A model for an undergraduate research experience program in quantitative sciences Journal Article


Authors: Tan, K. S.; Elkin, E. B.; Satagopan, J. M.
Article Title: A model for an undergraduate research experience program in quantitative sciences
Abstract: We developed a summer research experience program within a freestanding comprehensive cancer center to cultivate undergraduate students with an interest in and an aptitude for quantitative sciences focused on oncology. This unconventional location for an undergraduate program is an ideal setting for interdisciplinary training in the intersection of oncology, statistics, and epidemiology. This article describes the development and implementation of a hands-on research experience program in this unique environment. Core components of the program include faculty-mentored projects, instructional programs to improve research skills and domain knowledge, and professional development activities. We discuss key considerations such as fostering effective partnership between research and administrative units, recruiting students, and identifying faculty mentors with quantitative projects. We describe evaluation approaches and discuss post-program outcomes and lessons learned. In its initial two years, the program successfully improved the students’ perception of competence gained in research skills and statistical knowledge across several knowledge domains. The majority of students also went on to pursue graduate degrees in a quantitative field or work in oncology-centric academic research roles. Our research-based training model can be adapted by a variety of organizations motivated to develop a summer research experience program in quantitative sciences for undergraduate students. Supplemental files for this article are available online. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.
Keywords: computational biology; experiential learning; mentoring; analysis of biomedical data; applied statistics internship; statistical training
Journal Title: Journal of Statistics and Data Science Education
Volume: 30
Issue: 1
ISSN: 2693-9169
Publisher: Taylor & Francis Group  
Date Published: 2022-02-22
Start Page: 65
End Page: 74
Language: English
DOI: 10.1080/26939169.2021.2016036
PROVIDER: scopus
PMCID: PMC9199014
PUBMED: 35722171
DOI/URL:
Notes: Article -- Export Date: 25 April 2022 -- Source: Scopus
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  1. Kay See   Tan
    241 Tan