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<p>  </p> <p>First-Year Engineering (FYE) programs are a critical part of engineering education, yet they are quite complex settings. Given the importance and complexity of FYE programs, research to better understand student learning and inform design and assessment in FYE programs is imperative. Therefore, this dissertation showcases various uses of data analytics and educational theory to support decision-making when designing and assessing FYE programs. Three case studies shape this dissertation work. Each study encompasses a variety of educational data sources, analytical methods, and decision-making tools to produce valuable findings for FYE classrooms. In addition, this dissertation also discusses the potential for incorporating data analytics into FYE programs. A more detailed description of the research methods, a summary of findings, and a list of resulting publications for each case study follows.</p> <p>The first case study investigated the relationship between two related Computational Thinking (CT) practices, data practices and computational problem-solving practices, in acquiring other CT competencies in a large FYE course setting. This study explored the following research questions: (1) What are the different student profiles that characterize their foundational CT practices at the beginning of the semester? and (2) Within these profiles, what are the progressions that students follow in the acquisition of advanced CT practices? To answer these questions, N-TARP Clustering, a novel machine learning algorithm, and sound statistical tools were used to analyze assessment data from the course at the learning objective level. Such a hybrid approach was needed due to the high-dimensionality and homogeneity characteristics of the assessment. It was found that early mastery of troubleshooting and debugging is linked to the successful acquisition of more complex CT competencies. This research was published in an article in the journal <em>IEEE Access</em>.</p> <p>The second case study examined self-regulation components associated with students' successful acquisition of CT skills using students' reflections and assessment data. This research was grounded in three subprocesses of the Self-Regulated Learning (SRL) theory: strategic planning, access to feedback, and self-evaluation. This study responded to the following research question: What is the relationship between SRL subprocesses: access to feedback, self-evaluation, strategic planning, and the acquisition of CT skills in an FYE course? Results from a structural equation model, which reflects the complexity and multidimensionality of the analysis, provided evidence of the relevance of the three subprocesses in the acquisition of CT skills and highlighted the importance of self-assessment as key to success in the acquisition of programming skills. Furthermore, self-assessment was found to effectively represent the task strategy and access to feedback from the students. This analysis led to the understanding that even though the three SRL subprocesses are relevant for the student's success, self-evaluation serves as a catalyst between strategic planning and access to feedback. A resulting article from this case study will be submitted to the <em>International Journal of Engineering Education</em> in the future.</p> <p>Lastly, the third study aimed to predict the students' learning outcomes using data from the Learning Management System (LMS) in an FYE course. The following research questions were explored in this case study: (1) What type of LMS objects contain information to explain students' grades in a FYE course? (2) Is the inclusion of a human operator during the data transformation process significant to the analysis of learning outcomes? Two different sections of a large FYE course were used, one serving as a training data set and the other one as a testing data set. Two logistic regression models were trained. The first model corresponded to a common approach for building a predictive model, using the data from the LMS directly. The second model considered the specifics of the course by transforming the data from aggregate user interaction to more granular categories related to the content of the class. A comparison was made between the predictive measures, e.g., precision, accuracy, recall, and F1 score for both models. The findings from the transformed data set indicate that students' engagement with the career exploration curriculum was the strongest predictor of students' final grades in the course. This is a fascinating finding because the amount of weight the career assignments contributed to the overall course grade was relatively low. This study will be presented at the 2022 American Society of Engineering Education (ASEE) national conference in Minneapolis, Minnesota.</p>

<p>Problem framing is an essential stage in engineering design mainly because it is crucial in developing solutions to design problems. Engineers’ ability to frame a problem is naturally attributed to their reasoning abilities and expertise. Traditionally, our understanding of the type of reasoning is originated from cognitive sciences, sociology, and psychological theories of reasoning. Design reasoning models developed from these disciplines contributed significantly to understanding design reasoning. However, a different standpoint for understanding specialized form of knowledge and reasoning that are unique to engineering practices is needed.</p> <p>An important contribution of this dissertation to the body of research is its use of a new theoretical model, Design Reasoning Quadrants, developed to help organize types of design reasoning at the intersection of two axes, the disciplinary-multidisciplinary reasoning axis and theoretical-practical reasoning axis. Further, this dissertation uses the Design Reasoning Quadrants framework to understand first-year engineering students' reasoning while framing design problems. Prior research stated that it is necessary to elicit the forms of reasoning beginner students have while dealing with design problems, to improve problem-solving abilities. Therefore, this dissertation addresses the need to understand first-year engineering students' reasoning, while engaging in problem framing using four design reasoning quadrants: experiential observations, first principles, trade-offs, and complex abstractions.</p> <p>This dissertation examined changes in first-year engineering students’ design reasoning during problem framing across two different design projects students explored within a semester in an engineering course. The main data sources were answers to a questionnaire students completed in the first and final design project as the first-in-lecture activity for problem framing. Students answered each questionnaire individually. The analysis took place in two stages. </p> <p>First, a deductive analysis was conducted to identify types of reasoning in students’ formulated questions to understand a problem. Using a multinomial logit model and descriptive statistics, differences in the theoretical-practical and disciplinary-multidisciplinary reasoning through the time were identified. Second, students’ answers to the design reasoning quadrants’ questions were analyzed deductively and inductively. This analysis aimed to identify students’ design reasoning patterns when elicited in one of the four design reasoning quadrants.</p> <p>The results of the deductive analysis indicated that regardless of the design project, student reasoning in terms of the theoretical-practical reasoning is not significantly different between the two time points. However, students’ reasoning was more heavily disciplinary-focused in the second project and more multidisciplinary in the first design project. The results of the inductive analysis helped further explain this result. This analysis revealed that students were more familiar with the context and disciplinary concepts for the first rather than for the second design project.</p> <p>The results of this dissertation and framework can help researchers further understand how students reason from the perspective of the nature of engineering. In addition, understanding the type of reasoning students use while framing a problem will allow educators to understand the reasoning beginner students employ while framing a problem and to develop better learning experiences to enhance problem-solving skills.</p>

While first-year engineering (FYE) programs have grown dramatically over the last 30 years, they take a variety of different structures. However, few if any, researchers and FYE program developers has considered how program structure, and specifically matriculation, impacts retention – an issue that continues to be of concern as we seek to grown the national engineering workforce. Low retention rates combined with lack of diversity becomes even more acute when considering the field of Electrical and Computer Engineering (ECE) which ranks as one of the least diverse engineering disciplines. One factor that has been shown to support retention is self-efficacy or individuals' beliefs in their ability to succeed. Therefore, to help address the retention issues in ECE, this dissertation explores the programmatic influence of first-year engineering matriculation structures on self-efficacy development in electrical and computer engineering students. In particular, it compares declared engineering (DE) programs, which admit students to a specific engineering field, to general engineering (GE) programs, in which students are admitted to engineering but do not select a specific engineering field until after their first year. Using qualitative and quantitative methodologies, this dissertation presents three manuscripts: 1) a quantitative secondary analysis comparing competency beliefs in a GE program and a quasi- DE first-year engineering program for ECE students; 2) a qualitative secondary analysis of self-efficacy development in a DE first-year program; and 3) a qualitative analysis exploring similarities and differences in self-efficacy development in EE students at two universities, one with a DE program and one with a GE program. The exploratory studies resulted in findings that demonstrate strong similarities in self-efficacy development in students from the DE and GE programs. Those differences that did emerge are largely attributed to how self-efficacy is discussed by students: 1) self-efficacy is developed differently between the two programs because the tasks associated with each program are different; 2) GE students discuss self-efficacy more broadly regarding engineering in general, focusing on domains like professional skills; 3) DE students discuss self-efficacy development more narrowly, specifically related to being an electrical or computer engineer. Additionally, the findings from study 2 suggest that pedagogical structures may be more important regarding self-efficacy development than matriculation structures. These results broaden our understanding of how FYE programs impact self-efficacy development within the context of a specific major, but still lend themselves to further exploration regarding factors most related to persistence and the experiences of underrepresented minorities in engineering. / Doctor of Philosophy / While first-year engineering (FYE) programs have grown dramatically over the last 30 years, they take a variety of different structures. However, few if any, researchers and FYE program developers have considered how program structure impacts persistence – an issue that continues to be of concern as we seek to grown the national engineering workforce. Low retention rates combined with lack of diversity in the field becomes even more intense when considering the field of Electrical and Computer Engineering (ECE) which ranks as one of the least diverse engineering disciplines. One factor that has been shown to support retention is self-efficacy or individuals' beliefs in their ability to succeed. Therefore, to help address the retention issues in ECE, this dissertation explores the programmatic influence of first-year engineering matriculation structures on self-efficacy development in electrical and computer engineering students. In particular, it compares declared engineering (DE) programs, which admit students to a specific engineering field, to general engineering (GE) programs, in which students are admitted to engineering but do not select a specific engineering field until after their first year. The dissertation includes three studies: 1) a quantitative comparison of expectancy (similar to self-efficacy) beliefs in a GE program and a quasi- DE first-year engineering program for ECE students; 2) a qualitative study of self-efficacy development in a DE first-year program using interviews with students; and 3) a qualitative study of similarities and differences in self-efficacy development in EE students at two universities, one with a DE program and one with a GE program. The studies demonstrated similarities in self-efficacy development in students from the DE and GE programs, with differences largely attributed to how students described self-efficacy, as follows: 1) self-efficacy is developed differently between the two programs because the tasks associated with each program are different; 2) GE students discuss self-efficacy more broadly regarding engineering in general, focusing on issues like professional development skills; 3) DE students discuss self-efficacy development more narrowly, specifically related to being an electrical or computer engineer. Additionally, the findings from study 2 suggest that approaches to teaching may be more important for self-efficacy development than matriculation structures. These results broaden our understanding of how FYE programs impact self-efficacy development within the context of a specific major, but also point to the need for more research on factors most related to persistence and the experiences of underrepresented minorities in engineering.

This dissertation illuminates patterns across disparate university data sets to identify the insights that may be gained through the analysis of large amounts of disconnected student data on first-year engineering (FYE) students and to understand how FYE instructors use data to inform their teaching practices. Grounded by the Academic Plan Model, which highlights student characteristics as an important consideration in curriculum development, the study brings together seemingly distinct pieces of information related to students' learning, engagement with class resources, and motivation so that faculty may better understand the characteristics and activities of students enrolled in their classes. In the dissertation's first manuscript, I analyzed learning management system (LMS) timestamp log-files from 876 students enrolled in the FYE course during Fall 2013. Following a series of quantitative analyses, I discovered that students who use the LMS more frequently are more likely to have higher grades within the course. This finding suggests that LMS usage might be a way to understand how students interact with course materials outside of traditional class time. Additionally, I found differential relationships between LMS usage and course performance across different instructors as well as a relationship between timing of LMS use and students' course performance. For the second manuscript, I connected three distinct data sets: FYE student's LMS data, student record data, and FYE program survey data that captured students' motivation and identity as engineers at two time points. Structural equation modeling results indicate that SAT Math was the largest predictor of success in the FYE course, and that students' beginning of semester engineering expectancy was the only significant survey construct to predict final course grade. Finally, for the third manuscript I conducted interviews with eight FYE instructors on how they use student data to inform their teaching practices. Ten themes emerged which describe the limited explicit use of formal data, but many instructors use data on an informal basis to understand their students. Findings also point to specific, existing data that the university already collects that could be provided to instructors on an aggregate, class-level basis to help them better understand their students. / Ph. D.

Many universities across the United States have been experiencing an increased demand for computer science majors. Adjusting curriculum to meet increased demand runs the risk of damaging ongoing efforts to broaden participation in computer science. To manage growth, and increase the representation of women and underrepresented minorities in the field, we must first understand current patterns for participation, and factors that may impact access and persistence. Universities with common first-year engineering programs present an opportunity for addressing some of the barriers that have traditionally limited access to computer science to certain groups. In particular, common first-year programs could provide early positive experiences with computer programming which encourage more students to consider computer science as a viable major. To better understand how a common first-year engineering program may impact matriculation and persistence in computer science, I conducted studies to identify high-level patterns of participation in computer science, as well as how students experience programming instruction in an introductory engineering course. All studies share the same context: a large public research institution with a common first-year engineering program. Results indicate that women are leaving computer science at all points of the curriculum, contributing to a reduced representation of women earning CS degrees. In contrast, URM and first-generation students have higher representation at graduation than when declaring major interest before the start of their first year. / Doctor of Philosophy / Many universities across the United States have been experiencing an increased demand for computer science majors. Adjusting curriculum to meet demand runs the risk of damaging efforts to increase the diversity of the computer science workforce. To manage growth and increase the representation of women and underrepresented minorities (students who are not white or East Asian) in the field, we must first understand who currently studies computer science, and factors that lead to their success in the major. Universities with general first-year engineering programs present an opportunity for addressing some of the barriers that have traditionally discouraged women and underrepresented minorities from pursuing computer science. In particular, these programs could provide early positive experiences with computer programming which encourage more students to consider computer science as a possible major. To better understand how experiences during students’ first-year transition to college may impact decisions to major in computer science, I conducted studies to explore who enters computer science, and how they succeed in the major, as well as how students experience programming instruction in an introductory engineering course. All studies share the same context: a large public research institution with a general first-year engineering program. Results indicate that women are leaving computer science at all points of the curriculum, contributing to a reduced representation of women earning CS degrees. In contrast, underrepresented minority students and students with parents who did not receive a college degree, make up a higher percentage in the group graduating with a CS degree than in the group who declare CS as their first major.

<p dir="ltr"><a href="" target="_blank">Improving student motivation and changing students from a negative motivational mindset to a positive one can be a viable way to ensure that students stay in their programs and obtain academic success. While educators and administrators are interested in improving motivation, they may not have the full body of knowledge about motivational theories and make uninformed classroom interventions and departmental policies. Using theory to understand student motivations grounds the research in specific constructs that allow educators and policymakers to easily interpret the results and make better-informed decisions regarding classroom activities and academic policies. Tying motivational mindsets to effective classroom behaviors and learning outcomes can help educators determine what motivational orientations are effective within the classroom, and which may need to be altered.</a></p><p dir="ltr">The work that I have done as part of this dissertation helps to advance the use of motivational theory within the field of engineering education and provides useful insight into the motivational mindsets of first-year engineering students. I conducted a latent profile analysis using data from engineering undergraduate students, combining constructs from two established motivational theories to develop motivational profiles. Using two theories, achievement goal theory and expectancy-value theory, allows me to look at the students’ motivational mindsets based on their expectations for success (Expectancy Beliefs), the perceived value of doing well in the course (Task Value Beliefs), their desire to develop their skills (Mastery Orientation), their desire to look well in front of their peers (Performance Approach Orientation), and their desire to not look bad in relation to others (Performance Avoidance Orientation). These five constructs were used to develop profiles, which were then correlated with classroom behaviors and academic performance to determine which motivational profiles were more effective. Correlational analysis was conducted using either ANOVA or Kruskal-Wallis tests, depending on the normality of the data.</p><p dir="ltr">The results of the latent profile analysis yielded five distinct profiles of motivation: <i>Moderate-Low All</i>, <i>Moderate-Low Performance/Moderate-High Intrinsic</i>, <i>Moderate All</i>, <i>High Performance/Moderate Intrinsic</i>, <i>and High All</i>. The <i>Moderate-Low All </i>profile consisted of students who reported lower measures of all motivation constructs than their peers. The <i>Moderate-Low Performance/Moderate-High Intrinsic</i> profile consisted of students who reported average responses related to Expectancy, Task-Value, and Mastery beliefs but scored lower in the Performance Approach and Performance Avoidance beliefs. The <i>Moderate All</i> profile was comprised of students who scored on average along all motivational constructs. The <i>High Performance/Moderate Intrinsic</i> profile contained students who reported average responses to Expectancy, Task-Value, and Mastery beliefs but scored higher in the Performance Approach and Performance Avoidance beliefs. The <i>High All</i> profile was comprised of students who scored higher than the average for all motivational constructs.</p><p dir="ltr">Students were asked to reflect on their use of specific classroom behaviors that were categorized based on the Interactive-Constructive-Active-Passive framework of educational activities. Correlational analysis showed that <i>Moderate-Low All</i> students reported using Passive, Constructive, and Interactive behaviors at a lower rate than their peers, especially <i>High All </i>students. Correlational analysis of academic performance measures also found that there were non-significant differences between profiles related to exam scores, but there were significant differences found in the final grades. <i>Moderate-Low All</i> students had lower final grades than the <i>Moderate-Low Performance/Moderate-High Intrinsic</i>, <i>Moderate All</i>, and <i>High All</i> groups.</p><p dir="ltr">These findings suggest that students in the <i>Moderate-Low All </i>profile are not doing worse in the class because of their exams, but due to not performing the other activities in the class. These activities include large group projects (Interactive tasks) and homework assignments (Constructive tasks). Considering the context of the study and the course that these students are taking, educational recommendations would be finding ways to incorporate more Constructive behaviors (i.e., reflection on their learning or making meaning from the material) into the course, as the class already has multiple Interactive tasks. Further research can also be done to investigate why students hold the views that they do, and whether this is an issue of perception or some other phenomenon.</p>

Good academic literacy skills are vital for success in the 21st century for students in higher education and for professional people in the workforce to be able to process and convey information and knowledge. The purpose of the current study was to gain insights into the construct of academic literacy skills and to identify indicators affecting the development of the academic literacy skills of first year students in higher education. To this end, a case study was done on a cohort of 20 first year male Emirati students attending an academic literacy skills course in an engineering higher education institute in the Arabian Gulf region. The study was guided by three research questions concerning the development of academic literacy skills which were defined as writing strategies, library research strategies and general study skills (Bury, Sheese & Katz, 2013). Data gathered comprised surveys, grade comparisons, written assignments, semi-structured interviews, classroom observations recorded using a video camera and instructor observations. The framework of Academic Literacies developed by Lea and Street (1998, 2000, 2006) was used for analysis with a focus on the supplementing constructs of study skills and academic socialization. To extract more detailed knowledge and further insights about the students’ academic literacy skills, a comparison was also made between the developmental indicators regarding successful and non-successful students’ written work and their approaches to completing assignments. The indicators revealed included the students’ lack of library research strategies, digital literacy skills and sense of ownership. Theoretical and practical implications for developing students’ academic literacy skills are provided in conclusion.

Academic advising has been a part of United States (U.S.) colleges and universities since their inception, yet academic advising as we know it today is a relatively new profession. Over the last several decades, many colleges and universities have employed professional advisors, rather than teaching and learning faculty, to carry out the academic advising functions however little is known about the structures of these advising programs. Academic advisors often serve on the front lines (i.e., high student contact hours) and advocate for student success by supporting students in learning about their institutions, uncovering their personal and professional goals, and encouraging them to pursue life goals. However, the responsibility of academic advising and advisors varies at institutions of higher education across the country and this variation is not well understood. The purpose of this research was to better understand the structures of engineering academic advising at large four-year, primarily residential institutions with a first-year engineering program. To accomplish this purpose, the following overarching research question guided my study: How do first-year engineering programs structure academic advising, and what services, programs, and support are in place for academic advisors and students? To answer this question, I used a qualitative multi-case study design to understand the landscape of advising in first-year engineering programs and the organizational structures of their advising programs. I used Habley's Organizational Models for Academic Advising (1983) as a way to categorize the structures of academic advising and Frank's (1993) Integrated Model of Academic Advising Program Development as a conceptual framework for understanding how academic advising programs develop, the services provided, programming available, and how to enable the advisors to better support the student population. My findings include identifying: 1) several similarities between case sites' organizational structures of advising, 2) new student orientation and major exploration as main services offered at all sites, 3) a lack of formalized planning across all case sites, and 4) the prominence of advisor training with a desire to have more formal advisor recognition programs. Recommendations for future research, practice, and policy are provided along with a proposal for a new model for First-Year Engineering Advising Programs. / Doctor of Philosophy / Academic advising is a function within higher education that serves students by providing guidance to navigate the higher education system. Academic advisors often serve on the front lines of the higher education environment and advocate for student success by supporting students in learning about their institutions of higher education, uncovering their personal and professional goals, and encouraging them in their academic pursuit. Academic advising has been a part of the United States (U.S.) higher education system at colleges and universities since their inception, yet academic advising, as we know it today is a relatively new profession. Over the last several decades, many colleges and universities have employed individuals to serve as professional academic advisors. These individuals spend the majority of their time and availability on the sole function of academic advising. However, the responsibility of academic advising and advisors varies at institutions of higher education across the country and this variation is not well understood. The purpose of this research was to gain a better understanding of the responsibilities and organizations of first-year engineering academic advising programs at large four-year, primarily residential institutions with a first-year engineering program. I interviewed individuals at universities and analyzed relevant advising program documents to understand the evolution of their advising programs, the services they provide, their program goals, and professional development available to them. My research uncovered 1) several similarities among the organization of the advising programs, 2) key academic services such the onboarding process for students known as new student orientation and methods to help student select an academic major, 3) a need to develop program planning initiatives and 4) the existence of training and lack of advising awards. Recommendations for future research, practice, and policy are provided along with a proposal for a new model for First-Year Engineering Advising Programs.

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