Abstract
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This work provides evidence of various approaches to studying longitudinal student unit record data in undergraduate education in the USA and the outcomes that can be realized using a large multi-institutional longitudinal dataset, Multiple-Institution Database for Investigating Longitudinal Development (MIDFIELD). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Cross-sectional studies introduce a variety of sources of error in estimating student pathways and outcomes. Longitudinal outcomes that ignore pathways also miss important information, and some populations are systematically excluded (such as transfer students). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Intended Outcomes:</i> By providing examples of how longitudinal student unit-record data can be analyzed and the results that can be expected, this work aims to deepen the research toolbox in engineering education. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> MIDFIELD is being used to support studies of demographic and financial trends among universities in the southeastern USA, required math and science course grades and disciplinary cultures, time to find graduation major, educational data mining, and applications of selected advanced models.
Highlights
W E ARE honored to be invited to be part of this Special Issue for the 50th Anniversary of the Frontiers in Education (FIE) conference
We present the history of MIDFIELD and an overview of the current dataset, including data structures, student data, and policies data
Our work identified the importance of disaggregating the transfer student population to consider transfers from two-year institutions and those from four-year institutions, showing that they differ on several key characteristics, including gender, fullversus part-time enrollment status, and education outcomes including six-year graduation in engineering [25], and that the transfer pathway is an important success story for Latinx students in engineering [26]
Summary
W E ARE honored to be invited to be part of this Special Issue for the 50th Anniversary of the Frontiers in Education (FIE) conference. This article presents an overview of the Multiple-Institution Database for Investigating Longitudinal Development (MIDFIELD), which celebrated its 25th anniversary in 2021. MIDFIELD is a resource for research on undergraduate students’ trajectories through their universities that includes longitudinal, de-identified whole population data for multiple institutions across the USA. The dataset is large enough to permit disaggregation by multiple characteristics, such as race/ethnicity, sex, and discipline simultaneously. This enables researchers to examine student characteristics (race/ethnicity, sex, and prior achievement) and curricular pathways (including coursework and major) by institution and over time. Intended Outcomes: By providing examples of how longitudinal student unit-record data can be analyzed and the results that can be expected, this work aims to deepen the research toolbox in engineering education
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