establish one or two distinct courses that address sustainability grand challenges in depth. In the module method, engineering programs integrate sustainability grand challenges throughout existing courses. Neither method has been assessed in the literature. This thesis aimed to develop sustainability modules, to create methods for evaluating the modules’ effectiveness on student cognitive and affective outcomes, to create methods for evaluating students’ cumulative sustainability knowledge, and to evaluate the stand-alone course method to integrate sustainability grand challenges into engineering curricula via active and experiential learning. The Sustainable Metrics Module for teaching sustainability concepts and engaging and motivating diverse sets of students revealed that the activity portion of the module had the greatest impact on learning outcome retention. The Game Design Module addressed methods for assessing student mastery of course content with student-developed games indicated that using board game design improved student performance and increased student satisfaction. Evaluation of senior design capstone projects via novel comprehensive rubric to assess sustainability learned over students’ curriculum revealed that students’ performance is primarily driven by their instructor’s expectations. The rubric provided a universal tool for assessing students’ sustainability knowledge and could also be applied to sustainability-focused projects. With this in mind, engineering educators should pursue modules that connect sustainability grand challenges to engineering concepts, because student performance improves and students report higher satisfaction. Instructors should utilize pedagogies that engage diverse students and impact concept retention, such as active and experiential learning. When evaluating the impact of sustainability in the curriculum, innovative assessment methods should be employed to understand student mastery and application of course concepts and the impacts that topics and experiences have on student satisfaction. / Dissertation/Thesis / Doctoral Dissertation Engineering 2015

<div>This research addresses the need to advance systems engineering education, by assessing current undergraduate systems engineering programs in the US relative to the needs of the industry. </div><div><br></div><div>We extracted over 300 expressions relevant to the systems engineer’s duties from six sources. We chose sources that address the variety in how people define “systems engineering”, the evolving nature of the field, its practical aspect and the lessons learned through experience. We used these expressions to write 35 needed learning outcomes that should be taught to systems engineering students. The outcomes fall under six broad categories relating to requirements management, solution selection and implementation, system architecture and modeling, system performance evaluation, V&V activities and project management. We then looked at what existing undergraduate systems engineering programs are teaching and extracted each program’s current learning outcomes. We compared each program’s current outcomes to the industry-based needed outcomes to determine whether students are being taught what they need to know. </div><div><br></div><div>We learned that the duties of systems engineers are not uniquely defined and prioritized by the six sources, and that academic programs do not all teach the same outcomes. We found that all</div><div>current undergraduate systems engineering programs in the US are preparing students to meet at least some of the needs of the industry, such as to “Identify stakeholder needs”, “Develop highlevel system architecture” and “Estimate cost”, but that most programs do not teach students how to “Select optimal concept” or how to “Analyze system resilience”. </div><div><br></div><div>This work motivates the need to investigate potential gaps in systems engineering education and to determine how well we are preparing students to meet the needs of the industry.</div>

Spatial ability represents our ability to mentally arrange, rotate, and explore objects in multiple dimensions. This ability has been found to be important for engineers and engineering students. Past research has shown that many interventions can be created to boost an individual’s spatial ability. In fact, past research has indicated that engineering students significantly increase in spatial ability without an intervention while they are enrolled in certain engineering courses. Some researchers have claimed that the spatial ability boosts are permanent after an intervention. However, most researchers do not check the validity of that claim with continued assessment after more than a week past the end of an intervention. Additionally, if engineering education researchers are trying to measure the impact of their separate spatial ability intervention while the participating engineering students are actively enrolled in engineering courses, a confounding variable is introduced as the courses can impact students’ spatial ability. To resolve this, the work presented in this paper reflects research on engineering students’ spatial ability maintenance during the winter break between semesters. It was found that newer students exhibit spatial ability improvement during the break, while older students maintain their spatial ability at the same level. A deeper statistical analysis revealed that there are other factors that play a role in spatial ability changes over the break that are more significant than how far students had progressed in their studies. Those factors include with academic performance, the sex of the students, playing music during the break, and prior life experiences.

All engineering careers require some level of programming proficiency. However, beginning programming classes are challenging for many students. Difficulties have been well-documented and contribute to high drop-out rates which prevent students from pursuing engineering. While many approaches have been tried to improve the performance of students and reduce the dropout rate, continued work is needed. This research seeks to re-examine what items are critical for programming education and how those might inform what is taught in introductory programming classes (CS1). Following trends coming from accreditation and academic boards on the importance of professional skills, we desire to rank knowledge and professional skill areas in one list. While programming curricula focus almost exclusively on knowledge areas, integrating critical professional skill areas could provide students with a better high-level understanding of what engineering encompasses. Enhancing the current knowledge centric syllabi with critical professional skills should allow students to have better visibility into what an engineering job might be like at the earliest classes in the engineering degree. To define our list of important professional skills, we use a two-group, three-round Delphi survey to build consensus ranked lists of knowledge and professional skill areas from industry and academic experts. Performing a gap analysis between the expert groups shows that industry experts focus more on professional skills then their academic counterparts. We use this resulting list to recommend ways to further integrate professional skills into engineering programming curriculum.