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The persistence of engineering students through graduation continues to be a concern in higher education. Previous studies have highlighted a link between students' performance in introductory mathematics courses and graduation rates. Focusing on a crucial foundational course within the engineering curriculum, the purpose of this study is to investigate how students’ performance in Calculus I impact their persistence in the engineering program. Utilizing data from 22 diverse educational institutions using Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD), multilevel discrete-time survival analysis is employed to examine the longitudinal persistence patterns among the nested structure of students within institutions. Discrete-time analysis is an event-based approach that has the advantage of analyzing time in discrete chunks during which the event of interest could occur. The technique is a type of survival analysis, which has been used in other studies in engineering education and other educational studies. This approach addresses various challenges associated with analyzing student persistence data such as dealing with censored observations – observations for whom their entire educational pathway is not yet known because they are still enrolled. Using a multilevel form of this analysis approach also accounts for the hierarchical nature of the data involving students nested within institutions and incorporating variables that change over time. Thus, the study takes into account the variability and complexities inherent in the analysis of different institutions and examines persistence patterns more comprehensively than previous studies. By incorporating a diverse range of institutions, the study captures a broader spectrum of experiences and contexts, which enhances the generalizability of the results. 

The Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) has been developed over many years with substantial investment by the National Science Foundation through Engineering Education and Centers in the Engineering Directorate and the Division of Undergraduate Education in the Education and Human Resources Directorate. This project is focused on transitioning MIDFIELD to the American Society for Engineering Education (ASEE). The current team of MIDFIELD researchers continues to support this project including helping others learn to use the database. We have developed detailed tutorials in R that introduce MIDFIELD, key metrics, and example scenarios. We have also designed and facilitated workshops. In year 2, we offered the MIDFIELD Institute, an online three-day workshop to help researchers learn about and use MIDFIELD effectively. Attendees included graduate students, early career faculty, senior faculty, and an NSF program officer. Results from the 2023 offering of the MIDFIELD Institute are described in this paper. Dissemination and products are also summarized. 

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. 

Longitudinal, student-level data are a rich resource for characterizing how students navigate the terrain of higher education. Learning to work effectively with such data, however, can be a challenge. In this paper, we share some of our experiences over years of conducting research with the Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD). MIDFIELD contains individual student-level records for all undergraduate students at 19 US institutions with over 1.7 million unique students. This paper focuses on our lessons learned about processing longitudinal data to prepare it for analysis. We describe and define the steps that we take to process the data including filtering for data sufficiency, degree-seeking, and program (major), then classifying by completion status and demographics. We use the examples of calculation of graduation rate and stickiness to show the details of how the processed data is used in analysis. We hope this paper will help introduce the landscape of longitudinal research to a wider audience and provide tips for working with this valuable resource. 

A substantial investment by the National Science Foundation (NSF), including awards from Engineering Education and Centers in the Engineering Directorate and the Division of Undergraduate Education in the Education and Human Resources Directorate, has led to the creation and study of the Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD). This large database of student records has yielded groundbreaking research on student pathways by a small interdisciplinary team of researchers. The team has shown that while individual engineering programs may have poor graduation rates, a multi-institutional view reveals that engineering programs as a whole graduate a larger fraction of students than other groups of disciplines. The team has also shown that women and men have similar graduation rates in engineering, likely a result of efforts to make engineering education a welcoming environment for women and the high academic credentials of the women who do study engineering. As with the overall graduation rate, individual institutions and programs can and do have outcomes that depart from this aggregate perspective. A comprehensive study of student pathways in various engineering disciplines provided practitioners with rich information specific to their disciplinary context. The team has also designed a variety of metrics that have provided researchers and practitioners with an improved understanding of student pathways. The quality of the data source and the research team is attested by these substantial findings, multiple best paper awards, and other recognitions. This paper provides updates on transitioning MIDFIELD to the American Society for Engineering Education (ASEE), documentation of institutional policies, and supporting a growing community of researchers in using the database including the second offering of the MIDFIELD Institute. This work is supported by the NSF Division of Engineering Education and Centers.