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Enhancing the Community College Pathway to Engineering Careers
background variables) on the successes and failures of students who begin their engineering educations in two-year and four-year programs presents serious problems for an analysis of the transfer function of community colleges. Most often, community colleges lose sight of students once they transfer to four-year institutions, precisely when they should begin tracking the educational and career trajectories of their students. Compiling and publicizing data on transfer students’ success in obtaining B.S. or advanced engineering degrees would demonstrate the effectiveness of engineering studies in community colleges nationally and improve their recruitment rates or point to the need to strengthen community college programs.
Less specific factors (e.g., economic, geographic, political, and social factors) also influence the effectiveness of attempts to improve the recruitment, retention, transfer, persistence to degrees (A.S., B.S., and advanced degrees), and the diversity of students on the community college pathway to engineering careers. Compiling and analyzing data on these factors, which are sometimes crucial to decisions, require more financial and personnel resources than smaller schools can afford.
Some useful information is being collected. For example, primary data are collected by the National Center for Education Statistics at the U.S. Department of Education, National Science Foundation, and American Association of Engineering Societies Engineering Workforce Commission, which collects data on student enrollments and degrees. All of these data are necessary for policy makers and institutions to facilitate and evaluate the transfer process. However, the comparability and periodicity of data have yet to be determined. In fact, we do not know how many community colleges offer engineering sciences programs! The key challenge is to collect information disaggregated by institution and student characteristics, that is, longitudinal information. The Department of Education’s data are noteworthy examples of useful longitudinal data.
Among the workshop participants who reported that their institutions do compile data on their students, four-year educational institutions were more likely than community colleges to undertake elaborate data collection and analyses and to gather quantitative as well as qualitative data.