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MIDFIELD is a multi-institution, longitudinal, student-record–level dataset designed to study how engineering students progress through required curricula and what barriers affect progress to graduation. Designing, compiling, maintaining, protecting, and sharing such a large dataset yields practical lessons for other initiatives. 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OHLAND  \nAND  \nRUSSELL A. LONG Purdue University West Lafayette, IN  \nABSTRACT  \nSharing longitudinal student record data and merging data from different sources is critical to addressing important questions being asked of higher education. The Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) is a multi-institution, longitudinal, student record level dataset that is used to answer many research questions about how students maneuver through required engineering curriculum and what courses or policies stand in their way toward graduation. The process of designing, compiling, maintaining, protecting, and sharing a large dataset like MIDFIELD provides valuable insight for others.  \nKey words: Data sharing, MIDFIELD, student records.  \nINTRODUCTION  \nRetention, measured in various ways, has been the dominant mode of studying student success in engineering education and higher education in general. Data available from the Integrated Postsecondary Education Data System (IPEDS, 2015) and discipline-specific sources such as the American Society for Engineering Education (ASEE, 2015), the Engineering Workforce Commission (EWC, 2015), and the National Science Foundation (NSF) Science and Engineering Indicators (NSF SEI, 2015) do not facilitate longitudinal studies – nor were they designed to do so. Student-level longitudinal data allows calculation of six-year graduation rates and many other outcomes. Since such data are rarely available, various alternative measures have been used. Short-term measures such as one-year or two-year persistence fail to capture the important outcome of graduation,  \nSPRING 2016 1  \nADVANCES IN ENGINEERING EDUCATION  \nThe Multiple-Institution Database for Investigating Engineering Longitudinal Development: an Experiential Case Study of Data Sharing and Reuse  \nwhile cross-sectional approaches make the risky assumption that each cohort is the same as the next (Cosentino de Cohen & Deterding, 2009) and cannot account for migration among majors. The extensive use of data from the National Educational Longitudinal Study (NELS, 1988, 2002; USDOE NCES, 2000; Rowan, Chiang & Miller, 1997; Dávila & Mora, 2007), which tracks high school cohorts, demonstrates the value of longitudinal data in studying educational outcomes. NELS has been used to study varying topics: gender differences in mathematics achievement (Fan, Chen & Matsumoto, 1997); religious involvement, social capital, and adolescents’ academic progress (Muller & Ellison, 2001); adolescent cigarette smoking in U. S. racial/ethnic subgroups (Johnson & Hoffmann, 2000); and the social networks and resources of African–American eighth graders (Smith-Maddox, 1999) .  \nResearchers in the field of engineering education understood the need for and created a longitudinal student database that can be used to study how engineering students move through the curriculum and to create national benchmarks (Carson, 1997; Ohland & Anderson, 1999) . The Southeastern University and College Coalition for Engineering Education (SUCCEED) Longitudinal Database (LDB) was created in 1996. SUCCEED was one of eight coalitions developed by the National Science Foundation (NSF) through the Engineering Education Coalition (EEC) program. Out of the success of SUCCEED came the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD, 2015) .  \nMIDFIELD provides longitudinal data for 1,014,887 undergraduate students since fall 1987 – 210,725 of those students ever declared engineering as a major. MIDFIELD comprises whole population data of degree-seeking students at the 11 partner institutions—including students ","cbCaipEz3MNHye4v","https://ap.wps.com/l/cbCaipEz3MNHye4v","pdf",737322,25,"English","# Abstract\n# Introduction\n## Limits of existing student success measures\n## Prior longitudinal datasets and their value\n## MIDFIELD origins and partner institutions\n## Dataset scope and representativeness","[{\"question\":\"What problem does MIDFIELD address in engineering education research?\",\"answer\":\"MIDFIELD addresses the lack of student-level longitudinal data by enabling analysis of how students navigate engineering curricula and progress toward graduation.\"},{\"question\":\"How does the article justify using longitudinal student records instead of other measures?\",\"answer\":\"It argues that short-term persistence measures miss graduation outcomes, while cross-sectional approaches assume cohorts are similar and cannot handle major migration.\"},{\"question\":\"What scale and coverage does MIDFIELD provide?\",\"answer\":\"MIDFIELD covers 1,014,887 undergraduate students since fall 1987 across 11 partner institutions, including students from all disciplines and different enrollment statuses, with engineering major declarations tracked over time.\"}]","The Multiple-Institution Database for Investigating Engineering Longitudinal Development: an Experiential Case Study of Data Sharing and Reuse | PDF",63]