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Significant Factors in Successfully Matching Students to Biomedical Engineering Research Laboratories

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2011 ASEE Annual Conference & Exposition


Vancouver, BC

Publication Date

June 26, 2011

Start Date

June 26, 2011

End Date

June 29, 2011



Conference Session

Experiential Learning and Globalization in BME

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Page Count


Page Numbers

22.1291.1 - 22.1291.13



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Paper Authors


Jonathan Sanghoon Lee University of Virginia

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Jonathan S. Lee is currently an undergraduate in Biomedical Engineering at the University of Virginia.

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Shing Wai Yam University of Virginia


William H. Guilford University of Virginia Orcid 16x16

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Will Guilford is an Associate Professor of Biomedical Engineering at the University of Virginia, and the current Undergraduate Program Director. He received his B.S. in Biology and Chemistry from St. Francis College in Ft. Wayne, Indiana and his Ph.D. in Physiology from the University of Arizona. Will did his postdoctoral training in Molecular Biophysics at the University of Vermont under David Warshaw. His research interests include the molecular mechanisms of cell movement and muscle contraction, and effective and efficient means for education.

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Significant factors in successfully matching students to biomedical engineering research laboratoriesWe see increasing demand from undergraduates for research experiences and a parallel increasein college and university interest in promoting research-based undergraduate education. Asundergraduate programs and institutions grow, it becomes difficult to successfully matchstudents to laboratories. Apart from identifying labs that are willing to accept undergraduates,successful matching requires an as yet unknown combination of interests, skills, implicit orexplicit mindsets, and demographic factors. In a single department these factors may be knownto a limited extent of both the labs and the undergraduate applicants. With enough priorexperience one might intuit a good student-lab combination. However, for large departments,diverse educational programs, or entire institutions, it is impossible to hand-match students tolabs because our knowledge of all possible combinations is limited. The objective of this studywas to determine whether student or laboratory characteristics, or a combination of both,appreciably predict the outcome of student-lab pairings. The ultimate goal is to generate analgorithm and online tool for self-service matching of students to labs on an institutional scale.Biomedical engineering students and faculty were surveyed regarding personal needs andpreferences, as well as the requirements and characteristics of the labs. These included basicrequirements, lab atmosphere, and skills and abilities. Basic requirements included the desiredacademic year of the student, grade point average requirement, desired majors and minors, thetime commitment, compensation to be offered (money, academic credit, or nothing), and whenthe research experience could occur (semester or summer). Atmosphere describes the labenvironment including lab size, whether there are lab meetings or social get-togethers, and labtype (basic research versus design, wet lab versus dry lab, and individual or team projects). Italso includes who does the undergraduate mentoring in a given lab (i.e. principal investigators,graduate students, lab managers, or post-doctoral associates). Third, we measured the knowledge,skills, abilities, and techniques (KSATs) a student possessed or desired at the beginning of a labexperience, and conversely that the laboratories used or required. KSATs were divided into twocategories – technical skills, and research areas (e.g. cancer biology). Finally, students wereasked (in a blind fashion) to indicate whether specific lab experiences were “successful,” wheresuccess could indicate either enjoying their experience, having their educational expectations met,or if they would return to the same laboratory in a subsequent term. Binary logistic regressionwas used to determine what student-laboratory congruencies best predicted success by thesethree metrics.A logistic equation with three congruencies – KSATs (p=0.074), preference for workingindividually or in a team (p =0.068), and working with live animals (p =0.070) – were found tocorrectly predict at least 74% of successful lab experiences (for enjoyment and meeting studentexpectations, p=0.04 and 0.02 respectively) and at least 63% of both successful and unsuccessfulexperiences. No strong predictors were identified for whether a student would return to a lab – acommon measure of success by faculty standards. An interesting finding was that neithercharacteristics of the labs alone, such as the number of students who worked in a given lab(“popularity”) nor characteristics of the students alone (e.g. GPA or academic year) weresignificant predictors of success by any metric. Additional research is needed, includingmeasures of students’ implicit mindsets, to identify measurable predictors of long-term student-lab pairings.

Lee, J. S., & Yam, S. W., & Guilford, W. H. (2011, June), Significant Factors in Successfully Matching Students to Biomedical Engineering Research Laboratories Paper presented at 2011 ASEE Annual Conference & Exposition, Vancouver, BC. 10.18260/1-2--18360

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