Multiple stakeholders are interested in measuring undergraduate student success in college across academic fields. Different metrics might appeal to different stakeholders. Some metrics such as the fraction of first-time, full-time students who start in the fall who graduate within six years, the graduation rate, are federally mandated by the U.S. Department of Education, Integrated Postsecondary Education Data System (IPEDS). We argue that this calculation of graduation rate is inherently problematic because it excludes up to 60% of students who transfer into an institution, enroll part-time, or enroll in terms other than the fall. By expanding the starters definition, we propose a graduation rate definition that includes conventionally excluded students and provides information on progression in a specific program. Stickiness is an even more-inclusive alternative, measuring a program's success in graduating all undergraduates ever enrolled in the program. In this work, programs are grouped into six academic fields: Arts and Humanities, Business, Engineering, Other, Social Sciences, and STM (Science, Technology, and Mathematics. Stickiness is the percentage of students who ever enroll in an academic field that graduate in the same field. We use the Multiple Institution Dataset for Investigating Engineering Longitudinal Development (MIDFIELD) 2023 which contains unit-record data for over 2 million individual students at 19 institutions. For the academic fields studied, Engineering has the highest graduation rate and third highest stickiness. Social Sciences and Business also have higher graduation rates and stickiness than the other fields. We also track the relative fraction of students migrating to and from each academic field. This paper continues our work to derive better metrics for understanding student success.

This research full paper explores the academic outcomes of undergraduate engineering students who leave their native countries and pursue their education in the USA. These “international students” are defined as students who were not citizens or permanent residents of the USA who enrolled in engineering programs in the USA. In this paper, we quantitatively analyze metrics for international and domestic students pursuing undergraduate degrees in one of the five most popular engineering disciplines in the USA: chemical, civil, electrical, industrial/systems, and mechanical using the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD). MIDFIELD includes institutional records from over 1.7 million undergraduate, degree-seeking students in 21 programs at nineteen universities in the USA with over 85,000 men and 21,000 women enrolled in one of the most popular five engineering disciplines in the USA. Metrics used include representation at the start of university studies, initial engineering major choice, six-year graduation rate, and stickiness (the number of students graduating in a major divided by the number of students who ever enrolled in that major). Results are disaggregated by origin (domestic or international), sex (female and male), and major (chemical, civil, electrical, industrial/systems, and mechanical). Results show that there are more men than women in these disciplines and this is more pronounced for international students. In these disciplines, international students graduate at higher rates and have higher stickiness than domestic students. International females have the highest graduation rates. Industrial/Systems Engineering has the highest graduation rates and stickiness for all populations. Insights from this work can inform student services personnel and others committed to international student success.

This study focuses on quantitative analyses of international and domestic students pursuing undergraduate degrees at institutions in the USA. Metrics used include representation at start of university studies, representation at graduation and six-year graduation rate. Results are disaggregated by origin (domestic or international), sex (female and male), and major (engineering or non-engineering). Results show that more international students choose engineering than other majors. There are more men than women in engineering and this is more pronounced for international students. International students graduate at higher rates in engineering than domestic students by about 5%. This may reflect a tension between their higher academic qualifications but challenges of adjusting to studying in another country. These insights can be used to support student success.

The Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) is a unique research resource offering student record data at an unprecedented scale. This special session aims to introduce participants to MIDFIELD including the data that it contains and some key results from research using MIDFIELD, explore how to conduct research with such a resource, and explain how participants can access MIDFIELD. Understanding what data is available in MIDFIELD and how to access it will help researchers decide if this is a useful resource for their own research.

This work-in-progress presents an overview of current research with the Multiple-Institution Database for Investigating Longitudinal Development (MIDFIELD) and strategies for inviting and supporting other researchers to use this database in their own research. MIDFIELD is a resource for the study of students that includes longitudinal, de-identified, whole population data for multiple institutions. This enables researchers to examine student characteristics (such as race/ethnicity, sex, socioeconomic indicators) and curricular pathways (including coursework) by institution and over time. Because the dataset contains records of all students matriculating over a period of time, researchers can study students across all disciplines, not just engineering. The MIDFIELD team aims to educate the broader research community, expand the network of researchers capable of conducting this research, and share innovative research methods in addition to the actual data. We have offered workshops at several conferences as well as the MIDFIELD Institute. The inaugural MIDFIELD Institute brought together researchers from across the USA for two days in 2019. In an evaluation at the end of the Institute, participants were pleased with the Institute and rated it highly. There was high agreement about the value of the content and delivery. Most participants reported that the instituted lived up to their expectations and was a good way to learn about how to use MIDFIELD data. Areas of improvement suggested by participants included making it longer and more challenging. Several of the MIDFIELD Institute attendees are presenting works-in-progress together at FIE 2020 to provide attendees with a range of examples of research that is possible with MIDFIELD.

This workshop, similar to our 2018 FIE workshop but revised to reflect participant feedback, introduces data and tools for investigating undergraduate persistence metrics using R. Student record data are from MIDFIELD, a database of registrars’ data from US institutions. The stratified data sample includes demographic, term, course, and degree information for 98,000 students from 1987 to 2016. The midfieldr package provides functions for determining persistence metrics such as graduation rates and for grouping findings by institution, program, sex, and race/ethnicity. The goal of the workshop is to share our data, methods, and metrics for intersectional research in student persistence. The workshop is designed for R beginners.

This workshop introduces data and tools for investigating undergraduate persistence metrics using R. Student record data are from MIDFIELD, a database of registrars' data from US institutions. The stratified data sample includes demographic, term, course, and degree information for 98,000 students from 1987 to 2016. The midfieldr package provides functions for determining persistence metrics such as graduation rates or program stickiness and for grouping findings by institution, program, sex, and race/ethnicity. The goal of the workshop is to share our data, methods, and metrics for intersectional research in student persistence. The workshop is designed for R beginners.

How successful are undergraduate students who begin in another major and migrate into engineering disciplines after matriculation? In this work in progress, we present quantitative data on outcomes for engineering migrators disaggregated by discipline, race/ethnicity, and sex. The study includes over 73,000 engineering students from nine U.S. universities, including first-time-in-college and transfer students who ever majored in the most common engineering disciplines: Chemical, Civil, Electrical, Industrial, and Mechanical Engineering. Adopting an ecosystem mindset, we have developed metrics including the graduation rate of migrators and “migration yield” to uncover dynamic information, not afforded by the conventional pipeline model, about the successes of students who migrate among the top five engineering disciplines. Our data show that the graduation rates of migrators are typically higher than those of starters for all engineering majors studied. Migration yield varies by race/ethnicity-sex as well as discipline. Migration yield for Chemical, Electrical, Industrial and Mechanical Engineering shows a sex-based effect, whereas Civil shows a race/ethnicity-based effect.

The Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) is expanding from 14 to about 100 institutions across the USA. This special session aims to introduce participants to MIDFIELD, explore how to conduct research with such a resource, and explain how participants can access MIDFIELD.

The Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) is expanding to include 113 institutions and is being redesigned and archived to be more accessible to researchers. This special session will describe how researchers can better use or gain access to MIDFIELD. At the conclusion of the session participants should be able to: describe MIDFIELD including common data elements, discuss how new variables can be derived from MIDFIELD, understand what is necessary to access the data on the Interuniversity Consortium for Political and Social Research, and define quantitative and qualitative data types and structures and outline research questions and methods of personal interest to them.

Conventional data displays such as pie charts, bar charts, and tables are generally ineffective at communicating the logic of an argument. Effective alternatives exist are less well-known. Reasons for the prevalence of pie charts, bar charts, and tables include: they are native to Office software; engineers and engineering educators generally lack training in visual rhetoric; new designs require additional work and technical skill of the author; and audiences resist change. This workshop addresses some of these barriers to change. The superior alternative to pie charts, bar charts, and (some) tables is the dot plot, a display type absent from Office but native to R, an open-source software environment originating in the statistics community. Workshop participants learn why dot plots are effective and how to create them using R. The workshop is designed for R beginners. The agenda includes active learning, demonstration, and discussion. Programming topics are developed using self-paced tutorials. Participants, from any discipline, interested in learning why and how to improve their graphical communication of quantitative data are welcome. Participants should bring wireless-capable laptops. After completing the workshop, participants should be able to describe the limitations of pie charts, bar charts, and tables, cite principles underlying more effective graphs, and use R to create dot plots. By developing participants' technical skills and rhetorical skills in this way, we serve the greater goal of improving their abilities to explore data and communicate findings, helping them use the logic of a display design to better support the logic of their argument.

Practitioners in engineering education, in studying and presenting their quantitative data, typically seek meaning - an inherently rhetorical activity. Data displays are an important part of this discourse. Visual conventions (pie charts, bar charts, and line charts, for example) can help or hinder the discovery of meaning in a data set. Our work concerns the visual rhetoric of this community: we assess current conventions and promote contemporary approaches to more effectively discover and communicate stories in the data. In this work in progress, we present three data displays from the Journal of Engineering Education representing commonly encountered, conventional designs. We assess the rhetorical merits and shortcomings of the displays, redesign them using principles and practices from the data visualization community, and discuss the results. We conclude that intentional design of data displays can help researchers explore their data, discover questions that might not have arisen otherwise, and convey compelling messages to their audiences.

We are conducting a longitudinal, multi-institutional, and multivariate study of the trajectories of students in specific engineering disciplines in the U.S.A. to an extent never before possible. Focusing on the eleven partner institutions of the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD), we examine trajectories of engineering students in five engineering disciplines by race/ethnicity and gender. This work-in-progress focuses on the largest fields: chemical (ChE), civil (CVE), electrical (EE), industrial (IE), and mechanical (ME) engineering. Our results show that all disciplines lose about half of their starters. However, when transfer students and others who switch into the majors are included, the results vary by race/ethnicity, gender, and engineering discipline. The metrics used are trajectories and stickiness in the major and in engineering. This work can inform educators, administrators, and policy makers about how engineering disciplines are racialized and gendered in different ways. Sharing this information can help engineering disciplines learn from each other.

Using longitudinal data from eleven institutions in the U.S., this study explores the persistence of students in the two largest engineering disciplines: Electrical (EE) and Mechanical (ME). These programs have large enrollments of students but small percentages of women. Despite these similarities, enrollment and persistence in these majors is qualitatively different. In this research, we adopt an intersectional framework and consider both race/ethnicity and gender. Our results show that ME attracts more White students while EE attracts more Black and Asian students. Hispanic men and women are attracted in similar numbers to EE and ME. Overall, ME has higher graduation rates than EE and women have higher rates than men in both disciplines. Transfer students of nearly all race/gender groups are more likely to persist to graduation than starters in the same disciplines. Black and Hispanic female transfer students are particularly successful in EE and ME, which suggests enhancing the transfer pathway as a strategy to improve diversity. The success of ME starters causes a shift in the demographic profile between starters and graduates. ME could learn from EE how to diversify its enrollment and EE could learn from ME strategies to retain its diverse students. These findings suggest that program factors affect each race-gender group differently. Therefore, the success of recruitment and retention strategies may depend on considering both the target population and the discipline.

The goal of this workshop is to introduce participants to tools that can help them manage teams in their classes effectively and efficiently. We review some of the factors that instructors may wish to consider when administering self- and peer-evaluations and how they might improve the quality of those evaluations using rater training. Attendees with wireless-network-capable laptop computers will interact with the system in real-time.

A new metric, “stickiness,” is proposed, tracking longitudinally all students who have contact with a discipline to determine the likelihood those students will “stick” to that discipline and graduate in it. This metric has the versatility to be relevant for students making contact with engineering through a variety of pathways. Stickiness exhibits significant disciplinary differentiation. Whereas earlier work has shown that Industrial Engineering is the most successful at attracting and retaining students, the disciplinary distribution of stickiness shows that Industrial Engineering is exceptional. Disaggregating by race/ethnicity and gender, much larger variations in stickiness are observed (as much as 48 percent), and positive and negative outcomes are identified where students in particular subpopulations are more or less likely to stick than expected. Aggregated by race/ethnicity and gender, the stickiness of transfer students ranks the disciplines in the same order as the stickiness of first-time-in-college students, but transfer stickiness exhibits less disciplinary variation and transfer students in all disciplines exhibit higher stickiness than first-time-in-college students.

Longitudinal data from ten U.S. institutions are used to characterize outcomes of three matriculation models: Direct Matriculation to a specific major (DM), First-Year Engineering programs (FYE), and Post-General Education Programs (PGE). Both DM and FYE programs show high persistence rates, but FYE programs are less likely to attract transfer students and switchers. FYE graduates are the most likely to stick with their first choice of major (after completing FYE requirements), followed by DM graduates who begin in undesignated engineering (taking extra time to decide), then DM graduates who choose their major as part of the matriculation process, and then PGE graduates. FYE students also have the shortest time to graduation. We conclude that encouraging students to associate with engineering or an engineering discipline from the start, yet maintaining the curricular flexibility to allow alternate entry points onto the engineering path improves persistence, accessibility, effectiveness of major choice, and time to graduation.